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"
## [3] "CD3(Cd112)Di" "CD235-61-7-15(In113)Di"
## [5] "CD3(Cd114)Di" "CD45(In115)Di"
## [7] "CD19(Nd142)Di" "CD22(Nd143)Di"
## [9] "IgD(Nd145)Di" "CD79b(Nd146)Di"
## [11] "CD20(Sm147)Di" "CD34(Nd148)Di"
## [13] "CD179a(Sm149)Di" "CD72(Eu151)Di"
## [15] "IgM(Eu153)Di" "Kappa(Sm154)Di"
## [17] "CD10(Gd156)Di" "Lambda(Gd157)Di"
## [19] "CD24(Dy161)Di" "TdT(Dy163)Di"
## [21] "Rag1(Dy164)Di" "PreBCR(Ho165)Di"
## [23] "CD43(Er167)Di" "CD38(Er168)Di"
## [25] "CD40(Er170)Di" "CD33(Yb173)Di"
## [27] "HLA-DR(Yb174)Di"
##
## $functional
## [1] "pCrkL(Lu175)Di" "pCREB(Yb176)Di" "pBTK(Yb171)Di"
## [4] "pS6(Yb172)Di" "cPARP(La139)Di" "pPLCg2(Pr141)Di"
## [7] "pSrc(Nd144)Di" "Ki67(Sm152)Di" "pErk12(Gd155)Di"
## [10] "pSTAT3(Gd158)Di" "pAKT(Tb159)Di" "pBLNK(Gd160)Di"
## [13] "pP38(Tm169)Di" "pSTAT5(Nd150)Di" "pSyk(Dy162)Di"
## [16] "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] 703 615 663 737 881 934 499 760 546 561 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 703 369 386 245 734 752 469 602 160 845
## [2,] 615 751 255 164 725 362 994 237 583 815
## [3,] 663 956 935 821 667 680 89 674 859 246
## [4,] 737 912 640 144 101 74 344 204 748 577
## [5,] 881 703 33 333 757 27 752 368 791 283
## [6,] 934 762 766 12 933 342 9 868 906 650
## [7,] 499 753 399 42 757 783 243 482 108 65
## [8,] 760 604 737 898 748 302 891 344 908 316
## [9,] 546 906 529 549 211 853 12 762 947 537
## [10,] 561 254 611 444 735 58 819 760 803 72
## [11,] 154 600 729 588 476 836 575 275 623 930
## [12,] 537 9 455 853 75 440 549 601 19 95
## [13,] 496 617 814 569 343 164 579 994 423 182
## [14,] 530 917 855 552 749 559 191 887 522 949
## [15,] 129 476 53 530 729 787 943 365 528 623
## [16,] 352 787 750 26 616 338 530 45 168 365
## [17,] 638 779 716 974 636 682 306 224 217 742
## [18,] 241 201 239 596 227 784 52 209 630 41
## [19,] 314 529 906 989 12 9 918 217 131 651
## [20,] 413 930 530 938 686 441 600 352 928 750# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 2.88 3.27 4.35 3.21 2.81 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 2.883953 2.920141 2.964791 3.026797 3.053570 3.135941 3.180808
## [2,] 3.268422 3.517126 4.102763 4.228391 4.231963 4.313544 4.327914
## [3,] 4.346599 4.442967 4.848014 4.920839 4.969283 4.970772 5.004522
## [4,] 3.205816 3.242520 3.475945 3.640327 3.876835 3.881543 3.912304
## [5,] 2.814795 2.852736 2.911103 2.932717 2.973127 3.050343 3.099217
## [6,] 4.165912 4.208693 4.226428 4.310194 4.391947 4.395456 4.474153
## [7,] 3.090906 3.807217 4.047665 4.233044 4.238328 4.270631 4.530476
## [8,] 3.238448 3.468780 3.518805 3.531683 3.582698 3.617614 3.654889
## [9,] 2.612928 2.724873 2.775316 2.928508 2.941141 3.009110 3.016592
## [10,] 3.653285 3.700107 3.837157 3.911224 3.917315 3.941473 4.033796
## [11,] 4.482201 4.543668 4.698699 4.868759 4.967538 5.178057 5.335642
## [12,] 3.001880 3.016592 3.118466 3.181838 3.219256 3.226009 3.356572
## [13,] 4.164679 4.372719 4.526836 4.548545 4.567160 4.636970 4.660180
## [14,] 3.600701 3.861832 3.884845 4.061345 4.131129 4.139887 4.155880
## [15,] 3.645440 4.392323 4.398688 4.610302 4.723101 4.802364 4.891938
## [16,] 3.849542 4.028213 4.078992 4.159745 4.359839 4.386872 4.387797
## [17,] 3.085945 3.112783 3.156877 3.169237 3.244668 3.254884 3.265306
## [18,] 3.045342 3.272169 3.592151 3.780027 4.020970 4.132393 4.143439
## [19,] 3.267832 3.276624 3.277552 3.369748 3.433823 3.537103 3.597653
## [20,] 3.339597 3.643471 3.666349 3.708491 3.728559 3.969359 4.005788
## [,8] [,9] [,10]
## [1,] 3.225316 3.290506 3.330900
## [2,] 4.332077 4.356062 4.446324
## [3,] 5.039408 5.043362 5.061510
## [4,] 3.947851 3.961548 3.975931
## [5,] 3.198756 3.246064 3.297719
## [6,] 4.499441 4.540173 4.549172
## [7,] 4.592534 4.602821 4.623483
## [8,] 3.776265 3.807573 3.856978
## [9,] 3.046709 3.180061 3.280049
## [10,] 4.205665 4.257668 4.281344
## [11,] 5.465106 5.488455 5.499684
## [12,] 3.376996 3.433823 3.470655
## [13,] 4.708065 4.742229 4.751128
## [14,] 4.319296 4.369172 4.388089
## [15,] 4.892778 4.940585 4.978660
## [16,] 4.493017 4.494916 4.531708
## [17,] 3.270347 3.271496 3.312614
## [18,] 4.226182 4.280701 4.289129
## [19,] 3.615454 3.643005 3.673685
## [20,] 4.128160 4.183223 4.190485This 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 x 34
## `pCrkL(Lu175)Di… `pCREB(Yb176)Di… `pBTK(Yb171)Di.… `pS6(Yb172)Di.I…
## <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1 0.699
## 2 1 1 1 0.672
## 3 1 1 1 0.545
## 4 1 1 1 0.545
## 5 1 1 1 1
## 6 0.948 1 1 0.845
## 7 1 1 0.999 0.976
## 8 0.948 1 1 0.300
## 9 1 1 1 0.924
## 10 0.978 1 1 0.602
## # … with 990 more rows, and 30 more variables:
## # `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>, `pAKT(Tb159)Di.IL7.qvalue` <dbl>,
## # `pBLNK(Gd160)Di.IL7.qvalue` <dbl>, `pP38(Tm169)Di.IL7.qvalue` <dbl>,
## # `pSTAT5(Nd150)Di.IL7.qvalue` <dbl>, `pSyk(Dy162)Di.IL7.qvalue` <dbl>,
## # `tIkBa(Er166)Di.IL7.qvalue` <dbl>, `pCrkL(Lu175)Di.IL7.change` <dbl>,
## # `pCREB(Yb176)Di.IL7.change` <dbl>, `pBTK(Yb171)Di.IL7.change` <dbl>,
## # `pS6(Yb172)Di.IL7.change` <dbl>, `cPARP(La139)Di.IL7.change` <dbl>,
## # `pPLCg2(Pr141)Di.IL7.change` <dbl>, `pSrc(Nd144)Di.IL7.change` <dbl>,
## # `Ki67(Sm152)Di.IL7.change` <dbl>, `pErk12(Gd155)Di.IL7.change` <dbl>,
## # `pSTAT3(Gd158)Di.IL7.change` <dbl>, `pAKT(Tb159)Di.IL7.change` <dbl>,
## # `pBLNK(Gd160)Di.IL7.change` <dbl>, `pP38(Tm169)Di.IL7.change` <dbl>,
## # `pSTAT5(Nd150)Di.IL7.change` <dbl>, `pSyk(Dy162)Di.IL7.change` <dbl>,
## # `tIkBa(Er166)Di.IL7.change` <dbl>, IL7.fraction.cond.2 <dbl>,
## # density <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 x 51
## `CD3(Cd110)Di` `CD3(Cd111)Di` `CD3(Cd112)Di` `CD235-61-7-15(…
## <dbl> <dbl> <dbl> <dbl>
## 1 0.154 -0.0483 0.259 0.846
## 2 0.523 0.752 0.664 0.820
## 3 -0.0224 -0.156 1.50 0.356
## 4 0.256 -0.104 1.02 -0.537
## 5 -0.168 -0.0518 -0.321 0.161
## 6 0.202 -0.0108 0.381 0.551
## 7 -0.216 -0.157 -0.148 0.322
## 8 0.296 1.00 1.28 -0.0860
## 9 -0.439 -0.422 -0.257 -0.478
## 10 -0.165 1.48 -0.470 -0.545
## # … with 20 more rows, and 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>, `PreBCR(Ho165)Di` <dbl>, `CD43(Er167)Di` <dbl>,
## # `CD38(Er168)Di` <dbl>, `CD40(Er170)Di` <dbl>, `CD33(Yb173)Di` <dbl>,
## # `HLA-DR(Yb174)Di` <dbl>, Time <dbl>, Cell_length <dbl>,
## # `cPARP(La139)Di` <dbl>, `pPLCg2(Pr141)Di` <dbl>,
## # `pSrc(Nd144)Di` <dbl>, `pSTAT5(Nd150)Di` <dbl>, `Ki67(Sm152)Di` <dbl>,
## # `pErk12(Gd155)Di` <dbl>, `pSTAT3(Gd158)Di` <dbl>,
## # `pAKT(Tb159)Di` <dbl>, `pBLNK(Gd160)Di` <dbl>, `pSyk(Dy162)Di` <dbl>,
## # `tIkBa(Er166)Di` <dbl>, `pP38(Tm169)Di` <dbl>, `pBTK(Yb171)Di` <dbl>,
## # `pS6(Yb172)Di` <dbl>, `pCrkL(Lu175)Di` <dbl>, `pCREB(Yb176)Di` <dbl>,
## # `DNA1(Ir191)Di` <dbl>, `DNA2(Ir193)Di` <dbl>,
## # `Viability1(Pt195)Di` <dbl>, `Viability2(Pt196)Di` <dbl>,
## # wanderlust <dbl>, condition <chr># 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] 0.298 0.223 0.194 0.249 0.302 ...