We read in input.scone.csv, which is our file modified (and renamed) from the get.marker.names() function. The K-nearest neighbor generation is derived from the Fast Nearest Neighbors (FNN) R package, within our function Fnn(), which takes as input the “input markers” to be used, along with the concatenated data previously generated, and the desired k. We advise the default selection to the total number of cells in the dataset divided by 100, as has been optimized on existing mass cytometry datasets. The output of this function is a matrix of each cell and the identity of its k-nearest neighbors, in terms of its row number in the dataset used here as input.
library(Sconify)
# Markers from the user-generated excel file
marker.file <- system.file('extdata', 'markers.csv', package = "Sconify")
markers <- ParseMarkers(marker.file)
# How to convert your excel sheet into vector of static and functional markers
markers## $input
## [1] "CD3(Cd110)Di" "CD3(Cd111)Di" "CD3(Cd112)Di"
## [4] "CD235-61-7-15(In113)Di" "CD3(Cd114)Di" "CD45(In115)Di"
## [7] "CD19(Nd142)Di" "CD22(Nd143)Di" "IgD(Nd145)Di"
## [10] "CD79b(Nd146)Di" "CD20(Sm147)Di" "CD34(Nd148)Di"
## [13] "CD179a(Sm149)Di" "CD72(Eu151)Di" "IgM(Eu153)Di"
## [16] "Kappa(Sm154)Di" "CD10(Gd156)Di" "Lambda(Gd157)Di"
## [19] "CD24(Dy161)Di" "TdT(Dy163)Di" "Rag1(Dy164)Di"
## [22] "PreBCR(Ho165)Di" "CD43(Er167)Di" "CD38(Er168)Di"
## [25] "CD40(Er170)Di" "CD33(Yb173)Di" "HLA-DR(Yb174)Di"
##
## $functional
## [1] "pCrkL(Lu175)Di" "pCREB(Yb176)Di" "pBTK(Yb171)Di" "pS6(Yb172)Di"
## [5] "cPARP(La139)Di" "pPLCg2(Pr141)Di" "pSrc(Nd144)Di" "Ki67(Sm152)Di"
## [9] "pErk12(Gd155)Di" "pSTAT3(Gd158)Di" "pAKT(Tb159)Di" "pBLNK(Gd160)Di"
## [13] "pP38(Tm169)Di" "pSTAT5(Nd150)Di" "pSyk(Dy162)Di" "tIkBa(Er166)Di"# Get the particular markers to be used as knn and knn statistics input
input.markers <- markers[[1]]
funct.markers <- markers[[2]]
# Selection of the k. See "Finding Ideal K" vignette
k <- 30
# The built-in scone functions
wand.nn <- Fnn(cell.df = wand.combined, input.markers = input.markers, k = k)
# Cell identity is in rows, k-nearest neighbors are columns
# List of 2 includes the cell identity of each nn,
# and the euclidean distance between
# itself and the cell of interest
# Indices
str(wand.nn[[1]])## int [1:1000, 1:30] 196 148 683 689 20 754 44 573 774 293 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 196 378 235 875 548 311 179 181 149 556
## [2,] 148 189 714 152 795 957 308 562 498 62
## [3,] 683 33 903 837 421 936 359 677 628 927
## [4,] 689 11 639 321 348 203 18 747 749 178
## [5,] 20 802 885 826 356 64 456 533 90 574
## [6,] 754 91 207 542 635 625 192 213 686 716
## [7,] 44 835 450 731 140 652 161 701 420 577
## [8,] 573 398 534 654 802 885 133 795 330 465
## [9,] 774 236 992 539 185 879 625 571 499 426
## [10,] 293 32 275 567 661 436 385 880 433 351
## [11,] 121 547 689 321 203 4 528 749 550 59
## [12,] 225 627 290 241 372 872 503 139 772 887
## [13,] 887 960 276 627 708 772 12 785 872 31
## [14,] 771 259 765 813 797 580 760 558 176 988
## [15,] 644 809 384 325 982 576 187 129 704 697
## [16,] 195 148 314 836 562 391 356 802 882 456
## [17,] 387 181 285 640 736 107 378 23 440 149
## [18,] 626 203 425 747 157 595 973 11 321 110
## [19,] 952 555 876 197 681 196 940 743 514 35
## [20,] 5 802 651 885 546 161 330 177 420 533# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 2.64 2.95 3.15 4.07 2.2 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 2.635267 3.159057 3.313186 3.326059 3.334992 3.368979 3.588107 3.628469
## [2,] 2.954442 3.125124 3.126682 3.219256 3.253785 3.324642 3.453518 3.465046
## [3,] 3.154132 3.677320 3.979203 4.139017 4.191824 4.239740 4.334593 4.367278
## [4,] 4.065915 4.407605 4.990545 5.016599 5.145195 5.320813 5.351538 5.375015
## [5,] 2.197611 2.366281 2.565037 2.573734 2.635397 2.650510 2.855092 2.869122
## [6,] 3.955591 4.365962 4.390887 4.459106 4.617391 4.713135 4.716385 4.831987
## [7,] 2.501066 2.723471 2.826313 2.885968 2.888455 2.951259 3.002221 3.098564
## [8,] 2.737095 2.754134 2.757017 2.811023 2.812476 2.868728 2.881418 2.886851
## [9,] 4.038439 4.161308 4.285868 4.428155 4.560822 4.706743 4.834206 4.844865
## [10,] 2.396558 3.103443 3.259011 3.304500 3.369705 3.482707 3.581698 3.653757
## [11,] 3.390878 4.022779 4.066337 4.142060 4.281859 4.407605 4.483772 4.518206
## [12,] 3.925311 4.078974 4.134332 4.171182 4.189340 4.298179 4.408899 4.422572
## [13,] 4.699835 4.721952 4.955175 4.974715 5.221231 5.298123 5.473469 5.488858
## [14,] 3.986641 4.134249 4.305541 4.367062 4.375356 4.459449 4.502357 4.534870
## [15,] 3.141165 3.314284 3.404386 3.515832 3.533695 3.595742 3.641223 3.645218
## [16,] 3.164105 3.172309 3.392121 3.443828 3.452237 3.534371 3.550348 3.695981
## [17,] 4.090237 4.294670 4.424466 4.469931 4.495317 4.507633 4.531170 4.550135
## [18,] 4.036209 4.131262 4.135165 4.331332 4.381185 4.456790 4.553719 4.737671
## [19,] 2.746469 3.076413 3.145634 3.175229 3.198276 3.291187 3.346875 3.364675
## [20,] 2.197611 2.402989 2.549984 2.638514 2.661766 2.706844 2.724573 2.806825
## [,9] [,10]
## [1,] 3.680224 3.681337
## [2,] 3.514027 3.519632
## [3,] 4.393985 4.411200
## [4,] 5.381195 5.421614
## [5,] 2.885137 2.885680
## [6,] 4.903150 4.935613
## [7,] 3.184604 3.214041
## [8,] 2.889691 2.927191
## [9,] 4.930607 4.974619
## [10,] 3.678302 3.714439
## [11,] 4.525446 4.529158
## [12,] 4.432997 4.464119
## [13,] 5.525911 5.559348
## [14,] 4.822189 4.942998
## [15,] 3.702529 3.704667
## [16,] 3.702298 3.718399
## [17,] 4.564953 4.628969
## [18,] 4.818772 4.855006
## [19,] 3.392853 3.444106
## [20,] 2.895198 2.903233This function iterates through each KNN, and performs a series of calculations. The first is fold change values for each maker per KNN, where the user chooses whether this will be based on medians or means. The second is a statistical test, where the user chooses t test or Mann-Whitney U test. I prefer the latter, because it does not assume any properties of the distributions. Of note, the p values are adjusted for false discovery rate, and therefore are called q values in the output of this function. The user also inputs a threshold parameter (default 0.05), where the fold change values will only be shown if the corresponding statistical test returns a q value below said threshold. Finally, the “multiple.donor.compare” option, if set to TRUE will perform a t test based on the mean per-marker values of each donor. This is to allow the user to make comparisons across replicates or multiple donors if that is relevant to the user’s biological questions. This function returns a matrix of cells by computed values (change and statistical test results, labeled either marker.change or marker.qvalue). This matrix is intermediate, as it gets concatenated with the original input matrix in the post-processing step (see the relevant vignette). We show the code and the output below. See the post-processing vignette, where we show how this gets combined with the input data, and additional analysis is performed.
wand.scone <- SconeValues(nn.matrix = wand.nn,
cell.data = wand.combined,
scone.markers = funct.markers,
unstim = "basal")
wand.scone## # A tibble: 1,000 × 34
## `pCrkL(Lu175)Di.IL7.qvalue` pCREB(Yb176)Di.IL7.qvalu…¹ pBTK(Yb171)Di.IL7.qv…²
## <dbl> <dbl> <dbl>
## 1 0.998 0.856 1
## 2 0.960 0.955 0.992
## 3 0.958 1 0.791
## 4 0.614 0.971 0.828
## 5 0.958 0.983 0.730
## 6 0.858 0.902 0.962
## 7 1 0.861 0.777
## 8 0.998 0.856 0.791
## 9 0.982 0.771 0.966
## 10 0.879 0.771 1
## # ℹ 990 more rows
## # ℹ abbreviated names: ¹`pCREB(Yb176)Di.IL7.qvalue`,
## # ²`pBTK(Yb171)Di.IL7.qvalue`
## # ℹ 31 more variables: `pS6(Yb172)Di.IL7.qvalue` <dbl>,
## # `cPARP(La139)Di.IL7.qvalue` <dbl>, `pPLCg2(Pr141)Di.IL7.qvalue` <dbl>,
## # `pSrc(Nd144)Di.IL7.qvalue` <dbl>, `Ki67(Sm152)Di.IL7.qvalue` <dbl>,
## # `pErk12(Gd155)Di.IL7.qvalue` <dbl>, `pSTAT3(Gd158)Di.IL7.qvalue` <dbl>, …If one wants to export KNN data to perform other statistics not available in this package, then I provide a function that produces a list of each cell identity in the original input data matrix, and a matrix of all cells x features of its KNN.
I also provide a function to find the KNN density estimation independently of the rest of the “scone.values” analysis, to save time if density is all the user wants. With this density estimation, one can perform interesting analysis, ranging from understanding phenotypic density changes along a developmental progression (see post-processing vignette for an example), to trying out density-based binning methods (eg. X-shift). Of note, this density is specifically one divided by the aveage distance to k-nearest neighbors. This specific measure is related to the Shannon Entropy estimate of that point on the manifold (https://hal.archives-ouvertes.fr/hal-01068081/document).
I use this metric to avoid the unusual properties of the volume of a sphere as it increases in dimensions (https://en.wikipedia.org/wiki/Volume_of_an_n-ball). This being said, one can modify this vector to be such a density estimation (example http://www.cs.haifa.ac.il/~rita/ml_course/lectures_old/KNN.pdf), by treating the distance to knn as the radius of a n-dimensional sphere and incoroprating said volume accordingly.
An individual with basic programming skills can iterate through these elements to perform the statistics of one’s choosing. Examples would include per-KNN regression and classification, or feature imputation. The additional functionality is shown below, with the example knn.list in the package being the first ten instances:
# Constructs KNN list, computes KNN density estimation
wand.knn.list <- MakeKnnList(cell.data = wand.combined, nn.matrix = wand.nn)
wand.knn.list[[8]]## # A tibble: 30 × 51
## `CD3(Cd110)Di` `CD3(Cd111)Di` `CD3(Cd112)Di` `CD235-61-7-15(In113)Di`
## <dbl> <dbl> <dbl> <dbl>
## 1 -0.157 -0.168 -0.0231 -0.0425
## 2 -0.0457 -0.215 -0.663 -1.23
## 3 -0.0369 0.745 -0.168 -0.871
## 4 -0.0710 -0.00365 -0.235 -0.892
## 5 -0.0179 -0.0305 -0.663 -0.897
## 6 -0.0160 -0.136 -0.204 -0.392
## 7 -0.458 -0.355 -0.366 0.0316
## 8 -0.121 -0.0918 -0.283 -1.11
## 9 -0.0363 -0.184 -0.0884 0.733
## 10 -0.0403 -0.496 -0.108 -1.01
## # ℹ 20 more rows
## # ℹ 47 more variables: `CD3(Cd114)Di` <dbl>, `CD45(In115)Di` <dbl>,
## # `CD19(Nd142)Di` <dbl>, `CD22(Nd143)Di` <dbl>, `IgD(Nd145)Di` <dbl>,
## # `CD79b(Nd146)Di` <dbl>, `CD20(Sm147)Di` <dbl>, `CD34(Nd148)Di` <dbl>,
## # `CD179a(Sm149)Di` <dbl>, `CD72(Eu151)Di` <dbl>, `IgM(Eu153)Di` <dbl>,
## # `Kappa(Sm154)Di` <dbl>, `CD10(Gd156)Di` <dbl>, `Lambda(Gd157)Di` <dbl>,
## # `CD24(Dy161)Di` <dbl>, `TdT(Dy163)Di` <dbl>, `Rag1(Dy164)Di` <dbl>, …# Finds the KNN density estimation for each cell, ordered by column, in the
# original data matrix
wand.knn.density <- GetKnnDe(nn.matrix = wand.nn)
str(wand.knn.density)## num [1:1000] 0.264 0.276 0.219 0.179 0.336 ...