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] 309 873 240 865 638 38 389 980 495 968 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 309 855 887 141 329 395 435 474 685 924
## [2,] 873 317 696 60 690 843 779 27 205 805
## [3,] 240 227 236 976 78 38 107 714 303 671
## [4,] 865 141 27 217 946 330 828 911 518 476
## [5,] 638 455 704 982 441 524 228 388 832 128
## [6,] 38 55 833 730 85 706 261 467 784 270
## [7,] 389 805 188 937 297 102 530 334 713 88
## [8,] 980 38 222 164 726 189 683 593 554 331
## [9,] 495 117 85 839 863 773 554 333 574 137
## [10,] 968 915 456 607 914 417 288 894 776 239
## [11,] 910 111 98 783 630 846 675 592 75 239
## [12,] 442 738 595 294 39 285 470 974 510 754
## [13,] 423 23 512 785 209 595 355 487 39 121
## [14,] 863 270 65 653 117 523 929 784 302 345
## [15,] 638 771 407 128 741 167 455 704 5 578
## [16,] 267 658 729 28 949 221 510 634 311 272
## [17,] 72 159 503 592 58 637 100 910 354 324
## [18,] 718 868 174 616 502 639 422 88 905 873
## [19,] 914 72 968 284 218 585 829 520 327 75
## [20,] 156 27 808 551 977 796 865 651 454 235## num [1:1000, 1:30] 5.39 2.66 2.97 2.94 3 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 5.390803 5.540123 5.818519 6.146132 6.171004 6.216003 6.242732 6.258208
## [2,] 2.663107 3.117694 3.323556 3.331109 3.379523 3.402983 3.403950 3.434218
## [3,] 2.969913 3.207533 3.222807 3.260968 3.370018 3.412866 3.509718 3.582781
## [4,] 2.936871 3.503581 3.589198 3.615391 3.704460 3.812188 3.822302 3.926506
## [5,] 2.999932 3.006798 3.223405 3.246766 3.320653 3.569579 3.603670 3.667501
## [6,] 4.543026 4.678576 4.748999 4.927525 4.938888 5.004810 5.011992 5.024494
## [7,] 3.229654 3.417045 3.446511 3.581236 3.585237 3.628094 3.641761 3.656716
## [8,] 4.383755 4.466796 4.564162 4.650775 4.709518 4.712367 4.720591 4.767499
## [9,] 2.735375 3.050343 3.109112 3.146573 3.148475 3.254967 3.264843 3.304042
## [10,] 3.073766 3.880396 4.017572 4.341929 4.411343 4.716671 4.816199 4.851748
## [11,] 3.839412 3.950076 4.134983 4.188525 4.189104 4.235302 4.290163 4.321174
## [12,] 2.544907 2.904092 3.094208 3.324121 3.436294 3.530716 3.548810 3.584404
## [13,] 3.729572 3.931624 3.936064 3.957039 3.992877 4.012199 4.018576 4.041363
## [14,] 2.623459 2.826313 2.888455 2.963436 3.002221 3.098564 3.178326 3.214041
## [15,] 4.236799 4.238615 4.329188 4.365104 4.452540 4.492300 4.529948 4.571635
## [16,] 3.613840 3.823286 3.837713 3.990173 4.059068 4.132200 4.149736 4.193422
## [17,] 3.925311 4.095008 4.260545 4.467971 4.600207 4.703646 4.705292 4.788767
## [18,] 3.340989 3.596605 3.648404 3.873034 3.889519 3.897943 3.900658 3.986597
## [19,] 4.031811 4.221582 4.282473 4.364293 4.468301 4.484552 4.529442 4.678131
## [20,] 3.214287 3.252302 3.272900 3.287073 3.417971 3.436439 3.514234 3.584064
## [,9] [,10]
## [1,] 6.282054 6.282294
## [2,] 3.461892 3.491080
## [3,] 3.615966 3.618376
## [4,] 3.968345 3.985265
## [5,] 3.671480 3.689459
## [6,] 5.029906 5.037992
## [7,] 3.670929 3.702407
## [8,] 4.915030 4.931211
## [9,] 3.308123 3.347984
## [10,] 4.870489 4.997906
## [11,] 4.350126 4.364901
## [12,] 3.634390 3.636062
## [13,] 4.058339 4.064772
## [14,] 3.221525 3.249002
## [15,] 4.579444 4.590735
## [16,] 4.339484 4.340030
## [17,] 4.827746 4.877181
## [18,] 4.037006 4.103669
## [19,] 4.780792 4.823908
## [20,] 3.624421 3.630796This 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 0.957 1 0.988
## 2 0.890 0.983 0.988
## 3 0.767 1 0.970
## 4 0.908 1 0.988
## 5 0.974 1 0.988
## 6 0.974 1 0.955
## 7 0.832 1 0.988
## 8 0.890 1 1
## 9 0.974 0.832 0.955
## 10 0.974 1 0.988
## # 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 -0.0272 -0.447 1.48 -0.342
## 2 -0.0957 -0.176 -0.199 -0.493
## 3 -0.0161 -1.07 0.552 0.693
## 4 -0.236 -0.0121 -0.0770 0.169
## 5 -0.142 -0.171 -0.0665 0.220
## 6 0.482 -0.348 -0.164 -0.504
## 7 -0.0590 -0.108 -0.269 -0.937
## 8 -0.00887 0.180 -0.0171 0.365
## 9 0.631 -0.470 -0.00925 -0.703
## 10 -0.506 -0.356 -0.299 0.884
## # 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] 0.153 0.278 0.272 0.237 0.269 ...