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] 910 262 168 317 266 474 497 905 321 86 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 910 766 175 112 203 53 635 101 898 656
## [2,] 262 700 246 723 553 693 40 917 538 597
## [3,] 168 321 937 564 693 864 824 976 109 946
## [4,] 317 596 836 284 383 839 972 568 927 25
## [5,] 266 840 577 651 858 44 451 915 850 197
## [6,] 474 729 546 661 279 184 728 481 971 16
## [7,] 497 524 627 522 528 102 650 818 300 125
## [8,] 905 613 941 479 913 795 562 985 58 163
## [9,] 321 3 937 258 96 168 553 247 640 282
## [10,] 86 120 298 467 822 309 881 395 210 986
## [11,] 98 65 766 787 902 238 311 863 119 345
## [12,] 403 979 628 468 549 40 939 725 292 196
## [13,] 718 337 184 587 865 941 464 944 304 985
## [14,] 602 182 565 66 448 845 910 784 176 438
## [15,] 636 878 885 972 807 55 701 839 652 473
## [16,] 458 609 546 729 163 712 279 943 679 796
## [17,] 811 178 879 244 794 926 119 484 675 155
## [18,] 413 937 282 553 640 289 377 647 156 660
## [19,] 155 53 544 845 177 110 811 65 766 495
## [20,] 264 623 453 94 474 552 909 6 184 318# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 3.16 4.06 3.26 3.47 3.62 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.163055 3.642992 3.674974 3.867695 3.913557 3.994928 4.031744 4.037393
## [2,] 4.060528 4.122820 4.150343 4.335013 4.370475 4.460616 4.600119 4.722819
## [3,] 3.261762 3.577959 3.603593 3.648593 3.828365 3.839198 3.850349 3.865013
## [4,] 3.471128 4.285868 4.326560 4.444999 4.664446 4.700536 4.706743 4.804098
## [5,] 3.622950 3.631121 3.736735 3.749652 3.753022 3.867628 3.908090 3.925925
## [6,] 3.280572 3.393617 3.462860 3.582059 3.585558 3.630077 3.635046 3.638980
## [7,] 2.994119 3.285947 3.530676 3.556735 3.632301 3.712904 3.871217 3.882875
## [8,] 3.628330 3.721614 3.764528 3.812533 3.898113 3.902123 3.910839 3.950359
## [9,] 3.982846 4.241187 4.366695 4.421682 4.489985 4.508412 4.622240 4.627651
## [10,] 3.441652 3.709277 3.911358 3.913552 4.088325 4.136013 4.182617 4.187651
## [11,] 3.692020 3.766231 4.076608 4.109448 4.163996 4.241979 4.269572 4.392679
## [12,] 3.823747 3.992522 4.266755 4.304861 4.362201 4.535784 4.613245 4.656977
## [13,] 2.266397 2.469692 2.661384 2.682995 2.847252 2.964757 2.989359 2.994446
## [14,] 3.021091 3.218097 3.239051 3.386201 3.388964 3.425448 3.507701 3.512956
## [15,] 3.600701 3.812674 4.059967 4.131129 4.139887 4.336047 4.395158 4.444189
## [16,] 2.289549 2.674750 2.710660 2.817048 2.824527 2.830405 2.871365 2.916928
## [17,] 2.659198 3.246829 3.348197 3.371351 3.484666 3.544291 3.555447 3.580441
## [18,] 3.417272 3.767443 3.835858 3.858011 4.011873 4.080676 4.107612 4.207351
## [19,] 2.985225 3.063431 3.096491 3.246503 3.315205 3.442619 3.605121 3.650564
## [20,] 4.691686 4.787794 4.810937 4.869627 4.917376 4.990637 5.005949 5.016011
## [,9] [,10]
## [1,] 4.161751 4.179527
## [2,] 4.759005 4.787616
## [3,] 3.866061 3.883219
## [4,] 4.844865 4.930607
## [5,] 3.965490 4.027999
## [6,] 3.642457 3.657719
## [7,] 3.890315 3.914613
## [8,] 3.965693 3.968549
## [9,] 4.629036 4.634261
## [10,] 4.202808 4.249487
## [11,] 4.426677 4.465376
## [12,] 4.669576 4.819817
## [13,] 3.048919 3.054558
## [14,] 3.518717 3.536555
## [15,] 4.513846 4.547011
## [16,] 2.935566 2.946039
## [17,] 3.662005 3.678749
## [18,] 4.220961 4.327347
## [19,] 3.670544 3.768121
## [20,] 5.095549 5.108312This 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 0.747 1 0.757
## 2 0.960 1 0.848
## 3 1 1 0.785
## 4 0.995 1 0.908
## 5 0.829 1 0.783
## 6 0.546 1 0.910
## 7 0.939 1 0.785
## 8 0.992 1 0.688
## 9 0.831 1 0.928
## 10 1 1 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 -0.157 -0.168 -0.0231 -0.0425
## 2 -0.0882 1.04 0.385 -0.361
## 3 -0.192 0.452 -0.404 -0.350
## 4 -0.0982 -0.256 0.216 -0.554
## 5 -0.275 -0.295 -0.633 0.187
## 6 -0.474 -0.231 -0.312 -0.419
## 7 -0.269 -0.250 0.691 0.817
## 8 -0.171 -0.126 -0.0613 0.187
## 9 -0.203 -0.105 -0.347 -0.137
## 10 -0.0657 -0.243 -0.0264 -0.319
## # 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] 0.237 0.207 0.245 0.199 0.237 ...