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] 426 503 720 404 487 708 175 817 323 772 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 426 750 552 290 603 578 386 637 276 34
## [2,] 503 391 211 60 937 280 835 695 294 315
## [3,] 720 808 185 722 523 348 921 360 802 685
## [4,] 404 441 749 254 137 380 59 840 764 782
## [5,] 487 350 783 953 988 615 143 767 14 217
## [6,] 708 599 885 139 877 974 163 256 827 698
## [7,] 175 895 974 730 469 557 435 363 18 317
## [8,] 817 449 115 80 813 696 25 356 66 348
## [9,] 323 351 51 148 889 527 873 558 528 478
## [10,] 772 280 495 503 397 2 739 60 859 91
## [11,] 424 106 337 924 89 219 978 387 230 711
## [12,] 498 96 54 988 223 918 369 158 123 14
## [13,] 233 315 520 60 40 282 52 94 903 616
## [14,] 858 741 988 177 682 706 350 978 81 204
## [15,] 831 41 613 270 337 988 953 219 858 634
## [16,] 944 629 184 712 122 423 249 272 515 443
## [17,] 325 124 913 832 742 316 266 936 562 680
## [18,] 652 170 393 469 163 974 895 363 435 7
## [19,] 632 190 244 262 210 898 192 968 849 726
## [20,] 891 329 766 691 117 169 481 897 221 615## num [1:1000, 1:30] 3.38 2.52 3.02 4.23 2.99 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.381325 4.146984 4.354256 4.400671 4.407818 4.439486 4.568097 4.669324
## [2,] 2.522069 3.121466 3.127203 3.444082 3.452956 3.453405 3.455038 3.554004
## [3,] 3.016322 3.073767 3.263121 3.309315 3.314453 3.330960 3.385717 3.419556
## [4,] 4.227626 4.448529 4.672951 4.677396 4.736512 4.822613 4.830975 4.850460
## [5,] 2.992707 3.118008 3.148277 3.274980 3.361775 3.372370 3.439153 3.455383
## [6,] 3.819830 3.969982 3.985412 4.032106 4.236799 4.452540 4.492300 4.529948
## [7,] 4.140878 4.198442 4.430188 4.663886 4.703392 4.720072 4.761297 4.803194
## [8,] 3.357628 3.589494 3.644647 3.713032 3.830255 3.874822 3.918866 3.964005
## [9,] 3.344829 3.364253 3.395116 3.411248 3.412474 3.550818 3.589862 3.591775
## [10,] 3.631617 3.734908 3.749448 3.863303 3.864758 3.875702 3.935169 4.018140
## [11,] 3.968356 4.449110 4.477519 4.518838 4.547056 4.579580 4.605214 4.665375
## [12,] 2.616732 3.348047 3.383497 3.397420 3.399174 3.420635 3.532288 3.533299
## [13,] 4.966901 4.999660 5.039738 5.085931 5.087400 5.114662 5.172870 5.205922
## [14,] 2.337340 2.619225 2.633365 2.636770 2.699855 2.752843 2.829769 2.853038
## [15,] 3.690485 3.699995 3.782600 3.800346 3.897406 3.916336 4.009586 4.116667
## [16,] 4.346599 4.600549 4.835635 4.840011 4.889187 4.916895 4.997647 5.025768
## [17,] 3.403529 3.556980 3.914122 4.105836 4.136748 4.220445 4.313055 4.457099
## [18,] 4.090966 4.184798 4.201195 4.335344 4.378699 4.560742 4.626412 4.758725
## [19,] 2.862890 3.043581 3.073014 3.206544 3.244210 3.270410 3.340979 3.483959
## [20,] 2.958130 3.136652 3.205617 3.294737 3.307469 3.312932 3.313815 3.411723
## [,9] [,10]
## [1,] 4.804382 4.900333
## [2,] 3.555447 3.577432
## [3,] 3.428579 3.428636
## [4,] 4.868546 4.928710
## [5,] 3.463760 3.643865
## [6,] 4.559865 4.571635
## [7,] 4.843023 5.123233
## [8,] 3.989260 4.012792
## [9,] 3.599950 3.633145
## [10,] 4.070766 4.073460
## [11,] 4.721702 4.733855
## [12,] 3.599159 3.647355
## [13,] 5.265535 5.289222
## [14,] 2.855092 2.924205
## [15,] 4.152861 4.162815
## [16,] 5.046867 5.106651
## [17,] 4.630311 4.633842
## [18,] 4.765291 4.843023
## [19,] 3.499257 3.557708
## [20,] 3.413683 3.441045This 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 0.912 1 1
## 2 0.984 1 1
## 3 0.986 1 1
## 4 1 1 1
## 5 0.984 1 0.938
## 6 1 1 1
## 7 0.986 1 1
## 8 0.986 1 1
## 9 0.986 1 1
## 10 0.963 1 1
## # 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 -0.0674 0.695 -0.229 0.867
## 2 -0.242 -0.107 -0.0237 0.451
## 3 -0.176 -0.130 -0.221 -0.428
## 4 -0.249 -0.157 -0.275 0.608
## 5 -0.227 -0.219 -0.0323 -0.489
## 6 0.911 0.270 -0.567 -1.66
## 7 -0.392 -0.129 0.311 0.689
## 8 -0.0548 -0.249 -0.118 -0.301
## 9 0.943 -0.0216 -0.0820 -0.741
## 10 -0.194 -0.262 -0.0632 0.868
## # 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] 0.203 0.273 0.275 0.195 0.274 ...