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] 767 310 116 123 517 577 658 381 83 153 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 767 846 404 975 730 236 717 958 961 687
## [2,] 310 157 663 648 86 378 994 738 80 248
## [3,] 116 794 718 868 553 447 134 968 267 315
## [4,] 123 950 604 680 516 678 200 426 627 620
## [5,] 517 492 788 484 33 996 214 467 455 98
## [6,] 577 301 987 138 818 413 705 560 813 467
## [7,] 658 519 202 737 16 325 356 455 294 193
## [8,] 381 810 638 730 97 896 82 975 792 41
## [9,] 83 353 557 216 225 970 692 234 354 629
## [10,] 153 55 818 579 396 398 670 390 293 684
## [11,] 826 515 759 948 892 525 217 364 945 175
## [12,] 615 348 546 255 464 381 980 164 541 555
## [13,] 836 344 959 478 784 472 588 871 134 178
## [14,] 504 653 317 673 966 952 794 588 666 783
## [15,] 319 377 416 248 918 566 29 487 105 80
## [16,] 737 432 587 633 202 930 844 579 658 243
## [17,] 22 111 136 85 858 903 214 922 106 996
## [18,] 182 174 398 397 203 342 722 435 752 475
## [19,] 652 94 403 79 873 782 553 287 905 340
## [20,] 291 715 205 822 786 342 596 829 475 837# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 3.6 3.06 2.91 3.2 3.65 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.601931 3.734699 3.794082 3.915970 3.976458 3.996245 4.004307 4.022556
## [2,] 3.061600 3.467129 3.573239 3.947331 3.987906 3.989755 4.044506 4.144787
## [3,] 2.908461 2.996471 3.205213 3.212433 3.272868 3.274541 3.359775 3.377650
## [4,] 3.199075 3.287635 3.425696 3.488692 3.620936 3.629050 3.670389 3.671473
## [5,] 3.654094 3.678958 3.875789 4.023613 4.051438 4.116374 4.195495 4.208327
## [6,] 2.999438 3.090575 3.127478 3.170588 3.220559 3.221523 3.300110 3.377360
## [7,] 2.999932 3.044497 3.169950 3.246766 3.320653 3.473322 3.549419 3.603670
## [8,] 2.556705 2.649796 2.794155 2.800484 2.855793 2.986650 2.989920 3.023547
## [9,] 3.473602 3.696610 3.766708 3.841489 3.845948 3.930391 4.035887 4.048120
## [10,] 3.637631 4.394792 4.591897 4.621630 4.627091 4.651798 4.670793 4.692810
## [11,] 3.079229 3.165821 3.170567 3.178897 3.196916 3.266841 3.269239 3.428236
## [12,] 3.234716 3.426301 3.434113 3.503835 3.611451 3.612683 3.623632 3.629190
## [13,] 3.016322 3.263121 3.277102 3.291503 3.309315 3.330960 3.385717 3.439355
## [14,] 2.893655 3.021908 3.030025 3.263179 3.305017 3.310881 3.352035 3.386877
## [15,] 5.552992 5.652755 5.805457 5.842389 6.078806 6.087200 6.122799 6.191659
## [16,] 2.724922 2.853686 2.872871 2.903003 2.943644 2.960460 3.082562 3.110730
## [17,] 3.121466 3.127203 3.312508 3.445453 3.452956 3.455038 3.459874 3.554004
## [18,] 4.120388 4.148914 4.162334 4.238401 4.272652 4.427347 4.464406 4.558033
## [19,] 3.566107 3.738404 3.778731 3.789020 3.803238 3.818419 3.954976 4.026742
## [20,] 3.576371 4.386751 4.700618 5.209585 5.219471 5.268104 5.365884 5.376842
## [,9] [,10]
## [1,] 4.030236 4.092036
## [2,] 4.165210 4.183539
## [3,] 3.399999 3.431571
## [4,] 3.696871 3.712238
## [5,] 4.229447 4.240194
## [6,] 3.427583 3.435826
## [7,] 3.667501 3.689459
## [8,] 3.083338 3.107908
## [9,] 4.049054 4.067861
## [10,] 4.715257 4.723453
## [11,] 3.434775 3.475361
## [12,] 3.632135 3.651992
## [13,] 3.615325 3.645284
## [14,] 3.500371 3.646421
## [15,] 6.206025 6.371971
## [16,] 3.252051 3.270113
## [17,] 3.555447 3.575888
## [18,] 4.590076 4.590767
## [19,] 4.070396 4.075780
## [20,] 5.541786 5.564380This 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 0.959 0.928 0.888
## 2 0.798 0.939 0.989
## 3 0.928 0.997 0.937
## 4 0.913 0.964 0.937
## 5 0.896 0.928 0.971
## 6 0.905 0.928 0.995
## 7 0.941 0.928 1
## 8 0.898 0.928 1
## 9 0.896 0.965 0.971
## 10 0.999 0.928 0.964
## # 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 -0.180 -0.255 -0.0147 -0.455
## 2 -0.419 -0.166 -0.611 -0.716
## 3 0.197 -0.177 -0.0855 -0.343
## 4 -0.00931 -0.0592 -0.430 0.207
## 5 -0.0663 -0.0284 -0.180 0.385
## 6 -0.133 -0.0245 -0.0975 0.157
## 7 -0.153 -0.217 -0.192 0.753
## 8 0.478 -0.132 0.0318 -0.0733
## 9 -0.0785 -0.00929 -0.00777 -0.802
## 10 0.372 -0.138 0.107 0.194
## # 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] 0.239 0.232 0.282 0.267 0.23 ...