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] 447 933 291 434 47 693 943 382 100 68 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 447 385 524 152 374 11 465 688 362 294
## [2,] 933 724 19 918 380 859 655 773 149 755
## [3,] 291 100 834 655 149 318 464 928 56 757
## [4,] 434 371 358 555 509 230 730 932 139 801
## [5,] 47 311 729 897 575 969 979 564 242 976
## [6,] 693 220 259 518 986 499 455 906 963 425
## [7,] 943 792 587 723 433 857 358 640 944 198
## [8,] 382 349 849 585 789 918 503 616 865 277
## [9,] 100 291 850 51 834 724 24 655 987 464
## [10,] 68 589 923 461 808 974 611 754 727 78
## [11,] 922 231 872 374 489 492 524 294 82 507
## [12,] 294 141 688 177 238 374 603 869 362 82
## [13,] 926 801 741 27 685 730 545 169 198 139
## [14,] 572 709 563 503 916 117 953 619 113 956
## [15,] 258 266 224 319 953 384 616 935 802 288
## [16,] 503 585 135 288 918 709 293 690 619 616
## [17,] 273 662 726 499 788 984 324 907 195 277
## [18,] 789 274 656 989 321 435 356 298 478 865
## [19,] 452 430 598 658 634 925 966 298 384 802
## [20,] 766 337 102 275 257 308 25 199 458 344# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 3.76 4.33 2.64 3.41 3.07 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.756475 3.789129 3.981918 3.998771 4.028293 4.057891 4.208631 4.298468
## [2,] 4.332648 4.819611 4.841182 4.875379 4.955078 4.993592 5.006686 5.011562
## [3,] 2.636345 2.654513 2.748174 2.772766 2.860018 2.874252 2.972508 3.037446
## [4,] 3.413191 3.810307 3.983984 4.012622 4.103935 4.231706 4.332587 4.341292
## [5,] 3.074132 3.293389 3.435677 3.445301 3.496037 3.528708 3.577640 3.587734
## [6,] 3.181706 3.356851 3.359119 3.477465 3.662189 3.786363 3.906281 3.954731
## [7,] 4.047615 4.070849 4.157514 4.318927 4.394431 4.453658 4.458590 4.520748
## [8,] 2.742485 2.835728 2.875798 2.910265 3.053854 3.116936 3.123300 3.200762
## [9,] 3.289579 3.465452 3.609777 3.621416 3.720741 3.787543 3.793872 3.891145
## [10,] 2.289549 2.674750 2.767685 2.799328 2.817048 2.830405 2.916928 2.946039
## [11,] 2.548350 2.825372 2.887469 3.016062 3.174962 3.228291 3.232466 3.305843
## [12,] 2.853031 3.550295 3.665247 3.715607 3.732783 3.818286 3.821909 3.834746
## [13,] 3.815551 4.117455 4.121812 4.140898 4.259279 4.362753 4.409985 4.416034
## [14,] 3.616236 3.887278 3.927038 4.003654 4.026565 4.160402 4.168260 4.259835
## [15,] 2.905517 3.179290 3.203555 3.237597 3.251187 3.263027 3.274653 3.338310
## [16,] 2.316222 2.645666 2.917214 2.973821 2.978794 3.044055 3.080280 3.113044
## [17,] 3.799120 4.016923 4.100286 4.161794 4.196074 4.341566 4.406037 4.560377
## [18,] 3.740966 3.847246 3.875031 3.930930 4.052366 4.055403 4.063006 4.066817
## [19,] 2.102466 3.246829 3.284013 3.344346 3.348197 3.484666 3.662005 3.678749
## [20,] 2.762653 3.199871 3.268338 3.362720 3.380915 3.545225 3.561651 3.563866
## [,9] [,10]
## [1,] 4.314644 4.370974
## [2,] 5.032813 5.080259
## [3,] 3.059503 3.067768
## [4,] 4.375022 4.376458
## [5,] 3.603934 3.712189
## [6,] 3.974488 4.117223
## [7,] 4.581503 4.635136
## [8,] 3.208651 3.223906
## [9,] 3.892530 3.989745
## [10,] 3.027499 3.107892
## [11,] 3.314668 3.334937
## [12,] 3.846091 3.854649
## [13,] 4.426915 4.440302
## [14,] 4.284406 4.294126
## [15,] 3.363383 3.368632
## [16,] 3.185147 3.232046
## [17,] 4.567713 4.598744
## [18,] 4.067455 4.077304
## [19,] 3.729281 3.771632
## [20,] 3.625486 3.635257This 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 0.997 1 0.911
## 2 1 1 0.666
## 3 0.993 1 0.666
## 4 0.748 1 0.953
## 5 0.873 1 0.905
## 6 0.827 1 0.996
## 7 1 1 0.778
## 8 0.666 1 0.666
## 9 0.927 1 0.891
## 10 0.871 1 0.666
## # 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 -0.402 -0.356 -0.397 -0.883
## 2 -0.0897 -0.411 -0.395 -0.649
## 3 -0.140 -0.291 -0.305 -0.494
## 4 -0.374 -0.343 -0.443 -0.326
## 5 -0.234 -0.0633 -0.104 -0.579
## 6 -0.231 -0.449 -0.894 0.285
## 7 -0.445 -0.289 -0.451 -0.496
## 8 -0.00438 -0.304 -0.248 0.347
## 9 -0.251 -0.150 0.989 -0.00211
## 10 0.504 -0.0952 -0.0422 0.453
## # 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] 0.221 0.194 0.324 0.219 0.265 ...