---
title: "A workflow to study mechanistic indicators for driver gene prediction with Moonlight"
author: "Mona Nourbakhsh^+^ ,
        Astrid Saksager^+^,
        Nikola Tom, 
        Xi Steven Chen,
        Antonio Colaprico,
        Catharina Olsen,
        Matteo Tiberti,
        Elena Papaleo"
subtitle: ^+^ These authors contributed equally to the paper as first authors.

date: "`r Sys.Date()`"

output: 
    BiocStyle::html_document:
        toc: true
        number_sections: false
        toc_depth: 2
        highlight: haddock

vignette: >
  %\VignetteIndexEntry{A workflow to study mechanistic indicators for driver gene prediction with Moonlight}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}

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---
  
```{r setup, include=FALSE}
knitr::opts_chunk$set(dpi = 72)
knitr::opts_chunk$set(cache=FALSE)
```


# Abstract

In order to make light of cancer development, it is crucial to understand which genes play 
a role in the mechanisms linked to this disease and moreover which role that is. Commonly 
biological processes such as proliferation and apoptosis have been linked to cancer progression. 
We have developed the Moonlight framework that allows for prediction of cancer driver genes 
through multi-omics data integration. Based on expression data we perform functional enrichment 
analysis, infer gene regulatory networks and upstream regulator analysis to score the importance 
of well-known biological processes with respect to the studied cancer. We then use these scores 
to predict oncogenic mediators with two specific roles: genes that potentially act as tumor 
suppressor genes (TSGs) and genes that potentially act as oncogenes (OCGs). This constitutes 
Moonlight's primary layer. As gene expression data alone does not explain the cancer phenotypes, 
a second layer of evidence is needed. We have automated the integration of a secondary mutational 
layer that predicts driver mutations in the oncogenic mediators and thereby allows for the 
prediction of cancer driver genes using the driver mutation prediction tool CScape-somatic. 
These new functionalities are provided in the updated version of Moonlight, namely Moonlight2. 
Overall, this methodology not only allows us to identify genes with dual role (TSG in one cancer 
type and OCG in another) but also to elucidate the underlying biological processes. 

# Introduction 

Cancer development is influenced by mutations in two distinctly different categories of genes, 
known as  tumor suppressor genes (TSG) and oncogenes (OCG). The occurrence of mutations in genes 
of the first category leads to faster cell proliferation while mutations in genes of second 
category increases or changes their function.
In 2020, we developed the Moonlight framework that allows for prediction of cancer driver genes 
[@ref26]. Here, gene expression data are integrated together with 
biological processes and gene regulatory networks to score the importance of well-known biological 
processes with respect to the studied cancer. These scores are used to predict oncogenic mediators: 
putative TSGs and putative OCGs. As gene expression data alone is not enough to explain the 
deregulation of the genes, a second layer of evidence is needed. For this reason, we automated the 
integration of a secondary mutational layer which predicts driver mutations and passenger mutations 
in the oncogenic mediators. These new functionalities are released in the updated version of 
Moonlight to produce Moonlight2R. The prediction of the driver mutations are carried out using 
the CScape-somatic driver mutation prediction tool. Moreover, the new functionalities estimate 
the potential effect of a mutation on the transcriptional, translational, or protein 
structure/function level. Those oncogenic mediators with at least one driver mutation are retained
as the final set of driver genes [@ref27]. 

# Moonlight's pipeline

Moonlight's pipeline is shown below:
```{r, fig.width=3, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE}
knitr::include_graphics("Moonlight2_pipeline.gif")
```

# Moonlight's proposed workflow

The proposed pipeline consists of following eight steps:

1. The input to Moonlight is a set of differentially expressed genes between two biological conditions 
such as cancer and healthy samples or two cancer subtyoes. Besides differentially expressed genes,
gene expression and mutation data are also needed. 
2. **FEA** Functional Enrichment Analysis, using Fisher's test, to identify gene sets (with biological 
functions linked to cancer) significantly enriched by regulated genes (RG).
3. **GRN** Gene Regulatory Network inferred between each single DEG (sDEG) and all genes by means of 
mutual information, obtaining for each DEG a list of RG.
4. **URA** Upstream Regulator Analysis for DEGs in each enriched gene set, we applied z-score being 
the ratio between the sum of all predicted effects for all the gene involved in the specific function 
and the square-root of the number of all genes.
5. **PRA** Pattern Recognition Analysis identifies putative TSGs (down) and OCGs (up). We either use 
user defined biological processes or random forests.
6. **DMA** Driver Mutation Analysis identifies driver mutations in the putative TSGs and OCGs through 
the driver mutation prediction tool, CScape-somatic.
7. We applied the above procedure to multiple cancer types to obtain cancer-specific lists of TSGs 
and OCGs. We compared the lists for each cancer: if a sDEG was TSG in a cancer and OCG in another we 
defined it as dual-role TSG-OCG. Otherwise if we found a sDEG defined as OCG or TSG only in one tissue 
we defined it as tissue specific biomarker.
8. We use the COSMIC database to define a list of gold standard TSG and OCGs to assess the accuracy 
of the proposed method.


# Installation

To install Moonlight2R use the code below.

## Installation from BioConductor

```{r, eval = FALSE}
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("Moonlight2R")
```

## Installation from GitHub

First, install `devtools` or if you already have it installed, load it.
```{r, eval = FALSE}
install.packages("devtools")
library(devtools)
```

Install Moonlight2R from GitHub:
```{r, eval = FALSE}
devtools::install_github(repo = "ELELAB/Moonlight2R")
```

## Installation from GitHub with accompanying vignette 

First, install the BiocStyle Bioconductor package.

```{r, eval = FALSE}
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("BiocStyle")
```
Then install Moonlight2R with its accompanying vignette.  

```{r, eval = FALSE}
devtools::install_github(repo = "ELELAB/Moonlight2R", build_vignettes = TRUE)
```

You can view the vignette in the following way.  

```{r, eval = FALSE}
vignette( "Moonlight2R", package="Moonlight2R")
```


# Load libraries
```{r, eval = TRUE}
library(Moonlight2R)
library(magrittr)
library(dplyr)
```


# `Obtain Input`

The input to Moonlight is a set of differentially expressed genes and gene 
expression and mutation data are also needed. Gene expression data, mutation 
data and differentially expressed genes can for example be obtained from TCGA 
using the R package TCGAbiolinks. Help documents on how to use TCGAbiolinks
are available [here](https://bioconductor.org/packages/release/bioc/vignettes/TCGAbiolinks/inst/doc/index.html).
To find other examples of usage of TCGAbiolinks on TCGA cancer types see our
[GitHub repository](https://github.com/ELELAB/LUAD_LUSC_TCGA_comparison).
Example data of the input (differentially expressed genes, gene expression data, 
and mutation data) are stored in the Moonlight2R package:

```{r, eval = TRUE}
data(DEGsmatrix)
data(dataFilt)
data(dataMAF)
data(GEO_TCGAtab)
data(LOC_transcription)
data(LOC_translation)
data(LOC_protein)
data(Oncogenic_mediators_mutation_summary)
data(DEG_Mutations_Annotations)
```


# `Download`: Get GEO data

You can search GEO data using the `getDataGEO` function.

GEO_TCGAtab: a 18x12 matrix that provides the GEO data set we matched to one of the 18 given TCGA cancer types 

```{r, eval = TRUE, echo = TRUE}
knitr::kable(GEO_TCGAtab, digits = 2,
       caption = "Table with GEO data set matched to one
       of the 18 given TCGA cancer types ",
       row.names = TRUE)
```



## `getDataGEO`: Search by cancer type and data type [Gene Expression]
The user can query and download the cancer types supported by GEO, using the function `getDataGEO`:

```{r, eval = TRUE, echo = TRUE, results='hide', warning = FALSE, message = FALSE}
dataFilt_GEO <- getDataGEO(GEOobject = "GSE20347", platform = "GPL571")
```

```{r, eval = TRUE, echo = TRUE, results='hide', warning = FALSE, message = FALSE}
dataFilt_GEO <- getDataGEO(TCGAtumor = "ESCA")
```


## `FEA`: Functional Enrichment Analysis
The user can perform a functional enrichment analysis using the function `FEA`. 
For each DEG in the gene set a z-score is calculated. This score indicates how the genes act in the gene set.

```{r, eval = TRUE, echo = TRUE, results='hide'}
data(DEGsmatrix)
data(DiseaseList)
data(EAGenes)
dataFEA <- FEA(DEGsmatrix = DEGsmatrix)
```

The output can be visualized with a FEA plot. 

## `FEAplot`: Functional Enrichment Analysis Plot
The user can plot the result of a functional enrichment analysis using the function `plotFEA`. 
A negative z-score indicates that the process' activity is decreased.  A positive z-score 
indicates that the process' activity is increased. 

```{r, eval = TRUE, echo = TRUE, message=FALSE, results='hide', warning=FALSE}
plotFEA(dataFEA = dataFEA, additionalFilename = "_exampleVignette", height = 10, width = 20)
```

The figure generated by the above code is shown below:
```{r, fig.width=3, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE}
knitr::include_graphics("FEAplot.gif")
```


## `GRN`: Gene Regulatory Network
The user can perform a gene regulatory network analysis using the function
`GRN` which infers the network using the parmigene package. For illustrative
purposes and to decrease runtime, we have set `nGenesPerm = 5` and `nBoot = 5`
in the example below, however, we recommend setting these parameters to
`nGenesPerm = 2000` and `nBoot = 400` to achieve optimal results, as they are
set by default in the function arguments.

```{r, eval = TRUE}
data(DEGsmatrix)
data(dataFilt)
dataGRN <- GRN(DEGsmatrix = DEGsmatrix, TFs = sample(rownames(DEGsmatrix), 100), normCounts = dataFilt,
	               nGenesPerm = 5, kNearest = 3, nBoot = 5, DiffGenes = TRUE)
```


## `URA`: Upstream Regulator Analysis
The user can perform upstream regulator analysis using the function `URA`. This function is 
applied to each DEG in the enriched gene set and its neighbors in the GRN.

```{r, eval = TRUE, echo = TRUE, results='hide'}
data(dataGRN)
data(DEGsmatrix)
data(DiseaseList)
data(EAGenes)

dataFEA <- FEA(DEGsmatrix = DEGsmatrix)

BPselected <- dataFEA$Diseases.or.Functions.Annotation[1:5]
dataURA <- URA(dataGRN = dataGRN,
               DEGsmatrix = DEGsmatrix,
               BPname = BPselected, 
               nCores=1)
```

## `PRA`: Pattern Regognition Analysis
The user can retrieve TSG/OCG candidates using either selected biological processes or a 
random forest classifier trained on known COSMIC OCGs/TSGs.  

```{r, eval = TRUE}
data(dataURA)
data(tabGrowBlock)
data(knownDriverGenes)
dataPRA <- PRA(dataURA = dataURA,
               BPname = c("apoptosis","proliferation of cells"),
               thres.role = 0)
```

## `DMA`: Driver Mutation Analysis
The user can identify driver mutations with `DMA` in the oncogenic mediators established by `PRA`. 
The passenger or driver status is estimated with CScape-somatic.  
This function will further generate three files: DEG_Mutations_Annotations.rda, 
Oncogenic_mediators_mutation_summary.rda and cscape_somatic_output.rda. These will be placed 
in the specified results-folder. 
The user needs to download two CScape-somatic files in order to run DMA named css_coding.vcf.gz
and css_noncoding.vcf.gz, respectively. These two files can be downloaded at 
http://cscape-somatic.biocompute.org.uk/#download. The corresponding .tbi files (css_coding.vcf.gz.tbi
and css_noncoding.vcf.gz.tbi) must also be downloaded and be placed in the same folder.


```{r, eval = FALSE}
data(dataPRA)
data(dataMAF)
data(DEGsmatrix)
data(LOC_transcription)
data(LOC_translation)
data(LOC_protein)
data(NCG)
data(EncodePromoters)
dataDMA <- DMA(dataMAF = dataMAF,
               dataDEGs = DEGsmatrix, 
               dataPRA = dataPRA,
               results_folder = "DMAresults",
               coding_file = "css_coding.vcf.gz",
               noncoding_file = "css_noncoding.vcf.gz")
```

## `GLS`: Gene Literature Search
The user can perform a literature search on driver genes predicted from `DMA` 
using the `GLS` function. This function takes as input driver genes, 
a query and maximum number of records to retrieve from PubMed. 
Standard PubMed syntax can be used in the query. For example, Boolean 
operators AND, OR, NOT can be applied and tags such as [AU], 
[TITLE/ABSTRACT], [Affiliation] can be used. `GLS` fetches data of 
PubMed records matching the specified query and outputs PubMed IDs
matching the query along with doi, title, abstract, year of publication,
keywords, and total number of PubMed publications. This is done for each 
of the genes supplied in the input.

```{r, eval = TRUE}
data(dataDMA)
genes_query <- Reduce(c, dataDMA)
dataGLS <- GLS(genes = genes_query, 
	       query_string = "AND cancer AND driver",
	       max_records = 20)
head(dataGLS)
``` 


## `Level of consequence`: Effect of mutations on three different levels
The user can investigate the predicted effect of different mutation types on the transcriptional 
level through the table LOC_transcription:

```{r}
knitr::kable(LOC_transcription)
```

The user can investigate the predicted effect of different mutation types on the translational 
level through the table LOC_translation:

```{r}
knitr::kable(LOC_translation)
```

The user can investigate the predicted effect of different mutation types on the protein level 
through the table LOC_protein:
```{r}
knitr::kable(LOC_protein)
```


## `plotNetworkHive`: GRN hive visualization taking into account COSMIC cancer genes

In the following plot the nodes are separated into three groups: known tumor suppressor genes (yellow), 
known oncogenes (green) and the rest (gray).

```{r, eval = TRUE, echo = TRUE, results='hide', warning = FALSE, message = FALSE}
data(knownDriverGenes)
data(dataGRN)
plotNetworkHive(dataGRN, knownDriverGenes, 0.55)
```


## `plotDMA`: Heatmap of the driver/passenger status of mutations in TSGs/OCGs

In the following plot the driver genes with driver mutations are shown.

```{r, eval = TRUE, warning = FALSE, message = FALSE, include=TRUE}
data(dataDMA)
data(DEG_Mutations_Annotations)
data(Oncogenic_mediators_mutation_summary)
plotDMA(DEG_Mutations_Annotations, 
        Oncogenic_mediators_mutation_summary, 
        type = 'complete', additionalFilename = "")
```


```{r, fig.width=3, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE}
knitr::include_graphics("heatmap_complete.gif")
```


## `plotMoonlight`: Heatmap of Moonlight gene z-score for the TSGs/OCGs
In the following plot the top 50 genes with the most driver mutations are visualised.
The values are the moonlight gene z-score for the two biological processes

```{r, eval = TRUE, echo = TRUE, results='hide', warning = FALSE, message = FALSE}
data(DEG_Mutations_Annotations)
data(Oncogenic_mediators_mutation_summary)
data(dataURA_plot)
plotMoonlight(DEG_Mutations_Annotations,
        Oncogenic_mediators_mutation_summary, 
        dataURA_plot, gene_type = "drivers", n = 50)
```

```{r, fig.width=3, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE}
knitr::include_graphics("moonlight_heatmap.gif")
```


# Moonlight Analysis: Case Studies

### Introduction 
This vignette shows a complete workflow of the 'Moonlight2R' package.
The code is divided into three case studies:

* 1. Predicting oncogenic mediators using Moonlight's primary layer 
* 2. Moonlight pipeline in one function
* 3. Moonlight with driver mutation analysis

## Case study n. 1: Predicting oncogenic mediators using Moonlight's primary layer

For illustrative purposes and to decrease runtime, we have set `nGenesPerm = 5`
and `nBoot = 5` in the call of `GRN` in the following code block, however, we
recommend setting these parameters to `nGenesPerm = 2000` and `nBoot = 400` to
achieve optimal results, as they are set by default in the function arguments.

```{r,eval = TRUE,echo=TRUE,message=FALSE,warning=FALSE, results='hide'}
data(DEGsmatrix)
data(dataFilt)
data(DiseaseList)
data(EAGenes)
data(tabGrowBlock)
data(knownDriverGenes)

dataFEA <- FEA(DEGsmatrix = DEGsmatrix)

dataGRN <- GRN(TFs = sample(rownames(DEGsmatrix), 100), 
               DEGsmatrix = DEGsmatrix,
               DiffGenes = TRUE,
               normCounts = dataFilt,
               nGenesPerm = 5,
               nBoot = 5,
	       kNearest = 3)

dataURA <- URA(dataGRN = dataGRN, 
              DEGsmatrix = DEGsmatrix, 
              BPname = c("apoptosis",
                         "proliferation of cells"))

dataDual <- PRA(dataURA = dataURA, 
               BPname = c("apoptosis",
                          "proliferation of cells"),
               thres.role = 0)

oncogenic_mediators <- list("TSG"=names(dataDual$TSG), "OCG"=names(dataDual$OCG))

```


## `plotURA`: Upstream regulatory analysis plot
The user can plot the result of the upstream regulatory analysis using the function `plotURA`.

```{r, eval = TRUE,message=FALSE,warning=FALSE, results='hide'}
data(dataURA) 
plotURA(dataURA = dataURA, additionalFilename = "_exampleVignette")
```

The figure resulted from the code above is shown below:
```{r, fig.width=3, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE}
knitr::include_graphics("URAplot.gif")
```


## Case study n. 2: Moonlight pipeline in one function

For illustrative purposes and to decrease runtime, we have set `nGenesPerm = 5` and `nBoot = 5`
in the example below, however, we recommend setting these parameters to
`nGenesPerm = 2000` and `nBoot = 400` to achieve optimal results, as they are
set by default in the function arguments.

```{r,eval = FALSE,echo=TRUE,message=FALSE,warning=FALSE}
data(dataFilt)
data(DEGsmatrix)
data(dataMAF)
data(DiseaseList)
data(EAGenes)
data(tabGrowBlock)
data(knownDriverGenes)
data(LOC_transcription)
data(LOC_translation)
data(LOC_protein)
data(NCG)
data(EncodePromoters)

listMoonlight <- moonlight(dataDEGs = DEGsmatrix,
                           dataFilt = dataFilt,
                           nTF = 100,
                           DiffGenes = TRUE,
			   nGenesPerm = 5,
                           nBoot = 5,
                           BPname = c("apoptosis","proliferation of cells"),
                           dataMAF = dataMAF,
                           path_cscape_coding = "css_coding.vcf.gz", 
                           path_cscape_noncoding = "css_noncoding.vcf.gz")
save(listMoonlight, file = paste0("listMoonlight_ncancer4.Rdata"))


```

## `plotCircos`: Moonlight Circos Plot
An example of running Moonlight on five cancer types is visualized below in a circos plot.
Outer ring: color by cancer type, Inner ring: OCGs and TSGs, 
Inner connections:  green: common OCGs yellow: common TSGs red: possible dual role

```{r, eval = TRUE, echo = TRUE, results='hide', warning = FALSE, message = FALSE}
data(listMoonlight)
plotCircos(listMoonlight = listMoonlight, additionalFilename = "_ncancer5")
```

The figure generated by the code above is shown below:
```{r, fig.width=3, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE}
knitr::include_graphics("circos_ocg_tsg_ncancer5.gif")
```


## Case study n. 3: Moonlight with driver mutation analysis  

For illustrative purposes and to decrease runtime, we have set `nGenesPerm = 5` and `nBoot = 5`
in the example below, however, we recommend setting these parameters to
`nGenesPerm = 2000` and `nBoot = 400` to achieve optimal results, as they are
set by default in the function arguments.

```{r,eval = FALSE,echo=TRUE,message=FALSE,warning=FALSE}

data(DEGsmatrix)
data(dataFilt)
data(dataMAF)
data(DiseaseList)
data(EAGenes)
data(tabGrowBlock)
data(knownDriverGenes)
data(LOC_transcription)
data(LOC_translation)
data(LOC_protein)
data(NCG)
data(EncodePromoters)

# Perform gene regulatory network analysis
dataGRN <- GRN(TFs = rownames(DEGsmatrix), 
	       DEGsmatrix = DEGsmatrix,
	       DiffGenes = TRUE,
               normCounts = dataFilt,
               nGenesPerm = 5, 
               kNearest = 3, 
               nBoot = 5)

# Perform upstream regulatory analysis
# As example, we use apoptosis and proliferation of cells as the biological processes
dataURA <- URA(dataGRN = dataGRN,
               DEGsmatrix = DEGsmatrix,
               BPname = c("apoptosis", 
                          "proliferation of cells"), 
               nCores = 1)

# Perform pattern recognition analysis
dataPRA <- PRA(dataURA = dataURA,
               BPname = c("apoptosis", 
                          "proliferation of cells"),
               thres.role = 0)

# Perform driver mutation analysis
dataDMA <- DMA(dataMAF = dataMAF,
               dataDEGs = DEGsmatrix, 
               dataPRA = dataPRA,
               results_folder = "DMAresults",
               coding_file = "css_coding.vcf.gz",
               noncoding_file = "css_noncoding.vcf.gz")

```

Next, we analyze the predicted driver genes and their mutations. 

```{r, eval = TRUE}
data(Oncogenic_mediators_mutation_summary)
data(DEG_Mutations_Annotations)

# Extract oncogenic mediators that contain at least one driver mutation
# These are the driver genes
knitr::kable(Oncogenic_mediators_mutation_summary %>%
  filter(!is.na(CScape_Driver)))

# Extract mutation annotations of the predicted driver genes
driver_mut <- DEG_Mutations_Annotations %>% 
  filter(!is.na(Moonlight_Oncogenic_Mediator), 
         CScape_Mut_Class == "Driver")

# Extract driver genes with a predicted effect on the transcriptional level
transcription_mut <- Oncogenic_mediators_mutation_summary %>%
  filter(!is.na(CScape_Driver)) %>%
  filter(Transcription_mut_sum > 0)

# Extract mutation annotations of predicted driver genes that have a driver mutation
# in its promoter region with a potential effect on the transcriptional level 
promoters <- DEG_Mutations_Annotations %>% 
  filter(!is.na(Moonlight_Oncogenic_Mediator), 
         CScape_Mut_Class == "Driver",
         Potential_Effect_on_Transcription == 1,
         Annotation == 'Promoter')
```


## Citation

Please cite the MoonlightR and Moonlight2R packages:

* "Interpreting pathways to discover cancer driver genes with Moonlight." Nature Communications (2020): [10.1038/s41467-019-13803-0](https://doi.org/10.1038/s41467-019-13803-0). [@ref26]

* "A workflow to study mechanistic indicators for driver gene prediction with Moonlight." Briefings in Bioinformatics (2023): [10.1093/bib/bbad274](https://doi.org/10.1093/bib/bbad274). [@ref27]

Related publications to this vignette:

* "TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data." Nucleic acids research (2015): [gkv1507](http://dx.doi.org/doi:10.1093/nar/gkv1507). [@ref25]

* "TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages". F1000Research [10.12688/f1000research.8923.1](http://dx.doi.org/doi:10.12688/f1000research.8923.1) [@ref24]


******

Session Information
******
```{r sessionInfo}
sessionInfo()
```

# References
