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] 179 499 675 95 407 361 884 761 801 304 ...
wand.nn[[1]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 179 507 864 963 127 42 631 473 531 852
## [2,] 499 668 421 197 184 485 539 775 571 952
## [3,] 675 175 350 800 449 783 784 188 395 336
## [4,] 95 133 285 730 985 779 823 816 514 156
## [5,] 407 497 297 53 605 768 784 535 512 444
## [6,] 361 884 791 253 674 885 96 343 397 252
## [7,] 884 650 902 951 200 674 856 738 547 896
## [8,] 761 520 11 743 729 60 156 994 334 285
## [9,] 801 444 372 87 279 29 917 209 5 879
## [10,] 304 642 59 858 981 637 646 163 411 906
## [11,] 816 761 514 8 167 743 505 572 740 81
## [12,] 672 602 269 644 954 77 933 810 898 359
## [13,] 275 524 347 970 155 814 56 60 522 776
## [14,] 26 197 896 184 2 166 421 775 571 628
## [15,] 93 122 331 860 418 468 574 192 726 388
## [16,] 921 858 69 551 881 78 59 154 649 304
## [17,] 86 657 891 212 847 147 546 797 760 351
## [18,] 453 916 848 448 280 450 226 993 422 611
## [19,] 516 226 281 841 673 31 418 192 204 174
## [20,] 643 563 440 913 407 313 58 388 495 691
# Distance
str(wand.nn[[2]])
## num [1:1000, 1:30] 3.87 2.7 3.64 3.23 3.22 ...
wand.nn[[2]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.869815 3.961965 4.085290 4.104447 4.281820 4.310497 4.344866 4.551000
## [2,] 2.701264 2.736076 2.932863 2.972517 2.990896 3.003486 3.029786 3.044186
## [3,] 3.642712 3.726866 3.745536 3.764372 3.786412 3.790699 3.790938 3.822202
## [4,] 3.233651 3.259101 3.261762 3.512197 3.546980 3.561774 3.570982 3.609914
## [5,] 3.224171 3.251393 3.292420 3.358817 3.379048 3.396851 3.412648 3.453275
## [6,] 3.104493 3.120017 3.151310 3.259400 3.260959 3.302124 3.329819 3.341544
## [7,] 2.469708 2.565487 2.737319 2.886617 2.981166 2.981517 3.049882 3.090920
## [8,] 3.030203 3.340989 3.581615 3.596605 3.634253 3.648404 3.857570 3.873034
## [9,] 4.105703 4.162334 4.238401 4.272652 4.398573 4.427347 4.464406 4.538040
## [10,] 4.353073 4.734903 4.872277 4.923651 4.966864 4.972235 5.160121 5.237000
## [11,] 3.392868 3.395322 3.492632 3.581615 3.619415 3.708442 3.726359 3.761324
## [12,] 4.181574 4.639681 4.659546 5.078659 5.102001 5.177547 5.242917 5.292716
## [13,] 4.138642 4.227155 4.337072 4.659862 4.781641 4.874225 4.913038 4.914206
## [14,] 3.007084 3.058458 3.125352 3.158027 3.190994 3.192741 3.277275 3.282797
## [15,] 3.071579 3.169094 3.303703 3.317323 3.327155 3.334728 3.339836 3.352330
## [16,] 3.887296 4.332159 4.482201 4.517199 4.820245 4.845608 4.864856 4.936828
## [17,] 3.310888 3.500422 3.510578 3.569947 3.584703 3.650517 3.732448 3.756821
## [18,] 4.224166 4.270625 4.283367 4.288571 4.297067 4.388616 4.408025 4.409965
## [19,] 3.163485 3.320573 3.399586 3.472019 3.478667 3.552670 3.595827 3.617146
## [20,] 4.249507 4.817943 5.131622 5.133870 5.333571 5.379952 5.387872 5.408303
## [,9] [,10]
## [1,] 4.656194 4.690288
## [2,] 3.061991 3.099771
## [3,] 3.829362 3.875307
## [4,] 3.612372 3.705089
## [5,] 3.534975 3.589407
## [6,] 3.362425 3.400968
## [7,] 3.101901 3.102828
## [8,] 3.877377 3.900658
## [9,] 4.551256 4.560387
## [10,] 5.377997 5.463677
## [11,] 3.777349 3.875657
## [12,] 5.329571 5.345661
## [13,] 5.052050 5.072518
## [14,] 3.338076 3.373694
## [15,] 3.407887 3.417671
## [16,] 4.938594 4.953250
## [17,] 3.805591 3.819267
## [18,] 4.417019 4.442876
## [19,] 3.624391 3.676392
## [20,] 5.470608 5.475497
This 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 0.986 0.809 0.914
## 2 0.881 0.524 0.831
## 3 0.888 0.992 0.865
## 4 0.813 0.907 0.831
## 5 0.813 0.953 0.939
## 6 0.857 0.958 0.994
## 7 0.837 0.931 0.988
## 8 0.934 0.859 0.831
## 9 1 0.906 0.831
## 10 1 0.859 0.914
## # 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 -0.0224 -0.156 1.50 0.356
## 2 0.361 0.754 1.73 0.339
## 3 0.399 0.925 0.642 -1.27
## 4 0.753 0.445 0.661 -0.283
## 5 0.349 1.05 2.80 -0.182
## 6 -0.228 0.0463 1.31 -0.387
## 7 -0.186 0.564 0.358 0.764
## 8 0.493 -0.245 1.96 -0.205
## 9 0.0569 1.36 2.55 0.882
## 10 -0.223 0.425 -0.135 0.528
## # 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] 0.207 0.315 0.255 0.26 0.273 ...