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] 30 561 269 330 81 57 898 935 962 841 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 30 264 499 849 861 120 164 677 933 606
## [2,] 561 889 224 646 525 84 584 110 812 943
## [3,] 269 737 504 642 717 206 794 554 769 373
## [4,] 330 354 252 906 428 952 142 987 394 487
## [5,] 81 84 943 398 274 443 54 740 27 472
## [6,] 57 496 947 349 416 589 495 200 844 444
## [7,] 898 191 734 826 338 387 42 785 279 391
## [8,] 935 580 371 22 850 573 367 636 381 920
## [9,] 962 96 961 335 780 845 753 752 469 667
## [10,] 841 793 861 784 561 247 216 505 824 1
## [11,] 313 643 587 667 335 943 327 372 974 902
## [12,] 895 492 666 279 830 42 898 671 623 191
## [13,] 196 828 911 20 119 904 681 338 121 611
## [14,] 117 836 922 954 326 514 527 236 264 929
## [15,] 744 394 942 969 550 83 169 718 35 29
## [16,] 873 647 850 935 22 494 573 436 661 512
## [17,] 609 482 135 261 933 598 123 146 14 395
## [18,] 925 606 874 594 672 933 425 437 30 325
## [19,] 321 236 138 931 371 742 292 495 211 661
## [20,] 681 830 215 828 196 904 789 506 13 121# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 2.29 3.4 4.36 2.7 3.44 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 2.288550 3.000608 3.003486 3.044186 3.061991 3.109655 3.120947 3.143332
## [2,] 3.399359 3.590860 3.611403 3.614056 3.635727 3.651106 3.738744 3.751228
## [3,] 4.364293 4.453823 4.484552 4.529442 4.531635 4.603279 4.644896 4.653877
## [4,] 2.696517 3.045004 3.228404 3.275969 3.371336 3.466070 3.481313 3.509693
## [5,] 3.438753 3.547274 3.550283 3.574922 3.601996 3.622886 3.663286 3.665880
## [6,] 2.899267 3.228795 3.236289 3.294599 3.349499 3.407082 3.426532 3.497627
## [7,] 4.257666 4.947874 5.131117 5.163658 5.237404 5.323673 5.384779 5.470879
## [8,] 2.917114 3.200399 3.226009 3.557851 3.612108 3.722776 3.786677 4.152940
## [9,] 3.517126 3.827344 4.093881 4.102763 4.109884 4.138264 4.231963 4.243987
## [10,] 3.965962 4.003654 4.026565 4.088930 4.168260 4.186605 4.225033 4.235365
## [11,] 3.715483 3.774560 3.866266 4.166911 4.269376 4.396100 4.525753 4.592158
## [12,] 3.708601 3.884775 4.026843 4.052049 4.073326 4.124767 4.221158 4.266084
## [13,] 3.491925 3.688802 3.958095 4.057822 4.120862 4.163044 4.172630 4.205990
## [14,] 2.242857 2.549984 2.661766 2.669284 2.724573 2.768719 2.903233 3.017756
## [15,] 2.792933 3.177894 3.288704 3.351850 3.411552 3.464850 3.548353 3.592468
## [16,] 2.789617 2.958366 3.075730 3.432619 3.561533 3.631523 3.664861 3.682744
## [17,] 2.962943 3.015275 3.197231 3.294505 3.294976 3.369024 3.433291 3.526737
## [18,] 3.510986 3.640091 3.652262 3.811541 3.929153 3.966727 3.997528 4.029139
## [19,] 2.954442 3.082324 3.125124 3.190342 3.219256 3.324642 3.420620 3.434119
## [20,] 3.176084 3.580175 3.626039 3.633989 3.797314 3.899205 3.950938 3.973965
## [,9] [,10]
## [1,] 3.149825 3.229093
## [2,] 3.808707 3.829575
## [3,] 4.678131 4.780792
## [4,] 3.540702 3.577555
## [5,] 3.692212 3.692374
## [6,] 3.498950 3.589647
## [7,] 5.485570 5.602948
## [8,] 4.155591 4.251814
## [9,] 4.313544 4.327914
## [10,] 4.259835 4.284406
## [11,] 4.668892 4.735616
## [12,] 4.293006 4.490877
## [13,] 4.281796 4.339715
## [14,] 3.140742 3.142932
## [15,] 3.653179 3.676636
## [16,] 3.683244 3.695702
## [17,] 3.555110 3.586548
## [18,] 4.038410 4.058102
## [19,] 3.453518 3.465046
## [20,] 4.057822 4.128345This 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.qval…` `pCREB(Yb176)D…` `pBTK(Yb171)Di…` `pS6(Yb172)Di.…`
## <dbl> <dbl> <dbl> <dbl>
## 1 0.752 0.959 0.581 1
## 2 0.957 0.959 0.758 0.938
## 3 0.715 0.959 0.918 0.951
## 4 1 0.971 0.616 1
## 5 0.926 0.971 0.557 0.938
## 6 0.752 0.935 0.790 0.692
## 7 0.956 0.991 0.980 0.999
## 8 0.960 0.979 0.578 0.781
## 9 0.960 0.877 0.578 0.938
## 10 0.902 0.972 0.803 0.951
## # … 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>, …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(…` `CD3(Cd114)Di`
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -0.147 -0.264 -0.845 -1.25 0.131
## 2 -0.768 -0.580 -0.264 -1.63 -0.386
## 3 -0.00552 -0.187 -0.142 -1.89 0.102
## 4 -0.161 -0.0198 -0.339 -1.10 -0.419
## 5 -0.357 -0.0968 -0.253 -0.233 -0.457
## 6 -0.277 -0.287 -0.636 -0.0210 -0.484
## 7 -0.174 -0.0635 -0.219 -0.776 -0.391
## 8 -0.126 -0.0303 -0.481 -2.32 -0.915
## 9 -0.0964 -0.0378 0.0962 0.134 -0.411
## 10 0.360 -0.113 0.894 -0.315 -0.145
## # … with 20 more rows, and 46 more variables: `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>, …# 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] 0.303 0.255 0.203 0.274 0.262 ...