mixOmics 6.37.0
multidrug study
library(BiocParallel)
If you are following our online course, the following vignette will be useful as a complementary learning tool. This vignette also covers the essential use cases of various methods in this package for the general mixOmcis user. The below methods will be covered:
As outlined in 1.3, this is not an exhaustive list of all the methods found within mixOmics. More information can be found at our website and you can ask questions via our discourse forum.
Figure 1: Different types of analyses with mixOmics (Rohart, Gautier, Singh, and Le Cao 2017).The biological questions, the number of data sets to integrate, and the type of response variable, whether qualitative (classification), quantitative (regression), one (PLS1) or several (PLS) responses, all drive the choice of analytical method
All methods featured in this diagram include variable selection except rCCA. In N-integration, rCCA and PLS enable the integration of two quantitative data sets, whilst the block PLS methods (that derive from the methods from Tenenhaus and Tenenhaus (2011)) can integrate more than two data sets. In P-integration, our method MINT is based on multi-group PLS (Eslami et al. 2014).The following activities cover some of these methods.
mixOmics is an R toolkit dedicated to the exploration and integration of biological data sets with a specific focus on variable selection. The package currently includes more than twenty multivariate methodologies, mostly developed by the mixOmics team (see some of our references in 1.2.3). Originally, all methods were designed for omics data, however, their application is not limited to biological data only. Other applications where integration is required can be considered, but mostly for the case where the predictor variables are continuous (see also 1.1).
In mixOmics, a strong focus is given to graphical representation to better translate and understand the relationships between the different data types and visualize the correlation structure at both sample and variable levels.
Note the data pre-processing requirements before analysing data with mixOmics:
Types of data. Different types of biological data can be explored and integrated with mixOmics. Our methods can handle molecular features measured on a continuous scale (e.g. microarray, mass spectrometry-based proteomics and metabolomics) or sequenced-based count data (RNA-seq, 16S, shotgun metagenomics) that become `continuous’ data after pre-processing and normalisation.
Normalisation. The package does not handle normalisation as it is platform-specific and we cover a too wide variety of data! Prior to the analysis, we assume the data sets have been normalised using appropriate normalisation methods and pre-processed when applicable.
Prefiltering. While mixOmics methods can handle large data sets (several tens of thousands of predictors), we recommend pre-filtering the data to less than 10K predictor variables per data set, for example by using Median Absolute Deviation (Teng et al. 2016) for RNA-seq data, by removing consistently low counts in microbiome data sets (Lê Cao et al. 2016) or by removing near-zero variance predictors. Such step aims to lessen the computational time during the parameter tuning process.
Data format. Our methods use matrix decomposition techniques. Therefore, the numeric data matrix or data frames have \(n\) observations or samples in rows and \(p\) predictors or variables (e.g. genes, proteins, OTUs) in columns.
Covariates. In the current version of mixOmics, covariates that may confound the analysis are not included in the methods. We recommend correcting for those covariates beforehand using appropriate univariate or multivariate methods for batch effect removal. Contact us for more details as we are currently working on this aspect.
We list here the main methodological or theoretical concepts you need to know to be able to efficiently apply mixOmics:
Individuals, observations or samples: the experimental units on which information are collected, e.g. patients, cell lines, cells, faecal samples etc.
Variables, predictors: read-out measured on each sample, e.g. gene (expression), protein or OTU (abundance), weight etc.
Variance: measures the spread of one variable. In our methods, we estimate the variance of components rather that variable read-outs. A high variance indicates that the data points are very spread out from the mean, and from one another (scattered).
Covariance: measures the strength of the relationship between two variables, i.e. whether they co-vary. A high covariance value indicates a strong relationship, e.g. weight and height in individuals frequently vary roughly in the same way; roughly, the heaviest are the tallest. A covariance value has no lower or upper bound.
Correlation: a standardized version of the covariance that is bounded by -1 and 1.
Linear combination: variables are combined by multiplying each of them by a coefficient and adding the results. A linear combination of height and weight could be \(2 * weight - 1.5 * height\) with the coefficients \(2\) and \(-1.5\) assigned with weight and height respectively.
Component: an artificial variable built from a linear combination of the observed variables in a given data set. Variable coefficients are optimally defined based on some statistical criterion. For example in Principal Component Analysis, the coefficients of a (principal) component are defined so as to maximise the variance of the component.
Loadings: variable coefficients used to define a component.
Sample plot: representation of the samples projected in a small space spanned (defined) by the components. Samples coordinates are determined by their components values or scores.
Correlation circle plot: representation of the variables in a space spanned by the components. Each variable coordinate is defined as the correlation between the original variable value and each component. A correlation circle plot enables to visualise the correlation between variables - negative or positive correlation, defined by the cosine angle between the centre of the circle and each variable point) and the contribution of each variable to each component - defined by the absolute value of the coordinate on each component. For this interpretation, data need to be centred and scaled (by default in most of our methods except PCA). For more details on this insightful graphic, see Figure 1 in (González et al. 2012).
Unsupervised analysis: the method does not take into account any known sample groups and the analysis is exploratory. Examples of unsupervised methods covered in this vignette are Principal Component Analysis (PCA, Chapter 3), Projection to Latent Structures (PLS, Chapter 4), and also Canonical Correlation Analysis (CCA, not covered here but see the website page).
Supervised analysis: the method includes a vector indicating the class membership of each sample. The aim is to discriminate sample groups and perform sample class prediction. Examples of supervised methods covered in this vignette are PLS Discriminant Analysis (PLS-DA, Chapter 5), DIABLO (Chapter 6) and also MINT (Chapter 7).
If the above descriptions were not comprehensive enough and you have some more questions, feel free to explore the glossary on our website.
Here is an overview of the most widely used methods in mixOmics that will be further detailed in this vignette, with the exception of rCCA. We depict them along with the type of data set they can handle.
FIGURE 1: An overview of what quantity and type of dataset each method within mixOmics requires. Thin columns represent a single variable, while the larger blocks represent datasets of multiple samples and variables.
Figure 2: List of methods in mixOmics, sparse indicates methods that perform variable selection
Figure 3: Main functions and parameters of each method
The methods implemented in mixOmics are described in detail in the following publications. A more extensive list can be found at this link.
Overview and recent integrative methods: Rohart F., Gautier, B, Singh, A, Le Cao, K. A. mixOmics: an R package for ’omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752.
Graphical outputs for integrative methods: (González et al. 2012) Gonzalez I., Le Cao K.-A., Davis, M.D. and Dejean S. (2012) Insightful graphical outputs to explore relationships between two omics data sets. BioData Mining 5:19.
DIABLO: Singh A, Gautier B, Shannon C, Vacher M, Rohart F, Tebbutt S, K-A. Le Cao. DIABLO - multi-omics data integration for biomarker discovery.
sparse PLS: Le Cao K.-A., Martin P.G.P, Robert-Granie C. and Besse, P. (2009) Sparse Canonical Methods for Biological Data Integration: application to a cross-platform study. BMC Bioinformatics, 10:34.
sparse PLS-DA: Le Cao K.-A., Boitard S. and Besse P. (2011) Sparse PLS Discriminant Analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics, 22:253.
Multilevel approach for repeated measurements: Liquet B, Le Cao K-A, Hocini H, Thiebaut R (2012). A novel approach for biomarker selection and the integration of repeated measures experiments from two assays. BMC Bioinformatics, 13:325
sPLS-DA for microbiome data: Le Cao K-A\(^*\), Costello ME \(^*\), Lakis VA , Bartolo F, Chua XY, Brazeilles R and Rondeau P. (2016) MixMC: Multivariate insights into Microbial Communities.PLoS ONE 11(8): e0160169
Each methods chapter has the following outline:
Other methods not covered in this document are described on our website and the following references:
regularised Canonical Correlation Analysis, see the Methods and Case study tabs, and (González et al. 2008) that describes CCA for large data sets.
Microbiome (16S, shotgun metagenomics) data analysis, see also (Lê Cao et al. 2016) and kernel integration for microbiome data. The latter is in collaboration with Drs J Mariette and Nathalie Villa-Vialaneix (INRA Toulouse, France), an example is provided for the Tara ocean metagenomics and environmental data.
First, download the latest version from Bioconductor:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("mixOmics")
Alternatively, you can install the latest GitHub version of the package:
BiocManager::install("mixOmicsTeam/mixOmics")
The mixOmics package should directly import the following packages:
igraph, rgl, ellipse, corpcor, RColorBrewer, plyr, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra.
For Apple mac users: if you are unable to install the imported package rgl, you will need to install the XQuartz software first.
library(mixOmics)
Check that there is no error when loading the package, especially for the rgl library (see above).
The examples we give in this vignette use data that are already part of the package. To upload your own data, check first that your working directory is set, then read your data from a .txt or .csv format, either by using File > Import Dataset in RStudio or via one of these command lines:
# from csv file
data <- read.csv("your_data.csv", row.names = 1, header = TRUE)
# from txt file
data <- read.table("your_data.txt", header = TRUE)
For more details about the arguments used to modify those functions, type ?read.csv or ?read.table in the R console.
mixOmicsEach analysis should follow this workflow:
Then use your critical thinking and additional functions and visual tools to make sense of your data! (some of which are listed in 1.2.2) and will be described in the next Chapters.
For instance, for Principal Components Analysis, we first load the data:
data(nutrimouse)
X <- nutrimouse$gene
Then use the following steps:
MyResult.pca <- pca(X) # 1 Run the method
plotIndiv(MyResult.pca) # 2 Plot the samples
plotVar(MyResult.pca) # 3 Plot the variables
This is only a first quick-start, there will be many avenues you can take to deepen your exploratory and integrative analyses. The package proposes several methods to perform variable, or feature selection to identify the relevant information from rather large omics data sets. The sparse methods are listed in the Table in 1.2.2.
Following our example here, sparse PCA can be applied to select the top 5 variables contributing to each of the two components in PCA. The user specifies the number of variables to selected on each component, for example, here 5 variables are selected on each of the first two components (keepX=c(5,5)):
MyResult.spca <- spca(X, keepX=c(5,5)) # 1 Run the method
plotIndiv(MyResult.spca) # 2 Plot the samples
plotVar(MyResult.spca) # 3 Plot the variables
You can see know that we have considerably reduced the number of genes in the plotVar correlation circle plot.
Do not stop here! We are not done yet. You can enhance your analyses with the following:
Have a look at our manual and each of the functions and their examples, e.g. ?pca, ?plotIndiv, ?sPCA, …
Run the examples from the help file using the example function: example(pca), example(plotIndiv), …
Have a look at our website that features many tutorials and case studies,
Keep reading this vignette, this is just the beginning!
multidrug studyTo illustrate PCA and is variants, we will analyse the multidrug case study available in the package. This pharmacogenomic study investigates the patterns of drug activity in cancer cell lines (Szakács et al. 2004). These cell lines come from the NCI-60 Human Tumor Cell Lines established by the Developmental Therapeutics Program of the National Cancer Institute (NCI) to screen for the toxicity of chemical compound repositories in diverse cancer cell lines. NCI-60 includes cell lines derived from cancers of colorectal (7 cell lines), renal (8), ovarian (6), breast (8), prostate (2), lung (9) and central nervous system origin (6), as well as leukemia (6) and melanoma (8).
Two separate data sets (representing two types of measurements) on the same NCI-60 cancer cell lines are available in multidrug (see also ?multidrug):
$ABC.trans: Contains the expression of 48 human ABC transporters measured by quantitative real-time PCR (RT-PCR) for each cell line.
$compound: Contains the activity of 1,429 drugs expressed as GI50, which is the drug concentration that induces 50% inhibition of cellular growth for the tested cell line.
Additional information will also be used in the outputs:
$comp.name: The names of the 1,429 compounds.
$cell.line: Information on the cell line names ($Sample) and the cell line types ($Class).
In this activity, we illustrate PCA performed on the human ABC transporters ABC.trans, and sparse PCA on the compound data compound.
The input data matrix \(\boldsymbol{X}\) is of size \(N\) samples in rows and \(P\) variables (e.g. genes) in columns. We start with the ABC.trans data.
library(mixOmics)
data(multidrug)
X <- multidrug$ABC.trans
dim(X) # Check dimensions of data
## [1] 60 48
Contrary to the minimal code example, here we choose to also scale the variables for the reasons detailed earlier. The function tune.pca() calculates the cumulative proportion of explained variance for a large number of principal components (here we set ncomp = 10). A screeplot of the proportion of explained variance relative to the total amount of variance in the data for each principal component is output (Fig. 4):
tune.pca.multi <- tune.pca(X, ncomp = 10, scale = TRUE)
plot(tune.pca.multi)
Figure 4: Screeplot from the PCA performed on the ABC.trans data: Amount of explained variance for each principal component on the ABC transporter data.
# tune.pca.multidrug$cum.var # Outputs cumulative proportion of variance
From the numerical output (not shown here), we observe that the first two principal components explain 22.87% of the total variance, and the first three principal components explain 29.88% of the total variance. The rule of thumb for choosing the number of components is not so much to set a hard threshold based on the cumulative proportion of explained variance (as this is data-dependent), but to observe when a drop, or elbow, appears on the screeplot. The elbow indicates that the remaining variance is spread over many principal components and is not relevant in obtaining a low dimensional ‘snapshot’ of the data. This is an empirical way of choosing the number of principal components to retain in the analysis. In this specific example we could choose between 2 to 3 components for the final PCA, however these criteria are highly subjective and the reader must keep in mind that visualisation becomes difficult above three dimensions.
Based on the preliminary analysis above, we run a PCA with three components. Here we show additional input, such as whether to center or scale the variables.
final.pca.multi <- pca(X, ncomp = 3, center = TRUE, scale = TRUE)
# final.pca.multi # Lists possible outputs
The output is similar to the tuning step above. Here the total variance in the data is:
final.pca.multi$var.tot
## [1] 47.98305
By summing the variance explained from all possible components, we would achieve the same amount of explained variance. The proportion of explained variance per component is:
final.pca.multi$prop_expl_var$X
## PC1 PC2 PC3
## 0.12677541 0.10194929 0.07011818
The cumulative proportion of variance explained can also be extracted (as displayed in Figure 4):
final.pca.multi$cum.var
## PC1 PC2 PC3
## 0.1267754 0.2287247 0.2988429
To calculate components, we use the variable coefficient weights indicated in the loading vectors. Therefore, the absolute value of the coefficients in the loading vectors inform us about the importance of each variable in contributing to the definition of each component. We can extract this information through the selectVar() function which ranks the most important variables in decreasing order according to their absolute loading weight value for each principal component.
# Top variables on the first component only:
head(selectVar(final.pca.multi, comp = 1)$value)
## value.var
## ABCE1 0.3242162
## ABCD3 0.2647565
## ABCF3 0.2613074
## ABCA8 -0.2609394
## ABCB7 0.2493680
## ABCF1 0.2424253
Note:
We project the samples into the space spanned by the principal components to visualise how the samples cluster and assess for biological or technical variation in the data. We colour the samples according to the cell line information available in multidrug$cell.line$Class by specifying the argument group (Fig. 5).
plotIndiv(final.pca.multi,
comp = c(1, 2), # Specify components to plot
ind.names = TRUE, # Show row names of samples
group = multidrug$cell.line$Class,
title = 'ABC transporters, PCA comp 1 - 2',
legend = TRUE, legend.title = 'Cell line')
Figure 5: Sample plot from the PCA performed on the ABC.trans data. Samples are projected into the space spanned by the first two principal components, and coloured according to cell line type. Numbers indicate the rownames of the data.
Because we have run PCA on three components, we can examine the third component, either by plotting the samples onto the principal components 1 and 3 (PC1 and PC3) in the code above (comp = c(1, 3)) or by using the 3D interactive plot (code shown below). The addition of the third principal component only seems to highlight a potential outlier (sample 8, not shown). Potentially, this sample could be removed from the analysis, or, noted when doing further downstream analysis. The removal of outliers should be exercised with great caution and backed up with several other types of analyses (e.g. clustering) or graphical outputs (e.g. boxplots, heatmaps, etc).
# Interactive 3D plot will load the rgl library.
plotIndiv(final.pca.multi, style = '3d',
group = multidrug$cell.line$Class,
title = 'ABC transporters, PCA comp 1 - 3')
These plots suggest that the largest source of variation explained by the first two components can be attributed to the melanoma cell line, while the third component highlights a single outlier sample. Hence, the interpretation of the following outputs should primarily focus on the first two components.
Note:
Correlation circle plots indicate the contribution of each variable to each component using the plotVar() function, as well as the correlation between variables (indicated by a ‘cluster’ of variables). Note that to interpret the latter, the variables need to be centered and scaled in PCA:
plotVar(final.pca.multi, comp = c(1, 2),
var.names = TRUE,
cex = 3, # To change the font size
# cutoff = 0.5, # For further cutoff
title = 'Multidrug transporter, PCA comp 1 - 2')
ABC.trans data. The plot shows groups of transporters that are highly correlated, and also contribute to PC1 - near the big circle on the right hand side of the plot (transporters grouped with those in orange), or PC1 and PC2 - top left and top bottom corner of the plot, transporters grouped with those in pink and yellow." width="75%" />
Figure 6: Correlation Circle plot from the PCA performed on the ABC.trans data. The plot shows groups of transporters that are highly correlated, and also contribute to PC1 - near the big circle on the right hand side of the plot (transporters grouped with those in orange), or PC1 and PC2 - top left and top bottom corner of the plot, transporters grouped with those in pink and yellow.
The plot in Figure 6 highlights a group of ABC transporters that contribute to PC1: ABCE1, and to some extent the group clustered with ABCB8 that contributes positively to PC1, while ABCA8 contributes negatively. We also observe a group of transporters that contribute to both PC1 and PC2: the group clustered with ABCC2 contributes negatively both to PC1 and PC2, and a cluster of ABCC12 and ABCD2 that contributes negatively to PC1 and positively to PC2. We observe that several transporters are inside the small circle. However, examining the third component (argument comp = c(1, 3)) does not appear to reveal further transporters that contribute to this third component. The additional argument cutoff = 0.5 could further simplify this plot.
A biplot allows us to display both samples and variables simultaneously to further understand their relationships. Samples are displayed as dots while variables are displayed at the tips of the arrows. Similar to correlation circle plots, data must be centered and scaled to interpret the correlation between variables (as a cosine angle between variable arrows).
biplot(final.pca.multi, group = multidrug$cell.line$Class,
legend.title = 'Cell line')
ABS.trans data. The plot highlights which transporter expression levels may be related to specific cell lines, such as melanoma." width="75%" />
Figure 7: Biplot from the PCA performed on the ABS.trans data. The plot highlights which transporter expression levels may be related to specific cell lines, such as melanoma.
The biplot in Figure 7 shows that the melanoma cell lines seem to be characterised by a subset of transporters such as the cluster around ABCC2 as highlighted previously in Figure 6. Further examination of the data, such as boxplots (as shown in Fig. 8), can further elucidate the transporter expression levels for these specific samples.
ABCC2.scale <- scale(X[, 'ABCC2'], center = TRUE, scale = TRUE)
boxplot(ABCC2.scale ~
multidrug$cell.line$Class, col = color.mixo(1:9),
xlab = 'Cell lines', ylab = 'Expression levels, scaled',
par(cex.axis = 0.5), # Font size
main = 'ABCC2 transporter')
Figure 8: Boxplots of the transporter ABCC2 identified from the PCA correlation circle plot (Fig. 6) and the biplot (Fig. 7) show the level of ABCC2 expression related to cell line types. The expression level of ABCC2 was centered and scaled in the PCA, but similar patterns are also observed in the original data.
In the ABC.trans data, there is only one missing value. Missing values can be handled by sPCA via the NIPALS algorithm . However, if the number of missing values is large, we recommend imputing them with NIPALS, as we describe in our website in www.mixOmics.org.
First, we must decide on the number of components to evaluate. The previous tuning step indicated that ncomp = 3 was sufficient to explain most of the variation in the data, which is the value we choose in this example. We then set up a grid of keepX values to test, which can be thin or coarse depending on the total number of variables. We set up the grid to be thin at the start, and coarse as the number of variables increases. The ABC.trans data includes a sufficient number of samples to perform repeated 5-fold cross-validation to define the number of folds and repeats (leave-one-out CV is also possible if the number of samples \(N\) is small by specifying folds = \(N\)). The computation may take a while if you are not using parallelisation (see additional parameters in tune.spca()), here we use a small number of repeats for illustrative purposes. We then plot the output of the tuning function.
grid.keepX <- c(seq(5, 30, 5))
# grid.keepX # To see the grid
set.seed(30) # For reproducibility with this handbook, remove otherwise
tune.spca.result <- tune.spca(X, ncomp = 3,
folds = 5,
test.keepX = grid.keepX, nrepeat = 10)
# Consider adding up to 50 repeats for more stable results
tune.spca.result$choice.keepX
## comp1 comp2 comp3
## 20 30 10
plot(tune.spca.result)
ABC.trans data. For a grid of number of variables to select indicated on the x-axis, the average correlation between predicted and actual components based on cross-validation is calculated and shown on the y-axis for each component. The optimal number of variables to select per component is assessed via one-sided \(t-\)tests and is indicated with a diamond." width="75%" />
Figure 9: Tuning the number of variables to select with sPCA on the ABC.trans data. For a grid of number of variables to select indicated on the x-axis, the average correlation between predicted and actual components based on cross-validation is calculated and shown on the y-axis for each component. The optimal number of variables to select per component is assessed via one-sided \(t-\)tests and is indicated with a diamond.
The tuning function outputs the averaged correlation between predicted and actual components per keepX value for each component. It indicates the optimal number of variables to select for which the averaged correlation is maximised on each component. Figure 9 shows that this is achieved when selecting 20 transporters on the first component, and 30 on the second. Given the drop in values in the averaged correlations for the third component, we decide to only retain two components.
Note:
Based on the tuning above, we perform the final sPCA where the number of variables to select on each component is specified with the argument keepX. Arbitrary values can also be input if you would like to skip the tuning step for more exploratory analyses:
# By default center = TRUE, scale = TRUE
keepX.select <- tune.spca.result$choice.keepX[1:2]
final.spca.multi <- spca(X, ncomp = 2, keepX = keepX.select)
# Proportion of explained variance:
final.spca.multi$prop_expl_var$X
## PC1 PC2
## 0.1187036 0.1042215
Overall when considering two components, we lose approximately 0.6 % of explained variance compared to a full PCA, but the aim of this analysis is to identify key transporters driving the variation in the data, as we show below.
We first examine the sPCA sample plot:
plotIndiv(final.spca.multi,
comp = c(1, 2), # Specify components to plot
ind.names = TRUE, # Show row names of samples
group = multidrug$cell.line$Class,
title = 'ABC transporters, sPCA comp 1 - 2',
legend = TRUE, legend.title = 'Cell line')
Figure 10: Sample plot from the sPCA performed on the ABC.trans data. Samples are projected onto the space spanned by the first two sparse principal components that are calculated based on a subset of selected variables. Samples are coloured by cell line type and numbers indicate the sample IDs.
In Figure 10, component 2 in sPCA shows clearer separation of the melanoma samples compared to the full PCA. Component 1 is similar to the full PCA. Overall, this sample plot shows that little information is lost compared to a full PCA.
A biplot can also be plotted that only shows the selected transporters (Figure 11):
biplot(final.spca.multi, group = multidrug$cell.line$Class,
legend =FALSE)
Figure 11: Biplot from the sPCA performed on the ABS.trans data after variable selection. The plot highlights in more detail which transporter expression levels may be related to specific cell lines, such as melanoma, compared to a classical PCA.
The correlation circle plot highlights variables that contribute to component 1 and component 2 (Fig. 12):
plotVar(final.spca.multi, comp = c(1, 2), var.names = TRUE,
cex = 3, # To change the font size
title = 'Multidrug transporter, sPCA comp 1 - 2')
ABC.trans data. Only the transporters selected by the sPCA are shown on this plot. Transporters coloured in green are discussed in the text." width="75%" />
Figure 12: Correlation Circle plot from the sPCA performed on the ABC.trans data. Only the transporters selected by the sPCA are shown on this plot. Transporters coloured in green are discussed in the text.
The transporters selected by sPCA are amongst the most important ones in PCA. Those coloured in green in Figure 6 (ABCA9, ABCB5, ABCC2 and ABCD1) show an example of variables that contribute positively to component 2, but with a larger weight than in PCA. Thus, they appear as a clearer cluster in the top part of the correlation circle plot compared to PCA. As shown in the biplot in Figure 11, they contribute in explaining the variation in the melanoma samples.
We can extract the variable names and their positive or negative contribution to a given component (here 2), using the selectVar() function:
# On the first component, just a head
head(selectVar(final.spca.multi, comp = 2)$value)
## value.var
## ABCA9 0.3879488
## ABCB5 0.3797691
## ABCC2 0.3691762
## ABCD1 0.3517099
## ABCA5 0.2521508
## ABCA3 -0.2499891
The loading weights can also be visualised with plotLoading(), where variables are ranked from the least important (top) to the most important (bottom) in Figure 13). Here on component 2:
plotLoadings(final.spca.multi, comp = 2)
Figure 13: sPCA loading plot of the ABS.trans data for component 2. Only the transporters selected by sPCA on component 2 are shown, and are ranked from least important (top) to most important. Bar length indicates the loading weight in PC2.
The data come from a liver toxicity study in which 64 male rats were exposed to non-toxic (50 or 150 mg/kg), moderately toxic (1500 mg/kg) or severely toxic (2000 mg/kg) doses of acetaminophen (paracetamol) (Bushel et al. 2007). Necropsy was performed at 6, 18, 24 and 48 hours after exposure and the mRNA was extracted from the liver. Ten clinical measurements of markers for liver injury are available for each subject. The microarray data contain expression levels of 3,116 genes. The data were normalised and preprocessed by Bushel et al. (2007).
liver toxicity contains the following:
$gene: A data frame with 64 rows (rats) and 3116 columns (gene expression levels),$clinic: A data frame with 64 rows (same rats) and 10 columns (10 clinical variables),$treatment: A data frame with 64 rows and 4 columns, describe the different treatments, such as doses of acetaminophen and times of necropsy.We can analyse these two data sets (genes and clinical measurements) using sPLS1, then sPLS2 with a regression mode to explain or predict the clinical variables with respect to the gene expression levels.
library(mixOmics)
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
As we have discussed previously for integrative analysis, we need to ensure that the samples in the two data sets are in the same order, or matching, as we are performing data integration:
head(data.frame(rownames(X), rownames(Y)))
## rownames.X. rownames.Y.
## 1 ID202 ID202
## 2 ID203 ID203
## 3 ID204 ID204
## 4 ID206 ID206
## 5 ID208 ID208
## 6 ID209 ID209
We first start with a simple case scenario where we wish to explain one \(\boldsymbol Y\) variable with a combination of selected \(\boldsymbol X\) variables (transcripts). We choose the following clinical measurement which we denote as the \(\boldsymbol y\) single response variable:
y <- liver.toxicity$clinic[, "ALB.g.dL."]
Defining the ‘best’ number of dimensions to explain the data requires we first launch a PLS1 model with a large number of components. Some of the outputs from the PLS1 object are then retrieved in the perf() function to calculate the \(Q^2\) criterion using repeated 10-fold cross-validation.
tune.pls1.liver <- pls(X = X, Y = y, ncomp = 4, mode = 'regression')
set.seed(33) # For reproducibility with this handbook, remove otherwise
Q2.pls1.liver <- perf(tune.pls1.liver, validation = 'Mfold',
folds = 10, nrepeat = 5)
plot(Q2.pls1.liver, criterion = 'Q2')
Figure 14: \(Q^2\) criterion to choose the number of components in PLS1. For each dimension added to the PLS model, the \(Q^2\) value is shown. The horizontal line of 0.0975 indicates the threshold below which adding a dimension may not be beneficial to improve accuracy in PLS.
The plot in Figure 14 shows that the \(Q^2\) value varies with the number of dimensions added to PLS1, with a decrease to negative values from 2 dimensions. Based on this plot we would choose only one dimension, but we will still add a second dimension for the graphical outputs.
Note:
We now set a grid of values - thin at the start, but also restricted to a small number of genes for a parsimonious model, which we will test for each of the two components in the tune.spls() function, using the MAE criterion.
# Set up a grid of values:
list.keepX <- c(5:10, seq(15, 50, 5))
# list.keepX # Inspect the keepX grid
set.seed(33) # For reproducibility with this handbook, remove otherwise
tune.spls1.MAE <- tune.spls(X, y, ncomp= 2,
test.keepX = list.keepX,
validation = 'Mfold',
folds = 10,
nrepeat = 5,
progressBar = FALSE,
measure = 'MAE')
plot(tune.spls1.MAE)
keepX is indicated with a diamond." 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" alt="Mean Absolute Error criterion to choose the number of variables to select in PLS1, using repeated CV times for a grid of variables to select. The MAE increases with the addition of a second dimension comp 1 to 2, suggesting that only one dimension is sufficient. The optimal keepX is indicated with a diamond." width="75%" />
Figure 15: Mean Absolute Error criterion to choose the number of variables to select in PLS1, using repeated CV times for a grid of variables to select. The MAE increases with the addition of a second dimension comp 1 to 2, suggesting that only one dimension is sufficient. The optimal keepX is indicated with a diamond.
Figure 15 confirms that one dimension is sufficient to reach minimal MAE. Based on the tune.spls() function we extract the final parameters:
choice.ncomp <- tune.spls1.MAE$choice.ncomp$ncomp
# Optimal number of variables to select in X based on the MAE criterion
# We stop at choice.ncomp
choice.keepX <- tune.spls1.MAE$choice.keepX[1:choice.ncomp]
choice.ncomp
## [1] 1
choice.keepX
## comp1
## 20
Note:
measure = 'MSE, the optimal keepX was rather unstable, and is often smaller than when using the MAE criterion. As we have highlighted before, there is some back and forth in the analyses to choose the criterion and parameters that best fit our biological question and interpretation.Here is our final model with the tuned parameters:
spls1.liver <- spls(X, y, ncomp = choice.ncomp, keepX = choice.keepX,
mode = "regression")
The list of genes selected on component 1 can be extracted with the command line (not output here):
selectVar(spls1.liver, comp = 1)$X$name
We can compare the amount of explained variance for the \(\boldsymbol X\) data set based on the sPLS1 (on 1 component) versus PLS1 (that was run on 4 components during the tuning step):
spls1.liver$prop_expl_var$X
## comp1
## 0.08150917
tune.pls1.liver$prop_expl_var$X
## comp1 comp2 comp3 comp4
## 0.11079101 0.14010577 0.21714518 0.06433377
The amount of explained variance in \(\boldsymbol X\) is lower in sPLS1 than PLS1 for the first component. However, we will see in this case study that the Mean Squared Error Prediction is also lower (better) in sPLS1 compared to PLS1.
For further graphical outputs, we need to add a second dimension in the model, which can include the same number of keepX variables as in the first dimension. However, the interpretation should primarily focus on the first dimension. In Figure 16 we colour the samples according to the time of treatment and add symbols to represent the treatment dose. Recall however that such information was not included in the sPLS1 analysis.
spls1.liver.c2 <- spls(X, y, ncomp = 2, keepX = c(rep(choice.keepX, 2)),
mode = "regression")
plotIndiv(spls1.liver.c2,
group = liver.toxicity$treatment$Time.Group,
pch = as.factor(liver.toxicity$treatment$Dose.Group),
legend = TRUE, legend.title = 'Time', legend.title.pch = 'Dose')
liver.toxicity data with two dimensions. Components associated to each data set (or block) are shown. Focusing only on the projection of the sample on the first component shows that the genes selected in \(\boldsymbol X\) tend to explain the 48h length of treatment vs the earlier time points. This is somewhat in agreement with the levels of the \(\boldsymbol y\) variable. However, more insight can be obtained by plotting the first components only, as shown in Figure 17." width="75%" />
Figure 16: Sample plot from the PLS1 performed on the liver.toxicity data with two dimensions. Components associated to each data set (or block) are shown. Focusing only on the projection of the sample on the first component shows that the genes selected in \(\boldsymbol X\) tend to explain the 48h length of treatment vs the earlier time points. This is somewhat in agreement with the levels of the \(\boldsymbol y\) variable. However, more insight can be obtained by plotting the first components only, as shown in Figure 17.
The alternative is to plot the component associated to the \(\boldsymbol X\) data set (here corresponding to a linear combination of the selected genes) vs. the component associated to the \(\boldsymbol y\) variable (corresponding to the scaled \(\boldsymbol y\) variable in PLS1 with one dimension), or calculate the correlation between both components:
# Define factors for colours matching plotIndiv above
time.liver <- factor(liver.toxicity$treatment$Time.Group,
levels = c('18', '24', '48', '6'))
dose.liver <- factor(liver.toxicity$treatment$Dose.Group,
levels = c('50', '150', '1500', '2000'))
# Set up colours and symbols
col.liver <- color.mixo(time.liver)
pch.liver <- as.numeric(dose.liver)
plot(spls1.liver$variates$X, spls1.liver$variates$Y,
xlab = 'X component', ylab = 'y component / scaled y',
col = col.liver, pch = pch.liver)
legend('topleft', col = color.mixo(1:4), legend = levels(time.liver),
lty = 1, title = 'Time')
legend('bottomright', legend = levels(dose.liver), pch = 1:4,
title = 'Dose')
liver.toxicity data on one dimension. A reduced representation of the 20 genes selected and combined in the \(\boldsymbol X\) component on the \(x-\)axis with respect to the \(\boldsymbol y\) component value (equivalent to the scaled values of \(\boldsymbol y\)) on the \(y-\)axis. We observe a separation between the high doses 1500 and 2000 mg/kg (symbols \(+\) and \(\times\)) at 48h and 18h while low and medium doses cluster in the middle of the plot. High doses for 6h and 18h have high scores for both components." width="75%" />
Figure 17: Sample plot from the sPLS1 performed on the liver.toxicity data on one dimension. A reduced representation of the 20 genes selected and combined in the \(\boldsymbol X\) component on the \(x-\)axis with respect to the \(\boldsymbol y\) component value (equivalent to the scaled values of \(\boldsymbol y\)) on the \(y-\)axis. We observe a separation between the high doses 1500 and 2000 mg/kg (symbols \(+\) and \(\times\)) at 48h and 18h while low and medium doses cluster in the middle of the plot. High doses for 6h and 18h have high scores for both components.
cor(spls1.liver$variates$X, spls1.liver$variates$Y)
## comp1
## comp1 0.7515489
Figure 17 is a reduced representation of a multivariate regression with PLS1. It shows that PLS1 effectively models a linear relationship between \(\boldsymbol y\) and the combination of the 20 genes selected in \(\boldsymbol X\).
The performance of the final model can be assessed with the perf() function, using repeated cross-validation (CV). Because a single performance value has little meaning, we propose to compare the performances of a full PLS1 model (with no variable selection) with our sPLS1 model based on the MSEP (other criteria can be used):
set.seed(33) # For reproducibility with this handbook, remove otherwise
# PLS1 model and performance
pls1.liver <- pls(X, y, ncomp = choice.ncomp, mode = "regression")
perf.pls1.liver <- perf(pls1.liver, validation = "Mfold", folds =10,
nrepeat = 5, progressBar = FALSE)
perf.pls1.liver$measures$MSEP$summary
## feature comp mean sd
## 1 Y 1 0.7761829 0.05963869
# To extract values across all repeats:
# perf.pls1.liver$measures$MSEP$values
# sPLS1 performance
perf.spls1.liver <- perf(spls1.liver, validation = "Mfold", folds = 10,
nrepeat = 5, progressBar = FALSE)
perf.spls1.liver$measures$MSEP$summary
## feature comp mean sd
## 1 Y 1 0.5677406 0.02175215
The MSEP is lower with sPLS1 compared to PLS1, indicating that the \(\boldsymbol{X}\) variables selected (listed above with selectVar()) can be considered as a good linear combination of predictors to explain \(\boldsymbol y\).
PLS2 is a more complex problem than PLS1, as we are attempting to fit a linear combination of a subset of \(\boldsymbol{Y}\) variables that are maximally covariant with a combination of \(\boldsymbol{X}\) variables. The sparse variant allows for the selection of variables from both data sets.
As a reminder, here are the dimensions of the \(\boldsymbol{Y}\) matrix that includes clinical parameters associated with liver failure.
dim(Y)
## [1] 64 10
Similar to PLS1, we first start by tuning the number of components to select by using the perf() function and the \(Q^2\) criterion using repeated cross-validation.
tune.pls2.liver <- pls(X = X, Y = Y, ncomp = 5, mode = 'regression')
set.seed(33) # For reproducibility with this handbook, remove otherwise
Q2.pls2.liver <- perf(tune.pls2.liver, validation = 'Mfold', folds = 10,
nrepeat = 5)
plot(Q2.pls2.liver, criterion = 'Q2.total')
Figure 18: \(Q^2\) criterion to choose the number of components in PLS2. For each component added to the PLS2 model, the averaged \(Q^2\) across repeated cross-validation is shown, with the horizontal line of 0.0975 indicating the threshold below which the addition of a dimension may not be beneficial to improve accuracy.
Figure 18 shows that one dimension should be sufficient in PLS2. We will include a second dimension in the graphical outputs, whilst focusing our interpretation on the first dimension.
Note:
nrepeat = 1.Using the tune.spls() function, we can perform repeated cross-validation to obtain some indication of the number of variables to select. We show an example of code below which may take some time to run (see ?tune.spls() to use parallel computing). We had refined the grid of tested values as the tuning function tended to favour a very small signature. Hence we decided to constrain the start of the grid to 3 for a more insightful signature. Both measure = 'cor and RSS gave similar signature sizes, but this observation might differ for other case studies.
The optimal parameters can be output, along with a plot showing the tuning results, as shown in Figure 19:
# This code may take several min to run, parallelisation option is possible
list.keepX <- c(seq(5, 50, 5))
list.keepY <- c(3:10)
set.seed(33) # For reproducibility with this handbook, remove otherwise
tune.spls.liver <- tune.spls(X, Y, test.keepX = list.keepX,
test.keepY = list.keepY, ncomp = 2,
nrepeat = 1, folds = 10, mode = 'regression',
measure = 'cor',
# the following uses two CPUs for faster computation
# it can be commented out
BPPARAM = BiocParallel::SnowParam(workers = 2)
)
plot(tune.spls.liver)
keepX and keepY, the averaged correlation coefficients between the \(\boldsymbol t\) and \(\boldsymbol u\) components are shown across repeated CV, with optimal values (here corresponding to the highest mean correlation) indicated in a green square for each dimension and data set." width="60%" />
Figure 19: Tuning plot for sPLS2. For every grid value of keepX and keepY, the averaged correlation coefficients between the \(\boldsymbol t\) and \(\boldsymbol u\) components are shown across repeated CV, with optimal values (here corresponding to the highest mean correlation) indicated in a green square for each dimension and data set.
Here is our final model with the tuned parameters for our sPLS2 regression analysis. Note that if you choose to not run the tuning step, you can still decide to set the parameters of your choice here.
#Optimal parameters
choice.keepX <- tune.spls.liver$choice.keepX
choice.keepY <- tune.spls.liver$choice.keepY
choice.ncomp <- length(choice.keepX)
spls2.liver <- spls(X, Y, ncomp = choice.ncomp,
keepX = choice.keepX,
keepY = choice.keepY,
mode = "regression")
The amount of explained variance can be extracted for each dimension and each data set:
spls2.liver$prop_expl_var
## $X
## comp1 comp2
## 0.1967201 0.0855541
##
## $Y
## comp1 comp2
## 0.3650005 0.2174947
The selected variables can be extracted from the selectVar() function, for example for the \(\boldsymbol X\) data set, with either their $name or the loading $value (not output here):
selectVar(spls2.liver, comp = 1)$X$value
The VIP measure is exported for all variables in \(\boldsymbol X\), here we only subset those that were selected (non null loading value) for component 1:
vip.spls2.liver <- vip(spls2.liver)
# just a head
head(vip.spls2.liver[selectVar(spls2.liver, comp = 1)$X$name,1])
## A_42_P620915 A_43_P14131 A_42_P578246 A_43_P11724 A_42_P840776 A_42_P675890
## 22.40370 20.66500 15.29651 14.68876 13.81364 13.12778
The (full) output shows that most \(\boldsymbol X\) variables that were selected are important for explaining \(\boldsymbol Y\), since their VIP is greater than 1.
We can examine how frequently each variable is selected when we subsample the data using the perf() function to measure how stable the signature is (Table 1). The same could be output for other components and the \(\boldsymbol Y\) data set.
perf.spls2.liver <- perf(spls2.liver, validation = 'Mfold', folds = 10, nrepeat = 5)
# Extract stability
stab.spls2.liver.comp1 <- perf.spls2.liver$features$stability.X$comp1
# Averaged stability of the X selected features across CV runs, as shown in Table
stab.spls2.liver.comp1[1:choice.keepX[1]]
# We extract the stability measures of only the variables selected in spls2.liver
extr.stab.spls2.liver.comp1 <- stab.spls2.liver.comp1[selectVar(spls2.liver,
comp =1)$X$name]
| x |
|---|
| 0.84 |
| 0.94 |
| 0.74 |
| 0.76 |
| 0.82 |
| 0.82 |
| 0.70 |
| 0.66 |
| 0.52 |
| 0.40 |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
We recommend to mainly focus on the interpretation of the most stable selected variables (with a frequency of occurrence greater than 0.8).
Using the plotIndiv() function, we display the sample and metadata information using the arguments group (colour) and pch (symbol) to better understand the similarities between samples modelled with sPLS2.
The plot on the left hand side corresponds to the projection of the samples from the \(\boldsymbol X\) data set (gene expression) and the plot on the right hand side the \(\boldsymbol Y\) data set (clinical variables).
plotIndiv(spls2.liver, ind.names = FALSE,
group = liver.toxicity$treatment$Time.Group,
pch = as.factor(liver.toxicity$treatment$Dose.Group),
col.per.group = color.mixo(1:4),
legend = TRUE, legend.title = 'Time',
legend.title.pch = 'Dose')
liver.toxicity data. Samples are projected into the space spanned by the components associated to each data set (or block). We observe some agreement between the data sets, and a separation of the 1500 and 2000 mg doses (\(+\) and \(\times\)) in the 18h, 24h time points, and the 48h time point." width="75%" />
Figure 20: Sample plot for sPLS2 performed on the liver.toxicity data. Samples are projected into the space spanned by the components associated to each data set (or block). We observe some agreement between the data sets, and a separation of the 1500 and 2000 mg doses (\(+\) and \(\times\)) in the 18h, 24h time points, and the 48h time point.
From Figure 20 we observe an effect of low vs. high doses of acetaminophen (component 1) as well as time of necropsy (component 2). There is some level of agreement between the two data sets, but it is not perfect!
If you run an sPLS with three dimensions, you can consider the 3D plotIndiv() by specifying style = '3d in the function.
The plotArrow() option is useful in this context to visualise the level of agreement between data sets. Recall that in this plot:
plotArrow(spls2.liver, ind.names = FALSE,
group = liver.toxicity$treatment$Time.Group,
col.per.group = color.mixo(1:4),
legend.title = 'Time.Group')
liver.toxicity data. The start of the arrow indicates the location of a given sample in the space spanned by the components associated to the gene data set, and the tip of the arrow the location of that same sample in the space spanned by the components associated to the clinical data set. We observe large shifts for 18h, 24 and 48h samples for the high doses, however the clusters of samples remain the same, as we observed in Figure 20." width="75%" />
Figure 21: Arrow plot from the sPLS2 performed on the liver.toxicity data. The start of the arrow indicates the location of a given sample in the space spanned by the components associated to the gene data set, and the tip of the arrow the location of that same sample in the space spanned by the components associated to the clinical data set. We observe large shifts for 18h, 24 and 48h samples for the high doses, however the clusters of samples remain the same, as we observed in Figure 20.
In Figure 21 we observe that specific groups of samples seem to be located far apart from one data set to the other, indicating a potential discrepancy between the information extracted. However the groups of samples according to either dose or treatment remains similar.
Correlation circle plots illustrate the correlation structure between the two types of variables. To display only the name of the variables from the \(\boldsymbol{Y}\) data set, we use the argument var.names = c(FALSE, TRUE) where each boolean indicates whether the variable names should be output for each data set. We also modify the size of the font, as shown in Figure 22:
plotVar(spls2.liver, cex = c(3,4), var.names = c(FALSE, TRUE))
liver.toxicity data. The plot highlights correlations within selected genes (their names are not indicated here), within selected clinical parameters, and correlations between genes and clinical parameters on each dimension of sPLS2. This plot should be interpreted in relation to Figure 20 to better understand how the expression levels of these molecules may characterise specific sample groups." width="75%" />
Figure 22: Correlation circle plot from the sPLS2 performed on the liver.toxicity data. The plot highlights correlations within selected genes (their names are not indicated here), within selected clinical parameters, and correlations between genes and clinical parameters on each dimension of sPLS2. This plot should be interpreted in relation to Figure 20 to better understand how the expression levels of these molecules may characterise specific sample groups.
To display variable names that are different from the original data matrix (e.g. gene ID), we set the argument var.names as a list for each type of label, with geneBank ID for the \(\boldsymbol X\) data set, and TRUE for the \(\boldsymbol Y\) data set:
plotVar(spls2.liver,
var.names = list(X.label = liver.toxicity$gene.ID[,'geneBank'],
Y.label = TRUE), cex = c(3,4))
$gene.ID (Note: some gene names are missing)." 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alt="Correlation circle plot from the sPLS2 performed on the liver.toxicity data. A variant of Figure 22 with gene names that are available in $gene.ID (Note: some gene names are missing)." width="75%" />
Figure 23: Correlation circle plot from the sPLS2 performed on the liver.toxicity data. A variant of Figure 22 with gene names that are available in $gene.ID (Note: some gene names are missing).
The correlation circle plots highlight the contributing variables that, together, explain the covariance between the two data sets. In addition, specific subsets of molecules can be further investigated, and in relation with the sample group they may characterise. The latter can be examined with additional plots (for example boxplots with respect to known sample groups and expression levels of specific variables, as we showed in the PCA case study previously. The next step would be to examine the validity of the biological relationship between the clusters of genes with some of the clinical variables that we observe in this plot.
A 3D plot is also available in plotVar() with the argument style = '3d. It requires an sPLS2 model with at least three dimensions.
Other plots are available to complement the information from the correlation circle plots, such as Relevance networks and Clustered Image Maps (CIMs), as described in Module 2.
The network in sPLS2 displays only the variables selected by sPLS, with an additional cutoff similarity value argument (absolute value between 0 and 1) to improve interpretation. Because Rstudio sometimes struggles with the margin size of this plot, we can either launch X11() prior to plotting the network, or use the arguments save and name.save as shown below:
# Define red and green colours for the edges
color.edge <- color.GreenRed(50)
# X11() # To open a new window for Rstudio
network(spls2.liver, comp = 1:2,
cutoff = 0.7,
shape.node = c("rectangle", "circle"),
color.node = c("cyan", "pink"),
color.edge = color.edge,
# To save the plot, unotherwise:
# save = 'pdf', name.save = 'network_liver'
)
cutoff argument (optional)." 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" alt="Network representation from the sPLS2 performed on the liver.toxicity data. The networks are bipartite, where each edge links a gene (rectangle) to a clinical variable (circle) node, according to a similarity matrix described in Module 2. Only variables selected by sPLS2 on the two dimensions are represented and are further filtered here according to a cutoff argument (optional)." width="75%" />
Figure 24: Network representation from the sPLS2 performed on the liver.toxicity data. The networks are bipartite, where each edge links a gene (rectangle) to a clinical variable (circle) node, according to a similarity matrix described in Module 2. Only variables selected by sPLS2 on the two dimensions are represented and are further filtered here according to a cutoff argument (optional).
Figure 24 shows two distinct groups of variables. The first cluster groups four clinical parameters that are mostly positively associated with selected genes. The second group includes one clinical parameter negatively associated with other selected genes. These observations are similar to what was observed in the correlation circle plot in Figure 22.
Note:
igraph R package Csardi et al. (2006)).The Clustered Image Map also allows us to visualise correlations between variables. Here we choose to represent the variables selected on the two dimensions and we save the plot as a pdf figure.
# X11() # To open a new window if the graphic is too large
cim(spls2.liver, comp = 1:2, xlab = "clinic", ylab = "genes",
# To save the plot, uncomment:
# save = 'pdf', name.save = 'cim_liver'
)
liver.toxicity data. The plot displays the similarity values (as described in Module 2) between the \(\boldsymbol X\) and \(\boldsymbol Y\) variables selected across two dimensions, and clustered with a complete Euclidean distance method." width="75%" />
Figure 25: Clustered Image Map from the sPLS2 performed on the liver.toxicity data. The plot displays the similarity values (as described in Module 2) between the \(\boldsymbol X\) and \(\boldsymbol Y\) variables selected across two dimensions, and clustered with a complete Euclidean distance method.
The CIM in Figure 25 shows that the clinical variables can be separated into three clusters, each of them either positively or negatively associated with two groups of genes. This is similar to what we have observed in Figure 22. We would give a similar interpretation to the relevance network, had we also used a cutoff threshold in cim().
Note:
To finish, we assess the performance of sPLS2. As an element of comparison, we consider sPLS2 and PLS2 that includes all variables, to give insights into the different methods.
# Comparisons of final models (PLS, sPLS)
## PLS
pls.liver <- pls(X, Y, mode = 'regression', ncomp = 2)
perf.pls.liver <- perf(pls.liver, validation = 'Mfold', folds = 10,
nrepeat = 5)
## Performance for the sPLS model ran earlier
perf.spls.liver <- perf(spls2.liver, validation = 'Mfold', folds = 10,
nrepeat = 5)
plot(c(1,2), perf.pls.liver$measures$cor.upred$summary$mean,
col = 'blue', pch = 16,
ylim = c(0.6,1), xaxt = 'n',
xlab = 'Component', ylab = 't or u Cor',
main = 's/PLS performance based on Correlation')
axis(1, 1:2) # X-axis label
points(perf.pls.liver$measures$cor.tpred$summary$mean, col = 'red', pch = 16)
points(perf.spls.liver$measures$cor.upred$summary$mean, col = 'blue', pch = 17)
points(perf.spls.liver$measures$cor.tpred$summary$mean, col = 'red', pch = 17)
legend('bottomleft', col = c('blue', 'red', 'blue', 'red'),
pch = c(16, 16, 17, 17), c('u PLS', 't PLS', 'u sPLS', 't sPLS'))
Figure 26: Comparison of the performance of PLS2 and sPLS2, based on the correlation between the actual and predicted components \(\boldsymbol{t,u}\) associated to each data set for each component.
We extract the correlation between the actual and predicted components \(\boldsymbol{t,u}\) associated to each data set in Figure 26. The correlation remains high on the first dimension, even when variables are selected. On the second dimension the correlation coefficients are equivalent or slightly lower in sPLS compared to PLS. Overall this performance comparison indicates that the variable selection in sPLS still retains relevant information compared to a model that includes all variables.
Note:
The Small Round Blue Cell Tumours (SRBCT) data set from (Khan et al. 2001) includes the expression levels of 2,308 genes measured on 63 samples. The samples are divided into four classes: 8 Burkitt Lymphoma (BL), 23 Ewing Sarcoma (EWS), 12 neuroblastoma (NB), and 20 rhabdomyosarcoma (RMS). The data are directly available in a processed and normalised format from the mixOmics package and contains the following:
$gene: A data frame with 63 rows and 2,308 columns. These are the expression levels of 2,308 genes in 63 subjects,
$class: A vector containing the class of tumour for each individual (4 classes in total),
$gene.name: A data frame with 2,308 rows and 2 columns containing further information on the genes.
More details can be found in ?srbct. We will illustrate PLS-DA and sPLS-DA which are suited for large biological data sets where the aim is to identify molecular signatures, as well as classify samples. We will analyse the gene expression levels of srbct$gene to discover which genes may best discriminate the 4 groups of tumours.
We first load the data from the package. We then set up the data so that \(\boldsymbol X\) is the gene expression matrix and \(\boldsymbol y\) is the factor indicating sample class membership. \(\boldsymbol y\) will be transformed into a dummy matrix \(\boldsymbol Y\) inside the function. We also check that the dimensions are correct and match both \(\boldsymbol X\) and \(\boldsymbol y\):
library(mixOmics)
data(srbct)
X <- srbct$gene
# Outcome y that will be internally coded as dummy:
Y <- srbct$class
dim(X); length(Y)
## [1] 63 2308
## [1] 63
summary(Y)
## EWS BL NB RMS
## 23 8 12 20
As covered in Module 3, PCA is a useful tool to explore the gene expression data and to assess for sample similarities between tumour types. Remember that PCA is an unsupervised approach, but we can colour the samples by their class to assist in interpreting the PCA (Figure 27). Here we center (default argument) and scale the data:
pca.srbct <- pca(X, ncomp = 3, scale = TRUE)
plotIndiv(pca.srbct, group = srbct$class, ind.names = FALSE,
legend = TRUE,
title = 'SRBCT, PCA comp 1 - 2')
Figure 27: Preliminary (unsupervised) analysis with PCA on the SRBCT gene expression data. Samples are projected into the space spanned by the principal components 1 and 2. The tumour types are not clustered, meaning that the major source of variation cannot be explained by tumour types. Instead, samples seem to cluster according to an unknown source of variation.
We observe almost no separation between the different tumour types in the PCA sample plot, with perhaps the exception of the NB samples that tend to cluster with other samples. This preliminary exploration teaches us two important findings:
The perf() function evaluates the performance of PLS-DA - i.e., its ability to rightly classify ‘new’ samples into their tumour category using repeated cross-validation. We initially choose a large number of components (here ncomp = 10) and assess the model as we gradually increase the number of components. Here we use 3-fold CV repeated 10 times. In Module 2, we provided further guidelines on how to choose the folds and nrepeat parameters:
plsda.srbct <- plsda(X,Y, ncomp = 10)
set.seed(30) # For reproducibility with this handbook, remove otherwise
perf.plsda.srbct <- perf(plsda.srbct, validation = 'Mfold', folds = 3,
progressBar = FALSE, # Set to TRUE to track progress
nrepeat = 10) # We suggest nrepeat = 50
plot(perf.plsda.srbct, sd = TRUE, legend.position = 'horizontal')
max.dist, centroids.dist and mahalanobis.dist. Bars show the standard deviation across the repeated folds. The plot shows that the error rate reaches a minimum from 3 components." 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alt="Tuning the number of components in PLS-DA on the SRBCT gene expression data. For each component, repeated cross-validation (10 \(\times 3-\)fold CV) is used to evaluate the PLS-DA classification performance (overall and balanced error rate BER), for each type of prediction distance; max.dist, centroids.dist and mahalanobis.dist. Bars show the standard deviation across the repeated folds. The plot shows that the error rate reaches a minimum from 3 components." width="75%" />
Figure 28: Tuning the number of components in PLS-DA on the SRBCT gene expression data. For each component, repeated cross-validation (10 \(\times 3-\)fold CV) is used to evaluate the PLS-DA classification performance (overall and balanced error rate BER), for each type of prediction distance; max.dist, centroids.dist and mahalanobis.dist. Bars show the standard deviation across the repeated folds. The plot shows that the error rate reaches a minimum from 3 components.
From the classification performance output presented in Figure 28, we observe that:
There are some slight differences between the overall and balanced error rates (BER) with BER > overall, suggesting that minority classes might be ignored from the classification task when considering the overall performance (summary(Y) shows that BL only includes 8 samples). In general the trend is the same, however, and for further tuning with sPLS-DA we will consider the BER.
The error rate decreases and reaches a minimum for ncomp = 3 for the max.dist distance. These parameters will be included in further analyses.
Notes:
ncomp) as a ‘compounding’ model (i.e. PLS-DA with component 3 includes the trained model on the previous two components).Additional numerical outputs from the performance results are listed and can be reported as performance measures (not output here):
perf.plsda.srbct
We now run our final PLS-DA model that includes three components:
final.plsda.srbct <- plsda(X,Y, ncomp = 3)
We output the sample plots for the dimensions of interest (up to three). By default, the samples are coloured according to their class membership. We also add confidence ellipses (ellipse = TRUE, confidence level set to 95% by default, see the argument ellipse.level) in Figure 29. A 3D plot could also be insightful (use the argument type = '3D').
plotIndiv(final.plsda.srbct, ind.names = FALSE, legend=TRUE,
comp=c(1,2), ellipse = TRUE,
title = 'PLS-DA on SRBCT comp 1-2',
X.label = 'PLS-DA comp 1', Y.label = 'PLS-DA comp 2')
plotIndiv(final.plsda.srbct, ind.names = FALSE, legend=TRUE,
comp=c(2,3), ellipse = TRUE,
title = 'PLS-DA on SRBCT comp 2-3',
X.label = 'PLS-DA comp 2', Y.label = 'PLS-DA comp 3')
SRBCT gene expression data. Samples are projected into the space spanned by the first three components. (a) Components 1 and 2 and (b) Components 1 and 3. Samples are coloured by their tumour subtypes. Component 1 discriminates RMS + EWS vs. NB + BL, component 2 discriminates RMS + NB vs. EWS + BL, while component 3 discriminates further the NB and BL groups. It is the combination of all three components that enables us to discriminate all classes." width="50%" />SRBCT gene expression data. Samples are projected into the space spanned by the first three components. (a) Components 1 and 2 and (b) Components 1 and 3. Samples are coloured by their tumour subtypes. Component 1 discriminates RMS + EWS vs. NB + BL, component 2 discriminates RMS + NB vs. EWS + BL, while component 3 discriminates further the NB and BL groups. It is the combination of all three components that enables us to discriminate all classes." width="50%" />
Figure 29: Sample plots from PLS-DA performed on the SRBCT gene expression data. Samples are projected into the space spanned by the first three components. (a) Components 1 and 2 and (b) Components 1 and 3. Samples are coloured by their tumour subtypes. Component 1 discriminates RMS + EWS vs. NB + BL, component 2 discriminates RMS + NB vs. EWS + BL, while component 3 discriminates further the NB and BL groups. It is the combination of all three components that enables us to discriminate all classes.
We can observe improved clustering according to tumour subtypes, compared with PCA. This is to be expected since the PLS-DA model includes the class information of each sample. We observe some discrimination between the NB and BL samples vs. the others on the first component (x-axis), and EWS and RMS vs. the others on the second component (y-axis). From the plotIndiv() function, the axis labels indicate the amount of variation explained per component. However, the interpretation of this amount is not as important as in PCA, as PLS-DA aims to maximise the covariance between components associated to \(\boldsymbol X\) and \(\boldsymbol Y\), rather than the variance \(\boldsymbol X\).
We can rerun a more extensive performance evaluation with more repeats for our final model:
set.seed(30) # For reproducibility with this handbook, remove otherwise
perf.final.plsda.srbct <- perf(final.plsda.srbct, validation = 'Mfold',
folds = 3,
progressBar = FALSE, # TRUE to track progress
nrepeat = 10) # we recommend 50
Retaining only the BER and the max.dist, numerical outputs of interest include the final overall performance for 3 components:
perf.final.plsda.srbct$error.rate$BER[, 'max.dist']
## comp1 comp2 comp3
## 0.53309783 0.22898551 0.04657609
As well as the error rate per class across each component:
perf.final.plsda.srbct$error.rate.class$max.dist
## comp1 comp2 comp3
## EWS 0.2173913 0.0826087 0.09130435
## BL 0.7250000 0.4250000 0.00000000
## NB 0.3750000 0.3583333 0.02500000
## RMS 0.8150000 0.0500000 0.07000000
From this output, we can see that the first component tends to classify EWS and NB better than the other classes. As components 2 and then 3 are added, the classification improves for all classes. However we see a slight increase in classification error in component 3 for EWS and RMS while BL is perfectly classified. A permutation test could also be conducted to conclude about the significance of the differences between sample groups, but is not currently implemented in the package.
A prediction background can be added to the sample plot by calculating a background surface first, before overlaying the sample plot (Figure 30, see Extra Reading material, or ?background.predict). We give an example of the code below based on the maximum prediction distance:
background.max <- background.predict(final.plsda.srbct,
comp.predicted = 2,
dist = 'max.dist')
plotIndiv(final.plsda.srbct, comp = 1:2, group = srbct$class,
ind.names = FALSE, title = 'Maximum distance',
legend = TRUE, background = background.max)
Figure 30: Sample plots from PLS-DA on the SRBCT gene expression data and prediction areas based on prediction distances. From our usual sample plot, we overlay a background prediction area based on permutations from the first two PLS-DA components using the three different types of prediction distances. The outputs show how the prediction distance can influence the quality of the prediction, with samples projected into a wrong class area, and hence resulting in predicted misclassification. (Currently, the prediction area background can only be calculated for the first two components).
Figure 30 shows the differences in prediction according to the prediction distance, and can be used as a further diagnostic tool for distance choice. It also highlights the characteristics of the distances. For example the max.dist is a linear distance, whereas both centroids.dist and mahalanobis.dist are non linear. Our experience has shown that as discrimination of the classes becomes more challenging, the complexity of the distances (from maximum to Mahalanobis distance) should increase, see details in the Extra reading material.
In high-throughput experiments, we expect that many of the 2308 genes in \(\boldsymbol X\) are noisy or uninformative to characterise the different classes. An sPLS-DA analysis will help refine the sample clusters and select a small subset of variables relevant to discriminate each class.
We estimate the classification error rate with respect to the number of selected variables in the model with the function tune.splsda(). The tuning is being performed one component at a time inside the function and the optimal number of variables to select is automatically retrieved after each component run, as described in Module 2.
Previously, we determined the number of components to be ncomp = 3 with PLS-DA. Here we set ncomp = 4 to further assess if this would be the case for a sparse model, and use 5-fold cross validation repeated 10 times. We also choose the maximum prediction distance.
Note:
We first define a grid of keepX values. For example here, we define a fine grid at the start, and then specify a coarser, larger sequence of values:
# Grid of possible keepX values that will be tested for each comp
list.keepX <- c(1:10, seq(20, 100, 10))
list.keepX
## [1] 1 2 3 4 5 6 7 8 9 10 20 30 40 50 60 70 80 90 100
# This chunk takes ~ 2 min to run
# Some convergence issues may arise but it is ok as this is run on CV folds
tune.splsda.srbct <- tune.splsda(X, Y, ncomp = 4, validation = 'Mfold',
folds = 5, dist = 'max.dist',
test.keepX = list.keepX, nrepeat = 10)
The following command line will output the mean error rate for each component and each tested keepX value given the past (tuned) components.
# Just a head of the classification error rate per keepX (in rows) and comp
head(tune.splsda.srbct$error.rate)
## comp1 comp2 comp3 comp4
## 1 0.5994928 0.3168841 0.07839674 0.01536232
## 2 0.5683062 0.3078351 0.05471920 0.01302536
## 3 0.5482246 0.2932065 0.04076087 0.01302536
## 4 0.5397101 0.2806431 0.03821558 0.01302536
## 5 0.5317754 0.2790489 0.03488225 0.01302536
## 6 0.5248098 0.2738406 0.02759058 0.01411232
When we examine each individual row, this output globally shows that the classification error rate continues to decrease after the third component in sparse PLS-DA.
We display the mean classification error rate on each component, bearing in mind that each component is conditional on the previous components calculated with the optimal number of selected variables. The diamond in Figure 31 indicates the best keepX value to achieve the lowest error rate per component.
# To show the error bars across the repeats:
plot(tune.splsda.srbct, sd = TRUE)
keepX value chosen for the previous component (comp 1)." 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" alt="Tuning keepX for the sPLS-DA performed on the SRBCT gene expression data. Each coloured line represents the balanced error rate (y-axis) per component across all tested keepX values (x-axis) with the standard deviation based on the repeated cross-validation folds. The diamond indicates the optimal keepX value on a particular component which achieves the lowest classification error rate as determined with a one-sided \(t-\)test. As sPLS-DA is an iterative algorithm, values represented for a given component (e.g. comp 1 to 2) include the optimal keepX value chosen for the previous component (comp 1)." width="75%" />
Figure 31: Tuning keepX for the sPLS-DA performed on the SRBCT gene expression data. Each coloured line represents the balanced error rate (y-axis) per component across all tested keepX values (x-axis) with the standard deviation based on the repeated cross-validation folds. The diamond indicates the optimal keepX value on a particular component which achieves the lowest classification error rate as determined with a one-sided \(t-\)test. As sPLS-DA is an iterative algorithm, values represented for a given component (e.g. comp 1 to 2) include the optimal keepX value chosen for the previous component (comp 1).
The tuning results depend on the tuning grid list.keepX, as well as the values chosen for folds and nrepeat. Therefore, we recommend assessing the performance of the final model, as well as examining the stability of the selected variables across the different folds, as detailed in the next section.
Figure 31 shows that the error rate decreases when more components are included in sPLS-DA. To obtain a more reliable estimation of the error rate, the number of repeats should be increased (between 50 to 100). This type of graph helps not only to choose the ‘optimal’ number of variables to select, but also to confirm the number of components ncomp. From the code below, we can assess that in fact, the addition of a fourth component does not improve the classification (no statistically significant improvement according to a one-sided \(t-\)test), hence we can choose ncomp = 3.
# The optimal number of components according to our one-sided t-tests
tune.splsda.srbct$choice.ncomp$ncomp
## [1] 3
# The optimal keepX parameter according to minimal error rate
tune.splsda.srbct$choice.keepX
## comp1 comp2 comp3 comp4
## 8 90 40 40
Here is our final sPLS-DA model with three components and the optimal keepX obtained from our tuning step.
You can choose to skip the tuning step, and input your arbitrarily chosen parameters in the following code (simply specify your own ncomp and keepX values):
# Optimal number of components based on t-tests on the error rate
ncomp <- tune.splsda.srbct$choice.ncomp$ncomp
ncomp
## [1] 3
# Optimal number of variables to select
select.keepX <- tune.splsda.srbct$choice.keepX[1:ncomp]
select.keepX
## comp1 comp2 comp3
## 8 90 40
splsda.srbct <- splsda(X, Y, ncomp = ncomp, keepX = select.keepX)
The performance of the model with the ncomp and keepX parameters is assessed with the perf() function. We use 5-fold validation (folds = 5), repeated 10 times (nrepeat = 10) for illustrative purposes, but we recommend increasing to nrepeat = 50. Here we choose the max.dist prediction distance, based on our results obtained with PLS-DA.
The classification error rates that are output include both the overall error rate, as well as the balanced error rate (BER) when the number of samples per group is not balanced - as is the case in this study.
set.seed(34) # For reproducibility with this handbook, remove otherwise
perf.splsda.srbct <- perf(splsda.srbct, folds = 5, validation = "Mfold",
dist = "max.dist", progressBar = FALSE, nrepeat = 10)
# perf.splsda.srbct # Lists the different outputs
perf.splsda.srbct$error.rate
## $overall
## max.dist
## comp1 0.45238095
## comp2 0.21746032
## comp3 0.01587302
##
## $BER
## max.dist
## comp1 0.5203895
## comp2 0.2677989
## comp3 0.0169837
We can also examine the error rate per class:
perf.splsda.srbct$error.rate.class
## $max.dist
## comp1 comp2 comp3
## EWS 0.0173913 0.008695652 0.03043478
## BL 0.4875000 0.262500000 0.03750000
## NB 0.9166667 0.575000000 0.00000000
## RMS 0.6600000 0.225000000 0.00000000
These results can be compared with the performance of PLS-DA and show the benefits of variable selection to not only obtain a parsimonious model, but also to improve the classification error rate (overall and per class).
During the repeated cross-validation process in perf() we can record how often the same variables are selected across the folds. This information is important to answer the question: How reproducible is my gene signature when the training set is perturbed via cross-validation?.
par(mfrow=c(1,2))
# For component 1
stable.comp1 <- perf.splsda.srbct$features$stable$comp1
barplot(stable.comp1, xlab = 'variables selected across CV folds',
ylab = 'Stability frequency',
main = 'Feature stability for comp = 1')
# For component 2
stable.comp2 <- perf.splsda.srbct$features$stable$comp2
barplot(stable.comp2, xlab = 'variables selected across CV folds',
ylab = 'Stability frequency',
main = 'Feature stability for comp = 2')
par(mfrow=c(1,1))
Figure 32: Stability of variable selection from the sPLS-DA on the SRBCT gene expression data. We use a by-product from perf() to assess how often the same variables are selected for a given keepX value in the final sPLS-DA model. The barplot represents the frequency of selection across repeated CV folds for each selected gene for component 1 and 2. The genes are ranked according to decreasing frequency.
Figure 32 shows that the genes selected on component 1 are moderately stable (frequency < 0.5) whereas those selected on component 2 are more stable (frequency < 0.7). This can be explained as there are various combinations of genes that are discriminative on component 1, whereas the number of combinations decreases as we move to component 2 which attempts to refine the classification.
The function selectVar() outputs the variables selected for a given component and their loading values (ranked in decreasing absolute value). We concatenate those results with the feature stability, as shown here for variables selected on component 1:
# First extract the name of selected var:
select.name <- selectVar(splsda.srbct, comp = 1)$name
# Then extract the stability values from perf:
stability <- perf.splsda.srbct$features$stable$comp1[select.name]
# Just the head of the stability of the selected var:
head(cbind(selectVar(splsda.srbct, comp = 1)$value, stability))
## value.var Var1 Freq
## g123 0.6638973 g123 0.54
## g846 0.4518981 g846 0.54
## g1606 0.3015015 g1606 0.46
## g335 0.2953710 g335 0.50
## g836 0.2568761 g836 0.50
## g783 0.2110122 g783 0.26
As we hinted previously, the genes selected on the first component are not necessarily the most stable, suggesting that different combinations can lead to the same discriminative ability of the model. The stability increases in the following components, as the classification task becomes more refined.
Note:
vip() function on splsda.srbct.Previously, we showed the ellipse plots displayed for each class. Here we also use the star argument (star = TRUE), which displays arrows starting from each group centroid towards each individual sample (Figure 33).
plotIndiv(splsda.srbct, comp = c(1,2),
ind.names = FALSE,
ellipse = TRUE, legend = TRUE,
star = TRUE,
title = 'SRBCT, sPLS-DA comp 1 - 2')
plotIndiv(splsda.srbct, comp = c(2,3),
ind.names = FALSE,
ellipse = TRUE, legend = TRUE,
star = TRUE,
title = 'SRBCT, sPLS-DA comp 2 - 3')
SRBCT gene expression data. Samples are projected into the space spanned by the first three components. The plots represent 95% ellipse confidence intervals around each sample class. The start of each arrow represents the centroid of each class in the space spanned by the components. (a) Components 1 and 2 and (b) Components 2 and 3. Samples are coloured by their tumour subtype. Component 1 discriminates BL vs. the rest, component 2 discriminates EWS vs. the rest, while component 3 further discriminates NB vs. RMS vs. the rest. The combination of all three components enables us to discriminate all classes." width="50%" />SRBCT gene expression data. Samples are projected into the space spanned by the first three components. The plots represent 95% ellipse confidence intervals around each sample class. The start of each arrow represents the centroid of each class in the space spanned by the components. (a) Components 1 and 2 and (b) Components 2 and 3. Samples are coloured by their tumour subtype. Component 1 discriminates BL vs. the rest, component 2 discriminates EWS vs. the rest, while component 3 further discriminates NB vs. RMS vs. the rest. The combination of all three components enables us to discriminate all classes." width="50%" />
Figure 33: Sample plots from the sPLS-DA performed on the SRBCT gene expression data. Samples are projected into the space spanned by the first three components. The plots represent 95% ellipse confidence intervals around each sample class. The start of each arrow represents the centroid of each class in the space spanned by the components. (a) Components 1 and 2 and (b) Components 2 and 3. Samples are coloured by their tumour subtype. Component 1 discriminates BL vs. the rest, component 2 discriminates EWS vs. the rest, while component 3 further discriminates NB vs. RMS vs. the rest. The combination of all three components enables us to discriminate all classes.
The sample plots are different from PLS-DA (Figure 29) with an overlap of specific classes (i.e. NB + RMS on component 1 and 2), that are then further separated on component 3, thus showing how the genes selected on each component discriminate particular sets of sample groups.
We represent the genes selected with sPLS-DA on the correlation circle plot. Here to increase interpretation, we specify the argument var.names as the first 10 characters of the gene names (Figure 34). We also reduce the size of the font with the argument cex.
Note:
plotvar() as an object to output the coordinates and variable names if the plot is too cluttered.var.name.short <- substr(srbct$gene.name[, 2], 1, 10)
plotVar(splsda.srbct, comp = c(1,2),
var.names = list(var.name.short), cex = 3)
Figure 34: Correlation circle plot representing the genes selected by sPLS-DA performed on the SRBCT gene expression data. Gene names are truncated to the first 10 characters. Only the genes selected by sPLS-DA are shown in components 1 and 2. We observe three groups of genes (positively associated with component 1, and positively or negatively associated with component 2). This graphic should be interpreted in conjunction with the sample plot.
By considering both the correlation circle plot (Figure 34) and the sample plot in Figure 33, we observe that a group of genes with a positive correlation with component 1 (‘EH domain’, ‘proteasome’ etc.) are associated with the BL samples. We also observe two groups of genes either positively or negatively correlated with component 2. These genes are likely to characterise either the NB + RMS classes, or the EWS class. This interpretation can be further examined with the plotLoadings() function.
In this plot, the loading weights of each selected variable on each component are represented (see Module 2). The colours indicate the group in which the expression of the selected gene is maximal based on the mean (method = 'median' is also available for skewed data). For example on component 1:
plotLoadings(splsda.srbct, comp = 1, method = 'mean', contrib = 'max',
name.var = var.name.short)
SRBCT gene expression data. Genes are ranked according to their loading weight (most important at the bottom to least important at the top), represented as a barplot. Colours indicate the class for which a particular gene is maximally expressed, on average, in this particular class. The plot helps to further characterise the gene signature and should be interpreted jointly with the sample plot (Figure 33)." width="75%" />
Figure 35: Loading plot of the genes selected by sPLS-DA on component 1 on the SRBCT gene expression data. Genes are ranked according to their loading weight (most important at the bottom to least important at the top), represented as a barplot. Colours indicate the class for which a particular gene is maximally expressed, on average, in this particular class. The plot helps to further characterise the gene signature and should be interpreted jointly with the sample plot (Figure 33).
Here all genes are associated with BL (on average, their expression levels are higher in this class than in the other classes).
Notes:
ndisplay to only display the top selected genes if the signature is too large.contrib = 'min' to interpret the inverse trend of the signature (i.e. which genes have the smallest expression in which class, here a mix of NB and RMS samples).To complete the visualisation, the CIM in this special case is a simple hierarchical heatmap (see ?cim) representing the expression levels of the genes selected across all three components with respect to each sample. Here we use an Euclidean distance with Complete agglomeration method, and we specify the argument row.sideColors to colour the samples according to their tumour type (Figure 36).
cim(splsda.srbct, row.sideColors = color.mixo(Y))
Figure 36: Clustered Image Map of the genes selected by sPLS-DA on the SRBCT gene expression data across all 3 components. A hierarchical clustering based on the gene expression levels of the selected genes, with samples in rows coloured according to their tumour subtype (using Euclidean distance with Complete agglomeration method). As expected, we observe a separation of all different tumour types, which are characterised by different levels of expression.
The heatmap shows the level of expression of the genes selected by sPLS-DA across all three components, and the overall ability of the gene signature to discriminate the tumour subtypes.
Note:
comp if you wish to visualise a specific set of components in cim().In this section, we artificially create an ‘external’ test set on which we want to predict the class membership to illustrate the prediction process in sPLS-DA (see Extra Reading material). We randomly select 50 samples from the srbct study as part of the training set, and the remainder as part of the test set:
set.seed(33) # For reproducibility with this handbook, remove otherwise
train <- sample(1:nrow(X), 50) # Randomly select 50 samples in training
test <- setdiff(1:nrow(X), train) # Rest is part of the test set
# Store matrices into training and test set:
X.train <- X[train, ]
X.test <- X[test,]
Y.train <- Y[train]
Y.test <- Y[test]
# Check dimensions are OK:
dim(X.train); dim(X.test)
## [1] 50 2308
## [1] 13 2308
Here we assume that the tuning step was performed on the training set only (it is really important to tune only on the training step to avoid overfitting), and that the optimal keepX values are, for example, keepX = c(20,30,40) on three components. The final model on the training data is:
train.splsda.srbct <- splsda(X.train, Y.train, ncomp = 3, keepX = c(20,30,40))
We now apply the trained model on the test set X.test and we specify the prediction distance, for example mahalanobis.dist (see also ?predict.splsda):
predict.splsda.srbct <- predict(train.splsda.srbct, X.test,
dist = "mahalanobis.dist")
The $class output of our object predict.splsda.srbct gives the predicted classes of the test samples.
First we concatenate the prediction for each of the three components (conditionally on the previous component) and the real class - in a real application case you may not know the true class.
# Just the head:
head(data.frame(predict.splsda.srbct$class, Truth = Y.test))
## mahalanobis.dist.comp1 mahalanobis.dist.comp2 mahalanobis.dist.comp3
## EWS.T7 EWS EWS EWS
## EWS.T15 EWS EWS EWS
## EWS.C8 EWS EWS EWS
## EWS.C10 EWS EWS EWS
## BL.C8 BL BL BL
## NB.C6 NB NB NB
## Truth
## EWS.T7 EWS
## EWS.T15 EWS
## EWS.C8 EWS
## EWS.C10 EWS
## BL.C8 BL
## NB.C6 NB
If we only look at the final prediction on component 2, compared to the real class:
# Compare prediction on the second component and change as factor
predict.comp2 <- predict.splsda.srbct$class$mahalanobis.dist[,2]
table(factor(predict.comp2, levels = levels(Y)), Y.test)
## Y.test
## EWS BL NB RMS
## EWS 4 0 0 0
## BL 0 1 0 0
## NB 0 0 1 1
## RMS 0 0 0 6
And on the third compnent:
# Compare prediction on the third component and change as factor
predict.comp3 <- predict.splsda.srbct$class$mahalanobis.dist[,3]
table(factor(predict.comp3, levels = levels(Y)), Y.test)
## Y.test
## EWS BL NB RMS
## EWS 4 0 0 0
## BL 0 1 0 0
## NB 0 0 1 0
## RMS 0 0 0 7
The prediction is better on the third component, compared to a 2-component model.
Next, we look at the output $predict, which gives the predicted dummy scores assigned for each test sample and each class level for a given component (as explained in Extra Reading material). Each column represents a class category:
# On component 3, just the head:
head(predict.splsda.srbct$predict[, , 3])
## EWS BL NB RMS
## EWS.T7 1.26848551 -0.05273773 -0.24070902 0.024961232
## EWS.T15 1.15058424 -0.02222145 -0.11877994 -0.009582845
## EWS.C8 1.25628411 0.05481026 -0.16500118 -0.146093198
## EWS.C10 0.83995956 0.10871106 0.16452934 -0.113199949
## BL.C8 0.02431262 0.90877176 0.01775304 0.049162580
## NB.C6 0.06738230 0.05086884 0.86247360 0.019275265
In PLS-DA and sPLS-DA, the final prediction call is given based on this matrix on which a pre-specified distance (such as mahalanobis.dist here) is applied. From this output, we can understand the link between the dummy matrix \(\boldsymbol Y\), the prediction, and the importance of choosing the prediction distance. More details are provided in Extra Reading material.
As PLS-DA acts as a classifier, we can plot the AUC (Area Under The Curve) ROC (Receiver Operating Characteristics) to complement the sPLS-DA classification performance results. The AUC is calculated from training cross-validation sets and averaged. The ROC curve is displayed in Figure 37. In a multiclass setting, each curve represents one class vs. the others and the AUC is indicated in the legend, and also in the numerical output:
auc.srbct <- auroc(splsda.srbct)
SRBCT gene expression data on component 1 averaged across one-vs.-all comparisons. Numerical outputs include the AUC and a Wilcoxon test p-value for each ‘one vs. other’ class comparisons that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the sPLS-DA model can distinguish BL subjects from the other groups with a high true positive and low false positive rate, while the model is less well able to distinguish samples from other classes on component 1." width="75%" />
Figure 37: ROC curve and AUC from sPLS-DA on the SRBCT gene expression data on component 1 averaged across one-vs.-all comparisons. Numerical outputs include the AUC and a Wilcoxon test p-value for each ‘one vs. other’ class comparisons that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the sPLS-DA model can distinguish BL subjects from the other groups with a high true positive and low false positive rate, while the model is less well able to distinguish samples from other classes on component 1.
## $Comp1
## AUC p-value
## EWS vs Other(s) 0.3902 1.493e-01
## BL vs Other(s) 1.0000 5.586e-06
## NB vs Other(s) 0.8105 8.821e-04
## RMS vs Other(s) 0.6523 5.308e-02
##
## $Comp2
## AUC p-value
## EWS vs Other(s) 1.0000 5.135e-11
## BL vs Other(s) 1.0000 5.586e-06
## NB vs Other(s) 0.8627 1.020e-04
## RMS vs Other(s) 0.8140 6.699e-05
##
## $Comp3
## AUC p-value
## EWS vs Other(s) 1 5.135e-11
## BL vs Other(s) 1 5.586e-06
## NB vs Other(s) 1 8.505e-08
## RMS vs Other(s) 1 2.164e-10
The ideal ROC curve should be along the top left corner, indicating a high true positive rate (sensitivity on the y-axis) and a high true negative rate (or low 100 - specificity on the x-axis), with an AUC close to 1. This is the case for BL vs. the others on component 1. The numerical output shows a perfect classification on component 3.
Note:
N-Integration is the framework of having multiple datasets which measure different aspects of the same samples. For example, you may have transcriptomic, genetic and proteomic data for the same set of cells. N-integrative methods are built to use the information in all three of these dataframes simultaenously.
DIABLO is a novel mixOmics framework for the integration of multiple data sets while explaining their relationship with a categorical outcome variable. DIABLO stands for Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies. It can also be referred to as Multiblock (s)PLS-DA.
Human breast cancer is a heterogeneous disease in terms of molecular alterations, cellular composition, and clinical outcome. Breast tumours can be classified into several subtypes, according to their levels of mRNA expression (Sørlie et al. 2001). Here we consider a subset of data generated by The Cancer Genome Atlas Network (Cancer Genome Atlas Network et al. 2012). For the package, data were normalised, and then drastically prefiltered for illustrative purposes.
The data were divided into a training set with a subset of 150 samples from the mRNA, miRNA and proteomics data, and a test set including 70 samples, but only with mRNA and miRNA data (the proteomics data are missing). The aim of this integrative analysis is to identify a highly correlated multi-omics signature discriminating the breast cancer subtypes Basal, Her2 and LumA.
The breast.TCGA (more details can be found in ?breast.TCGA) is a list containing training and test sets of omics data data.train and data.test which include:
$miRNA: A data frame with 150 (70) rows and 184 columns in the training (test) data set for the miRNA expression levels,$mRNA: A data frame with 150 (70) rows and 520 columns in the training (test) data set for the mRNA expression levels,$protein: A data frame with 150 rows and 142 columns in the training data set for the protein abundance (there are no proteomics in the test set),$subtype: A factor indicating the breast cancer subtypes in the training (for 150 samples) and test sets (for 70 samples).This case study covers an interesting scenario where one omic data set is missing in the test set, but because the method generates a set of components per training data set, we can still assess the prediction or performance evaluation using majority or weighted prediction vote.
To illustrate the multiblock sPLS-DA approach, we will integrate the expression levels of miRNA, mRNA and the abundance of proteins while discriminating the subtypes of breast cancer, then predict the subtypes of the samples in the test set.
The input data is first set up as a list of \(Q\) matrices \(\boldsymbol X_1, \dots, \boldsymbol X_Q\) and a factor indicating the class membership of each sample \(\boldsymbol Y\). Each data frame in \(\boldsymbol X\) should be named as we will match these names with the keepX parameter for the sparse method.
library(mixOmics)
data(breast.TCGA)
# Extract training data and name each data frame
# Store as list
X <- list(mRNA = breast.TCGA$data.train$mrna,
miRNA = breast.TCGA$data.train$mirna,
protein = breast.TCGA$data.train$protein)
# Outcome
Y <- breast.TCGA$data.train$subtype
summary(Y)
## Basal Her2 LumA
## 45 30 75
The choice of the design can be motivated by different aspects, including:
Biological apriori knowledge: Should we expect mRNA and miRNA to be highly correlated?
Analytical aims: As further developed in Singh et al. (2019), a compromise needs to be achieved between a classification and prediction task, and extracting the correlation structure of the data sets. A full design with weights = 1 will favour the latter, but at the expense of classification accuracy, whereas a design with small weights will lead to a highly predictive signature. This pertains to the complexity of the analytical task involved as several constraints are included in the optimisation procedure. For example, here we choose a 0.1 weighted model as we are interested in predicting test samples later in this case study.
design <- matrix(0.1, ncol = length(X), nrow = length(X),
dimnames = list(names(X), names(X)))
diag(design) <- 0
design
## mRNA miRNA protein
## mRNA 0.0 0.1 0.1
## miRNA 0.1 0.0 0.1
## protein 0.1 0.1 0.0
Note however that even with this design, we will still unravel a correlated signature as we require all data sets to explain the same outcome \(\boldsymbol y\), as well as maximising pairs of covariances between data sets.
res1.pls.tcga <- pls(X$mRNA, X$protein, ncomp = 1)
cor(res1.pls.tcga$variates$X, res1.pls.tcga$variates$Y)
res2.pls.tcga <- pls(X$mRNA, X$miRNA, ncomp = 1)
cor(res2.pls.tcga$variates$X, res2.pls.tcga$variates$Y)
res3.pls.tcga <- pls(X$protein, X$miRNA, ncomp = 1)
cor(res3.pls.tcga$variates$X, res3.pls.tcga$variates$Y)
## comp1
## comp1 0.9031761
## comp1
## comp1 0.8456299
## comp1
## comp1 0.7982008
The data sets taken in a pairwise manner are highly correlated, indicating that a design with weights \(\sim 0.8 - 0.9\) could be chosen.
As in the PLS-DA framework presented in Module 3, we first fit a block.plsda model without variable selection to assess the global performance of the model and choose the number of components. We run perf() with 10-fold cross validation repeated 10 times for up to 5 components and with our specified design matrix. Similar to PLS-DA, we obtain the performance of the model with respect to the different prediction distances (Figure 38):
diablo.tcga <- block.plsda(X, Y, ncomp = 5, design = design)
set.seed(123) # For reproducibility, remove for your analyses
perf.diablo.tcga = perf(diablo.tcga, validation = 'Mfold', folds = 10, nrepeat = 10)
#perf.diablo.tcga$error.rate # Lists the different types of error rates
# Plot of the error rates based on weighted vote
plot(perf.diablo.tcga)
Figure 38: Choosing the number of components in block.plsda using perf() with 10 x 10-fold CV function in the breast.TCGA study. Classification error rates (overall and balanced, see Module 2) are represented on the y-axis with respect to the number of components on the x-axis for each prediction distance presented in PLS-DA in Seciton 3.4 and detailed in Extra reading material 3 from Module 3. Bars show the standard deviation across the 10 repeated folds. The plot shows that the error rate reaches a minimum from 2 to 3 dimensions.
The performance plot indicates that two components should be sufficient in the final model, and that the centroids distance might lead to better prediction. A balanced error rate (BER) should be considered for further analysis.
The following outputs the optimal number of components according to the prediction distance and type of error rate (overall or balanced), as well as a prediction weighting scheme illustrated further below.
perf.diablo.tcga$choice.ncomp$WeightedVote
## max.dist centroids.dist mahalanobis.dist
## Overall.ER 3 3 3
## Overall.BER 3 2 3
Thus, here we choose our final ncomp value:
ncomp <- perf.diablo.tcga$choice.ncomp$WeightedVote["Overall.BER", "centroids.dist"]
We then choose the optimal number of variables to select in each data set using the tune.block.splsda function. The function tune() is run with 10-fold cross validation, but repeated only once (nrepeat = 1) for illustrative and computational reasons here. For a thorough tuning process, we advise increasing the nrepeat argument to 10-50, or more.
We choose a keepX grid that is relatively fine at the start, then coarse. If the data sets are easy to classify, the tuning step may indicate the smallest number of variables to separate the sample groups. Hence, we start our grid at the value 5 to avoid a too small signature that may preclude biological interpretation.
# chunk takes about 2 min to run
set.seed(123) # for reproducibility
test.keepX <- list(mRNA = c(5:9, seq(10, 25, 5)),
miRNA = c(5:9, seq(10, 20, 2)),
proteomics = c(seq(5, 25, 5)))
tune.diablo.tcga <- tune.block.splsda(X, Y, ncomp = 2,
test.keepX = test.keepX, design = design,
validation = 'Mfold', folds = 10, nrepeat = 1,
BPPARAM = BiocParallel::SnowParam(workers = 2),
dist = "centroids.dist")
list.keepX <- tune.diablo.tcga$choice.keepX
Note:
?tune.block.splsda.The number of features to select on each component is returned and stored for the final model:
list.keepX
## $mRNA
## [1] 8 25
##
## $miRNA
## [1] 14 5
##
## $protein
## [1] 10 5
Note:
ncomp and keepX parameters (as a list for the latter, as shown below).list.keepX <- list( mRNA = c(8, 25), miRNA = c(14,5), protein = c(10, 5))
The final multiblock sPLS-DA model includes the tuned parameters and is run as:
diablo.tcga <- block.splsda(X, Y, ncomp = ncomp,
keepX = list.keepX, design = design)
## Design matrix has changed to include Y; each block will be
## linked to Y.
#06.tcga # Lists the different functions of interest related to that object
A warning message informs us that the outcome \(\boldsymbol Y\) has been included automatically in the design, so that the covariance between each block’s component and the outcome is maximised, as shown in the final design output:
diablo.tcga$design
## mRNA miRNA protein Y
## mRNA 0.0 0.1 0.1 1
## miRNA 0.1 0.0 0.1 1
## protein 0.1 0.1 0.0 1
## Y 1.0 1.0 1.0 0
The selected variables can be extracted with the function selectVar(), for example in the mRNA block, along with their loading weights (not output here):
# mRNA variables selected on component 1
selectVar(diablo.tcga, block = 'mRNA', comp = 1)
Note:
perf() function, similar to the example given in the PLS-DA analysis (Module 3).plotDiabloplotDiablo() is a diagnostic plot to check whether the correlations between components from each data set were maximised as specified in the design matrix. We specify the dimension to be assessed with the ncomp argument (Figure 39).
plotDiablo(diablo.tcga, ncomp = 1)
breast.TCGA study. Samples are represented based on the specified component (here ncomp = 1) for each data set (mRNA, miRNA and protein). Samples are coloured by breast cancer subtype (Basal, Her2 and LumA) and 95% confidence ellipse plots are represented. The bottom left numbers indicate the correlation coefficients between the first components from each data set. In this example, mRNA expression and protein concentration are highly correlated on the first dimension." width="75%" />
Figure 39: Diagnostic plot from multiblock sPLS-DA applied on the breast.TCGA study. Samples are represented based on the specified component (here ncomp = 1) for each data set (mRNA, miRNA and protein). Samples are coloured by breast cancer subtype (Basal, Her2 and LumA) and 95% confidence ellipse plots are represented. The bottom left numbers indicate the correlation coefficients between the first components from each data set. In this example, mRNA expression and protein concentration are highly correlated on the first dimension.
The plot indicates that the first components from all data sets are highly correlated. The colours and ellipses represent the sample subtypes and indicate the discriminative power of each component to separate the different tumour subtypes. Thus, multiblock sPLS-DA is able to extract a strong correlation structure between data sets, as well as discriminate the breast cancer subtypes on the first component.
plotIndivThe sample plot with the plotIndiv() function projects each sample into the space spanned by the components from each block, resulting in a series of graphs corresponding to each data set (Figure 40). The optional argument blocks can output a specific data set. Ellipse plots are also available (argument ellipse = TRUE).
plotIndiv(diablo.tcga, ind.names = FALSE, legend = TRUE,
title = 'TCGA, DIABLO comp 1 - 2')
breast.TCGA study. The samples are plotted according to their scores on the first 2 components for each data set. Samples are coloured by cancer subtype and are classified into three classes: Basal, Her2 and LumA. The plot shows the degree of agreement between the different data sets and the discriminative ability of each data set." width="75%" />
Figure 40: Sample plot from multiblock sPLS-DA performed on the breast.TCGA study. The samples are plotted according to their scores on the first 2 components for each data set. Samples are coloured by cancer subtype and are classified into three classes: Basal, Her2 and LumA. The plot shows the degree of agreement between the different data sets and the discriminative ability of each data set.
This type of graphic allows us to better understand the information extracted from each data set and its discriminative ability. Here we can see that the LumA group can be difficult to classify in the miRNA data.
Note:
block = 'average' that averages the components from all blocks to produce a single plot. The argument block='weighted.average' is a weighted average of the components according to their correlation with the components associated with the outcome.plotArrowIn the arrow plot in Figure 41, the start of the arrow indicates the centroid between all data sets for a given sample and the tip of the arrow the location of that same sample but in each block. Such graphics highlight the agreement between all data sets at the sample level when modelled with multiblock sPLS-DA.
plotArrow(diablo.tcga, ind.names = FALSE, legend = TRUE,
title = 'TCGA, DIABLO comp 1 - 2')
breast.TCGA study. The samples are projected into the space spanned by the first two components for each data set then overlaid across data sets. The start of the arrow indicates the centroid between all data sets for a given sample and the tip of the arrow the location of the same sample in each block. Arrows further from their centroid indicate some disagreement between the data sets. Samples are coloured by cancer subtype (Basal, Her2 and LumA)." width="75%" />
Figure 41: Arrow plot from multiblock sPLS-DA performed on the breast.TCGA study. The samples are projected into the space spanned by the first two components for each data set then overlaid across data sets. The start of the arrow indicates the centroid between all data sets for a given sample and the tip of the arrow the location of the same sample in each block. Arrows further from their centroid indicate some disagreement between the data sets. Samples are coloured by cancer subtype (Basal, Her2 and LumA).
This plot shows that globally, the discrimination of all breast cancer subtypes can be extracted from all data sets, however, there are some dissimilarities at the samples level across data sets (the common information cannot be extracted in the same way across data sets).
The visualisation of the selected variables is crucial to mine their associations in multiblock sPLS-DA. Here we revisit existing outputs presented in Module 2 with further developments for multiple data set integration. All the plots presented provide complementary information for interpreting the results.
plotVarThe correlation circle plot highlights the contribution of each selected variable to each component. Important variables should be close to the large circle (see Module 2). Here, only the variables selected on components 1 and 2 are depicted (across all blocks), see Figure 42. Clusters of points indicate a strong correlation between variables. For better visibility we chose to hide the variable names.
plotVar(diablo.tcga, var.names = FALSE, style = 'graphics', legend = TRUE,
pch = c(16, 17, 15), cex = c(2,2,2),
col = c('darkorchid', 'brown1', 'lightgreen'),
title = 'TCGA, DIABLO comp 1 - 2')
Figure 42: Correlation circle plot from multiblock sPLS-DA performed on the breast.TCGA study. The variable coordinates are defined according to their correlation with the first and second components for each data set. Variable types are indicated with different symbols and colours, and are overlaid on the same plot. The plot highlights the potential associations within and between different variable types when they are important in defining their own component.
The correlation circle plot shows some positive correlations (between selected miRNA and proteins, between selected proteins and mRNA) and negative correlations between mRNAand miRNA on component 1. The correlation structure is less obvious on component 2, but we observe some key selected features (proteins and miRNA) that seem to highly contribute to component 2.
Note:
These results can be further investigated by showing the variable names on this plot (or extracting their coordinates available from the plot saved into an object, see ?plotVar), and looking at various outputs from selectVar() and plotLoadings().
You can choose to only show specific variable type names, e.g. var.names = c(FALSE, FALSE, TRUE) (where each argument is assigned to a data set in \(\boldsymbol X\)). Here for example, the protein names only would be output.
circosPlotThe circos plot represents the correlations between variables of different types, represented on the side quadrants. Several display options are possible, to show within and between connections between blocks, and expression levels of each variable according to each class (argument line = TRUE). The circos plot is built based on a similarity matrix, which was extended to the case of multiple data sets from González et al. (2012) (see also Module 2 and Extra Reading material from that module). A cutoff argument can be further included to visualise correlation coefficients above this threshold in the multi-omics signature (Figure 43). The colours for the blocks and correlation lines can be chosen with color.blocks and color.cor respectively:
circosPlot(diablo.tcga, cutoff = 0.7, line = TRUE,
color.blocks = c('darkorchid', 'brown1', 'lightgreen'),
color.cor = c("chocolate3","grey20"), size.labels = 1.5)
breast.TCGA study. The plot represents the correlations greater than 0.7 between variables of different types, represented on the side quadrants. The internal connecting lines show the positive (negative) correlations. The outer lines show the expression levels of each variable in each sample group (Basal, Her2 and LumA)." width="75%" />
Figure 43: Circos plot from multiblock sPLS-DA performed on the breast.TCGA study. The plot represents the correlations greater than 0.7 between variables of different types, represented on the side quadrants. The internal connecting lines show the positive (negative) correlations. The outer lines show the expression levels of each variable in each sample group (Basal, Her2 and LumA).
The circos plot enables us to visualise cross-correlations between data types, and the nature of these correlations (positive or negative). Here we observe that correlations > 0.7 are between a few mRNAand some Proteins, whereas the majority of strong (negative) correlations are observed between miRNA and mRNAor Proteins. The lines indicating the average expression levels per breast cancer subtype indicate that the selected features are able to discriminate the sample groups.
networkRelevance networks, which are also built on the similarity matrix, can also visualise the correlations between the different types of variables. Each colour represents a type of variable. A threshold can also be set using the argument cutoff (Figure 44). By default the network includes only variables selected on component 1, unless specified in comp.
Note that sometimes the output may not show with Rstudio due to margin issues. We can either use X11() to open a new window, or save the plot as an image using the arguments save and name.save, as we show below. An interactive argument is also available for the cutoff argument, see details in ?network.
# X11() # Opens a new window
network(diablo.tcga, blocks = c(1,2,3),
cutoff = 0.4,
color.node = c('darkorchid', 'brown1', 'lightgreen'),
# To save the plot, uncomment below line
#save = 'png', name.save = 'diablo-network'
)
Figure 44: Relevance network for the variables selected by multiblock sPLS-DA performed on the breast.TCGA study on component 1. Each node represents a selected variable with colours indicating their type. The colour of the edges represent positive or negative correlations. Further tweaking of this plot can be obtained, see the help file ?network.
The relevance network in Figure 44 shows two groups of features of different types. Within each group we observe positive and negative correlations. The visualisation of this plot could be further improved by changing the names of the original features.
Note that the network can be saved in a .gml format to be input into the software Cytoscape, using the R package igraph (Csardi et al. 2006):
# Not run
library(igraph)
myNetwork <- network(diablo.tcga, blocks = c(1,2,3), cutoff = 0.4)
write.graph(myNetwork$gR, file = "myNetwork.gml", format = "gml")
plotLoadingsplotLoadings() visualises the loading weights of each selected variable on each component and each data set. The colour indicates the class in which the variable has the maximum level of expression (contrib = 'max') or minimum (contrib = 'min'), on average (method = 'mean') or using the median (method = 'median').
plotLoadings(diablo.tcga, comp = 1, contrib = 'max', method = 'median')
breast.TCGA study on component 1. The most important variables (according to the absolute value of their coefficients) are ordered from bottom to top. As this is a supervised analysis, colours indicate the class for which the median expression value is the highest for each feature (variables selected characterise Basal and LumA)." width="75%" />
Figure 45: Loading plot for the variables selected by multiblock sPLS-DA performed on the breast.TCGA study on component 1. The most important variables (according to the absolute value of their coefficients) are ordered from bottom to top. As this is a supervised analysis, colours indicate the class for which the median expression value is the highest for each feature (variables selected characterise Basal and LumA).
The loading plot shows the multi-omics signature selected on component 1, where each panel represents one data type. The importance of each variable is visualised by the length of the bar (i.e. its loading coefficient value). The combination of the sign of the coefficient (positive / negative) and the colours indicate that component 1 discriminates primarily the Basal samples vs. the LumA samples (see the sample plots also). The features selected are highly expressed in one of these two subtypes. One could also plot the second component that discriminates the Her2 samples.
cimDiabloThe cimDiablo() function is a clustered image map specifically implemented to represent the multi-omics molecular signature expression for each sample. It is very similar to a classical hierarchical clustering (Figure 46).
cimDiablo(diablo.tcga, color.blocks = c('darkorchid', 'brown1', 'lightgreen'),
comp = 1, margin=c(8,20), legend.position = "right")
##
## trimming values to [-3, 3] range for cim visualisation. See 'trim' arg in ?cimDiablo
Figure 46: Clustered Image Map for the variables selected by multiblock sPLS-DA performed on the breast.TCGA study on component 1. By default, Euclidean distance and Complete linkage methods are used. The CIM represents samples in rows (indicated by their breast cancer subtype on the left hand side of the plot) and selected features in columns (indicated by their data type at the top of the plot).
According to the CIM, component 1 seems to primarily classify the Basal samples, with a group of overexpressed miRNA and underexpressed mRNAand proteins. A group of LumA samples can also be identified due to the overexpression of the same mRNAand proteins. Her2 samples remain quite mixed with the other LumA samples.
We assess the performance of the model using 10-fold cross-validation repeated 10 times with the function perf(). The method runs a block.splsda() model on the pre-specified arguments input from our final object diablo.tcga but on cross-validated samples. We then assess the accuracy of the prediction on the left out samples. Since the tune() function was used with the centroid.dist argument, we examine the outputs of the perf() function for that same distance:
set.seed(123) # For reproducibility with this handbook, remove otherwise
perf.diablo.tcga <- perf(diablo.tcga, validation = 'Mfold', folds = 10,
nrepeat = 10, dist = 'centroids.dist')
#perf.diablo.tcga # Lists the different outputs
We can extract the (balanced) classification error rates globally or overall with
perf.diablo.tcga$error.rate.per.class, the predicted components associated to \(\boldsymbol Y\), or the stability of the selected features with perf.diablo.tcga$features.
Here we look at the different performance assessment schemes specific to multiple data set integration.
First, we output the performance with the majority vote, that is, since the prediction is based on the components associated to their own data set, we can then weight those predictions across data sets according to a majority vote scheme. Based on the predicted classes, we then extract the classification error rate per class and per component:
# Performance with Majority vote
perf.diablo.tcga$MajorityVote.error.rate
## $centroids.dist
## comp1 comp2
## Basal 0.03555556 0.048888889
## Her2 0.23333333 0.130000000
## LumA 0.05466667 0.005333333
## Overall.ER 0.08466667 0.043333333
## Overall.BER 0.10785185 0.061407407
The output shows that with the exception of the Basal samples, the classification improves with the addition of the second component.
Another prediction scheme is to weight the classification error rate from each data set according to the correlation between the predicted components and the \(\boldsymbol Y\) outcome.
# Performance with Weighted vote
perf.diablo.tcga$WeightedVote.error.rate
## $centroids.dist
## comp1 comp2
## Basal 0.01555556 0.044444444
## Her2 0.16666667 0.120000000
## LumA 0.05466667 0.005333333
## Overall.ER 0.06533333 0.040000000
## Overall.BER 0.07896296 0.056592593
Compared to the previous majority vote output, we can see that the classification accuracy is slightly better on component 2 for the subtype Her2.
An AUC plot per block is plotted using the function auroc(). We have already mentioned in Module 3 for PLS-DA, the interpretation of this output may not be particularly insightful in relation to the performance evaluation of our methods, but can complement the statistical analysis. For example, here for the miRNA data set once we have reached component 2 (Figure 47):
auc.diablo.tcga <- auroc(diablo.tcga, roc.block = "miRNA", roc.comp = 2,
print = FALSE)
Figure 47: ROC and AUC based on multiblock sPLS-DA performed on the breast.TCGA study for the miRNA data set after 2 components. The function calculates the ROC curve and AUC for one class vs. the others. If we set print = TRUE, the Wilcoxon test p-value that assesses the differences between the predicted components from one class vs. the others is output.
Figure 47 shows that the Her2 subtype is the most difficult to classify with multiblock sPLS-DA compared to the other subtypes.
The predict() function associated with a block.splsda() object predicts the class of samples from an external test set. In our specific case, one data set is missing in the test set but the method can still be applied. We need to ensure the names of the blocks correspond exactly to those from the training set:
# Prepare test set data: here one block (proteins) is missing
data.test.tcga <- list(mRNA = breast.TCGA$data.test$mrna,
miRNA = breast.TCGA$data.test$mirna)
predict.diablo.tcga <- predict(diablo.tcga, newdata = data.test.tcga)
# The warning message will inform us that one block is missing
#predict.diablo # List the different outputs
The following output is a confusion matrix that compares the real subtypes with the predicted subtypes for a 2 component model, for the distance of interest centroids.dist and the prediction scheme WeightedVote:
confusion.mat.tcga <- get.confusion_matrix(truth = breast.TCGA$data.test$subtype,
predicted = predict.diablo.tcga$WeightedVote$centroids.dist[,2])
confusion.mat.tcga
## predicted.as.Basal predicted.as.Her2 predicted.as.LumA
## Basal 20 1 0
## Her2 0 13 1
## LumA 0 3 32
From this table, we see that one Basal and one Her2 sample are wrongly predicted as Her2 and Lum A respectively, and 3 LumA samples are wrongly predicted as Her2. The balanced prediction error rate can be obtained as:
get.BER(confusion.mat.tcga)
## [1] 0.06825397
It would be worthwhile at this stage to revisit the chosen design of the multiblock sPLS-DA model to assess the influence of the design on the prediction performance on this test set - even though this back and forth analysis is a biased criterion to choose the design!
We integrate four transcriptomics studies of microarray stem cells (125 samples in total). The original data set from the Stemformatics database1 www.stemformatics.org (Wells et al. 2013) was reduced to fit into the package, and includes a randomly-chosen subset of the expression levels of 400 genes. The aim is to classify three types of human cells: human fibroblasts (Fib) and human induced Pluripotent Stem Cells (hiPSC & hESC).
There is a biological hierarchy among the three cell types. On one hand, differences between pluripotent (hiPSC and hESC) and non-pluripotent cells (Fib) are well-characterised and are expected to contribute to the main biological variation. On the other hand, hiPSC are genetically reprogrammed to behave like hESC and both cell types are commonly assumed to be alike. However, differences have been reported in the literature (Chin et al. (2009), Newman and Cooper (2010)). We illustrate the use of MINT to address sub-classification problems in a single analysis.
We first load the data from the package and set up the categorical outcome \(\boldsymbol Y\) and the study membership:
library(mixOmics)
data(stemcells)
# The combined data set X
X <- stemcells$gene
dim(X)
## [1] 125 400
# The outcome vector Y:
Y <- stemcells$celltype
length(Y)
## [1] 125
summary(Y)
## Fibroblast hESC hiPSC
## 30 37 58
We then store the vector indicating the sample membership of each independent study:
study <- stemcells$study
# Number of samples per study:
summary(study)
## 1 2 3 4
## 38 51 21 15
# Experimental design
table(Y,study)
## study
## Y 1 2 3 4
## Fibroblast 6 18 3 3
## hESC 20 3 8 6
## hiPSC 12 30 10 6
We first perform a MINT PLS-DA with all variables included in the model and ncomp = 5 components. The perf() function is used to estimate the performance of the model using LOGOCV, and to choose the optimal number of components for our final model (see Fig 48).
mint.plsda.stem <- mint.plsda(X = X, Y = Y, study = study, ncomp = 5)
set.seed(2543) # For reproducible results here, remove for your own analyses
perf.mint.plsda.stem <- perf(mint.plsda.stem)
plot(perf.mint.plsda.stem)
Figure 48: Choosing the number of components in mint.plsda using perf() with LOGOCV in the stemcells study. Classification error rates (overall and balanced, see Module 2) are represented on the y-axis with respect to the number of components on the x-axis for each prediction distance (see Module 3 and Extra Reading material ‘PLS-DA appendix’). The plot shows that the error rate reaches a minimum from 1 component with the BER and centroids distance.
Based on the performance plot (Figure 48), ncomp = 2 seems to achieve the best performance for the centroid distance, and ncomp = 1 for the Mahalanobis distance in terms of BER. Additional numerical outputs such as the BER and overall error rates per component, and the error rates per class and per prediction distance, can be output:
perf.mint.plsda.stem$global.error$BER
# Type also:
# perf.mint.plsda.stem$global.error
## max.dist centroids.dist mahalanobis.dist
## comp1 0.3803556 0.3333333 0.3333333
## comp2 0.3519556 0.3320000 0.3725111
## comp3 0.3499556 0.3384000 0.3232889
## comp4 0.3541111 0.3427778 0.3898000
## comp5 0.3353778 0.3268667 0.3097111
While we may want to focus our interpretation on the first component, we run a final MINT PLS-DA model for ncomp = 2 to obtain 2D graphical outputs (Figure 49):
final.mint.plsda.stem <- mint.plsda(X = X, Y = Y, study = study, ncomp = 2)
#final.mint.plsda.stem # Lists the different functions
plotIndiv(final.mint.plsda.stem, legend = TRUE, title = 'MINT PLS-DA',
subtitle = 'stem cell study', ellipse = T)
stemcells gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate the study membership. Component 1 discriminates fibroblast vs. the others, while component 2 discriminates some of the hiPSC vs. hESC." width="75%" />
Figure 49: Sample plot from the MINT PLS-DA performed on the stemcells gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate the study membership. Component 1 discriminates fibroblast vs. the others, while component 2 discriminates some of the hiPSC vs. hESC.
The sample plot (Fig 49) shows that fibroblast are separated on the first component. We observe that while deemed not crucial for an optimal discrimination, the second component seems to help separate hESC and hiPSC further. The effect of study after MINT modelling is not strong.
We can compare this output to a classical PLS-DA to visualise the study effect (Figure 50):
plsda.stem <- plsda(X = X, Y = Y, ncomp = 2)
plotIndiv(plsda.stem, pch = study,
legend = TRUE, title = 'Classic PLS-DA',
legend.title = 'Cell type', legend.title.pch = 'Study')
Figure 50: Sample plot from a classic PLS-DA performed on the stemcells gene expression data that highlights the study effect (indicated by symbols). Samples are projected into the space spanned by the first two components. We still do observe some discrimination between the cell types.
The MINT PLS-DA model shown earlier is built on all 400 genes in \(\boldsymbol X\), many of which may be uninformative to characterise the different classes. Here we aim to identify a small subset of genes that best discriminate the classes.
We can choose the keepX parameter using the tune() function for a MINT object. The function performs LOGOCV for different values of test.keepX provided on each component, and no repeat argument is needed. Based on the mean classification error rate (overall error rate or BER) and a centroids distance, we output the optimal number of variables keepX to be included in the final model.
set.seed(2543) # For a reproducible result here, remove for your own analyses
tune.mint.splsda.stem <- tune(X = X, Y = Y, study = study,
ncomp = 2, test.keepX = seq(1, 100, 1),
method = 'mint.splsda', #Specify the method
measure = 'BER',
dist = "centroids.dist",
nrepeat = 3)
#tune.mint.splsda.stem # Lists the different types of outputs
# Mean error rate per component and per tested keepX value:
#tune.mint.splsda.stem$error.rate[1:5,]
The optimal number of variables to select on each specified component:
tune.mint.splsda.stem$choice.keepX
## comp1 comp2
## 24 1
plot(tune.mint.splsda.stem)
keepX in MINT sPLS-DA performed on the stemcells gene expression data. Each coloured line represents the balanced error rate (y-axis) per component across all tested keepX values (x-axis). The diamond indicates the optimal keepX value on a particular component which achieves the lowest classification error rate as determined with a one-sided \(t-\)test across the studies." width="75%" />
Figure 51: Tuning keepX in MINT sPLS-DA performed on the stemcells gene expression data. Each coloured line represents the balanced error rate (y-axis) per component across all tested keepX values (x-axis). The diamond indicates the optimal keepX value on a particular component which achieves the lowest classification error rate as determined with a one-sided \(t-\)test across the studies.
The tuning plot in Figure 51 indicates the optimal number of variables to select on component 1 (24) and on component 2 (1). In fact, whilst the BER decreases with the addition of component 2, the standard deviation remains large, and thus only one component is optimal. However, the addition of this second component is useful for the graphical outputs, and also to attempt to discriminate the hESC and hiPCS cell types.
Note:
keepX values.Following the tuning results, our final model is as follows (we still choose a model with two components in order to obtain 2D graphics):
final.mint.splsda.stem <- mint.splsda(X = X, Y = Y, study = study, ncomp = 2,
keepX = tune.mint.splsda.stem$choice.keepX)
#mint.splsda.stem.final # Lists useful functions that can be used with a MINT object
The samples can be projected on the global components or alternatively using the partial components from each study (Fig 52).
plotIndiv(final.mint.splsda.stem, study = 'global', legend = TRUE,
title = 'Stem cells, MINT sPLS-DA',
subtitle = 'Global', ellipse = T)
plotIndiv(final.mint.splsda.stem, study = 'all.partial', legend = TRUE,
title = 'Stem cells, MINT sPLS-DA',
subtitle = paste("Study",1:4))
stemcells gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate study membership. (a) Global components from the model with 95% ellipse confidence intervals around each sample class. (b) Partial components per study show a good agreement across studies. Component 1 discriminates fibroblast vs. the rest, component 2 discriminates further hESC vs. hiPSC." width="50%" />stemcells gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate study membership. (a) Global components from the model with 95% ellipse confidence intervals around each sample class. (b) Partial components per study show a good agreement across studies. Component 1 discriminates fibroblast vs. the rest, component 2 discriminates further hESC vs. hiPSC." width="50%" />
Figure 52: Sample plots from the MINT sPLS-DA performed on the stemcells gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate study membership. (a) Global components from the model with 95% ellipse confidence intervals around each sample class. (b) Partial components per study show a good agreement across studies. Component 1 discriminates fibroblast vs. the rest, component 2 discriminates further hESC vs. hiPSC.
The visualisation of the partial components enables us to examine each study individually and check that the model is able to extract a good agreement between studies.
We can examine our molecular signature selected with MINT sPLS-DA. The correlation circle plot, presented in Module 2, highlights the contribution of each selected transcript to each component (close to the large circle), and their correlation (clusters of variables) in Figure 53:
plotVar(final.mint.splsda.stem)
stemcells gene expression data to examine the association of the genes selected on the first two components. We mainly observe two groups of genes, either positively or negatively associated with component 1 along the x-axis. This graphic should be interpreted in conjunction with the sample plot." width="75%" />
Figure 53: Correlation circle plot representing the genes selected by MINT sPLS-DA performed on the stemcells gene expression data to examine the association of the genes selected on the first two components. We mainly observe two groups of genes, either positively or negatively associated with component 1 along the x-axis. This graphic should be interpreted in conjunction with the sample plot.
We observe a subset of genes that are strongly correlated and negatively associated to component 1 (negative values on the x-axis), which are likely to characterise the groups of samples hiPSC and hESC, and a subset of genes positively associated to component 1 that may characterise the fibroblast samples (and are negatively correlated to the previous group of genes).
Note:
var.name argument to show gene name ID, as shown in Module 3 for PLS-DA.The Clustered Image Map represents the expression levels of the gene signature per sample, similar to a PLS-DA object (see Module 3). Here we use the default Euclidean distance and Complete linkage in Figure 54 for a specific component (here 1):
# If facing margin issues, use either X11() or save the plot using the
# arguments save and name.save
cim(final.mint.splsda.stem, comp = 1, margins=c(10,5),
row.sideColors = color.mixo(as.numeric(Y)), row.names = FALSE,
title = "MINT sPLS-DA, component 1")
stemcells gene expression data for component 1 only. A hierarchical clustering based on the gene expression levels of the selected genes on component 1, with samples in rows coloured according to cell type showing a separation of the fibroblast vs. the other cell types." width="75%" />
Figure 54: Clustered Image Map of the genes selected by MINT sPLS-DA on the stemcells gene expression data for component 1 only. A hierarchical clustering based on the gene expression levels of the selected genes on component 1, with samples in rows coloured according to cell type showing a separation of the fibroblast vs. the other cell types.
As expected and observed from the sample plot Figure 52, we observe in the CIM that the expression of the genes selected on component 1 discriminates primarily the fibroblast vs. the other cell types.
Relevance networks can also be plotted for a PLS-DA object, but would only show the association between the selected genes and the cell type (dummy variable in \(\boldsymbol Y\) as an outcome category) as shown in Figure 55. Only the variables selected on component 1 are shown (comp = 1):
# If facing margin issues, use either X11() or save the plot using the
# arguments save and name.save
network(final.mint.splsda.stem, comp = 1,
color.node = c(color.mixo(1), color.mixo(2)),
shape.node = c("rectangle", "circle"))
stemcells gene expression data for component 1 only. Associations between variables from \(\boldsymbol X\) and the dummy matrix \(\boldsymbol Y\) are calculated as detailed in Extra Reading material from Module 2. Edges indicate high or low association between the genes and the different cell types." width="75%" />
Figure 55: Relevance network of the genes selected by MINT sPLS-DA performed on the stemcells gene expression data for component 1 only. Associations between variables from \(\boldsymbol X\) and the dummy matrix \(\boldsymbol Y\) are calculated as detailed in Extra Reading material from Module 2. Edges indicate high or low association between the genes and the different cell types.
The selectVar() function outputs the selected transcripts on the first component along with their loading weight values. We consider variables as important in the model when their absolute loading weight value is high. In addition to this output, we can compare the stability of the selected features across studies using the perf() function, as shown in PLS-DA in Module 3.
# Just a head
head(selectVar(final.mint.plsda.stem, comp = 1)$value)
## value.var
## ENSG00000181449 -0.09764220
## ENSG00000123080 0.09606034
## ENSG00000110721 -0.09595070
## ENSG00000176485 -0.09457383
## ENSG00000184697 -0.09387322
## ENSG00000102935 -0.09370298
The plotLoadings() function displays the coefficient weight of each selected variable in each study and shows the agreement of the gene signature across studies (Figure 56). Colours indicate the class in which the mean expression value of each selected gene is maximal. For component 1, we obtain:
plotLoadings(final.mint.splsda.stem, contrib = "max", method = 'mean', comp=1,
study="all.partial", title="Contribution on comp 1",
subtitle = paste("Study",1:4))
stemcells data, on component 1 per study. Each plot represents one study, and the variables are coloured according to the cell type they are maximally expressed in, on average. The length of the bars indicate the loading coefficient values that define the component. Several genes distinguish between fibroblast and the other cell types, and are consistently overexpressed in these samples across all studies. We observe slightly more variability in whether the expression levels of the other genes are more indicative of hiPSC or hESC cell types." width="75%" />stemcells data, on component 1 per study. Each plot represents one study, and the variables are coloured according to the cell type they are maximally expressed in, on average. The length of the bars indicate the loading coefficient values that define the component. Several genes distinguish between fibroblast and the other cell types, and are consistently overexpressed in these samples across all studies. We observe slightly more variability in whether the expression levels of the other genes are more indicative of hiPSC or hESC cell types." width="75%" />
Figure 56: Loading plots of the genes selected by the MINT sPLS-DA performed on the stemcells data, on component 1 per study. Each plot represents one study, and the variables are coloured according to the cell type they are maximally expressed in, on average. The length of the bars indicate the loading coefficient values that define the component. Several genes distinguish between fibroblast and the other cell types, and are consistently overexpressed in these samples across all studies. We observe slightly more variability in whether the expression levels of the other genes are more indicative of hiPSC or hESC cell types.
Several genes are consistently over-expressed on average in the fibroblast samples in each of the studies, however, we observe a less consistent pattern for the other genes that characterise hiPSC} and hESC. This can be explained as the discrimination between both classes is challenging on component 1 (see sample plot in Figure 52).
We assess the performance of the MINT sPLS-DA model with the perf() function. Since the previous tuning was conducted with the distance centroids.dist, the same distance is used to assess the performance of the final model. We do not need to specify the argument nrepeat as we use LOGOCV in the function.
set.seed(123) # For reproducible results here, remove for your own study
perf.mint.splsda.stem.final <- perf(final.mint.plsda.stem, dist = 'centroids.dist')
perf.mint.splsda.stem.final$global.error
## $BER
## centroids.dist
## comp1 0.3333333
## comp2 0.3320000
##
## $overall
## centroids.dist
## comp1 0.456
## comp2 0.392
##
## $error.rate.class
## $error.rate.class$centroids.dist
## comp1 comp2
## Fibroblast 0.0000000 0.0000000
## hESC 0.1891892 0.4594595
## hiPSC 0.8620690 0.5517241
The classification error rate per class is particularly insightful to understand which cell types are difficult to classify, hESC and hiPS - whose mixture can be explained for biological reasons.
An AUC plot for the integrated data can be obtained using the function auroc() (Fig 57).
Remember that the AUC incorporates measures of sensitivity and specificity for every possible cut-off of the predicted dummy variables. However, our PLS-based models rely on prediction distances, which can be seen as a determined optimal cut-off. Therefore, the ROC and AUC criteria may not be particularly insightful in relation to the performance evaluation of our supervised multivariate methods, but can complement the statistical analysis (from Rohart, Gautier, Singh, and Lê Cao (2017)).
auroc(final.mint.splsda.stem, roc.comp = 1)
We can also obtain an AUC plot per study by specifying the argument roc.study:
auroc(final.mint.splsda.stem, roc.comp = 1, roc.study = '2')
stemcells gene expression data for global and specific studies, averaged across one-vs-all comparisons. Numerical outputs include the AUC and a Wilcoxon test \(p-\)value for each ‘one vs. other’ class comparison that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the selected features are more accurate in classifying fibroblasts versus the other cell types, and less accurate in distinguishing hESC versus the other cell types or hiPSC versus the other cell types." width="50%" />stemcells gene expression data for global and specific studies, averaged across one-vs-all comparisons. Numerical outputs include the AUC and a Wilcoxon test \(p-\)value for each ‘one vs. other’ class comparison that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the selected features are more accurate in classifying fibroblasts versus the other cell types, and less accurate in distinguishing hESC versus the other cell types or hiPSC versus the other cell types." width="50%" />
Figure 57: ROC curve and AUC from the MINT sPLS-DA performed on the stemcells gene expression data for global and specific studies, averaged across one-vs-all comparisons. Numerical outputs include the AUC and a Wilcoxon test \(p-\)value for each ‘one vs. other’ class comparison that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the selected features are more accurate in classifying fibroblasts versus the other cell types, and less accurate in distinguishing hESC versus the other cell types or hiPSC versus the other cell types.
We use the predict() function to predict the class membership of new test samples from an external study. We provide an example where we set aside a particular study, train the MINT model on the remaining three studies, then predict on the test study. This process exactly reflects the inner workings of the tune() and perf() functions using LOGOCV.
Here during our model training on the three studies only, we assume we have performed the tuning steps described in this case study to choose ncomp and keepX (here set to arbitrary values to avoid overfitting):
# We predict on study 3
indiv.test <- which(study == "3")
# We train on the remaining studies, with pre-tuned parameters
mint.splsda.stem2 <- mint.splsda(X = X[-c(indiv.test), ],
Y = Y[-c(indiv.test)],
study = droplevels(study[-c(indiv.test)]),
ncomp = 1,
keepX = 30)
mint.predict.stem <- predict(mint.splsda.stem2, newdata = X[indiv.test, ],
dist = "centroids.dist",
study.test = factor(study[indiv.test]))
# Store class prediction with a model with 1 comp
indiv.prediction <- mint.predict.stem$class$centroids.dist[, 1]
# The confusion matrix compares the real subtypes with the predicted subtypes
conf.mat <- get.confusion_matrix(truth = Y[indiv.test],
predicted = indiv.prediction)
conf.mat
## predicted.as.Fibroblast predicted.as.hESC predicted.as.hiPSC
## Fibroblast 3 0 0
## hESC 0 4 4
## hiPSC 2 2 6
Here we have considered a trained model with one component, and compared the cell type prediction for the test study 3 with the known cell types. The classification error rate is relatively high, but potentially could be improved with a proper tuning, and a larger number of studies in the training set.
# Prediction error rate
(sum(conf.mat) - sum(diag(conf.mat)))/sum(conf.mat)
## [1] 0.3809524
## [1] '6.37.0'
## R version 4.6.0 Patched (2026-05-01 r89994)
## Platform: aarch64-apple-darwin23
## Running under: macOS Tahoe 26.3.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] mixOmics_6.37.0 ggplot2_4.0.3 lattice_0.22-9
## [4] MASS_7.3-65 kableExtra_1.4.0 knitr_1.51
## [7] BiocParallel_1.47.0 BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 ellipse_0.5.0 xfun_0.57
## [4] bslib_0.11.0 ggrepel_0.9.8 vctrs_0.7.3
## [7] tools_4.6.0 generics_0.1.4 parallel_4.6.0
## [10] tibble_3.3.1 rARPACK_0.11-0 pkgconfig_2.0.3
## [13] Matrix_1.7-5 RColorBrewer_1.1-3 S7_0.2.2
## [16] lifecycle_1.0.5 compiler_4.6.0 farver_2.1.2
## [19] stringr_1.6.0 textshaping_1.0.5 tinytex_0.59
## [22] codetools_0.2-20 htmltools_0.5.9 snow_0.4-4
## [25] sass_0.4.10 yaml_2.3.12 pillar_1.11.1
## [28] jquerylib_0.1.4 tidyr_1.3.2 cachem_1.1.0
## [31] magick_2.9.1 RSpectra_0.16-2 tidyselect_1.2.1
## [34] digest_0.6.39 stringi_1.8.7 dplyr_1.2.1
## [37] reshape2_1.4.5 purrr_1.2.2 bookdown_0.46
## [40] labeling_0.4.3 fastmap_1.2.0 grid_4.6.0
## [43] cli_3.6.6 magrittr_2.0.5 dichromat_2.0-0.1
## [46] corpcor_1.6.10 withr_3.0.2 scales_1.4.0
## [49] rmarkdown_2.31 matrixStats_1.5.0 igraph_2.3.1
## [52] otel_0.2.0 gridExtra_2.3 evaluate_1.0.5
## [55] viridisLite_0.4.3 rlang_1.2.0 Rcpp_1.1.1-1.1
## [58] glue_1.8.1 BiocManager_1.30.27 xml2_1.5.2
## [61] svglite_2.2.2 rstudioapi_0.18.0 jsonlite_2.0.0
## [64] R6_2.6.1 plyr_1.8.9 systemfonts_1.3.2