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] 331 402 895 319 583 525 937 92 479 414 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 331 85 900 271 859 342 35 731 673 835
## [2,] 402 220 35 85 900 611 859 295 685 976
## [3,] 895 921 982 411 564 281 583 387 626 239
## [4,] 319 824 175 39 172 667 281 32 192 147
## [5,] 583 895 199 537 147 83 921 635 105 272
## [6,] 525 976 673 85 651 307 489 891 425 547
## [7,] 937 147 847 248 904 140 537 603 921 447
## [8,] 92 638 744 199 840 281 359 921 111 41
## [9,] 479 416 976 548 651 765 871 799 618 733
## [10,] 414 28 638 584 127 337 660 443 136 283
## [11,] 767 157 986 675 937 889 820 583 723 333
## [12,] 883 124 453 256 511 965 796 14 1000 204
## [13,] 67 132 767 672 319 937 831 615 200 564
## [14,] 965 941 12 370 453 62 409 361 439 980
## [15,] 72 662 773 928 535 778 264 901 171 144
## [16,] 444 174 912 968 717 520 648 563 691 944
## [17,] 869 251 180 237 833 615 495 186 936 95
## [18,] 667 892 390 7 952 38 939 34 319 870
## [19,] 519 359 386 608 565 809 589 414 527 332
## [20,] 537 739 112 574 518 637 615 118 682 143# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 3.06 3.59 2.59 3.11 3.72 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.061600 3.467129 3.947331 3.987906 4.044506 4.047196 4.078871 4.096658
## [2,] 3.593361 3.671397 3.776295 3.900636 3.904336 3.967070 4.100474 4.178218
## [3,] 2.587125 2.668626 2.702495 2.703718 2.952970 2.968647 2.970473 2.983170
## [4,] 3.113533 3.318731 3.567572 3.609403 3.625169 3.636588 3.723023 3.757469
## [5,] 3.721614 3.860959 3.921677 3.937205 3.938807 3.942885 3.963566 3.970520
## [6,] 3.339616 4.188286 4.243879 4.321129 4.331707 4.366374 4.398688 4.560838
## [7,] 3.063972 3.149381 3.246037 3.251005 3.377825 3.391909 3.407321 3.408640
## [8,] 3.160703 3.437360 3.555439 3.589279 3.624309 3.700522 3.705418 3.782933
## [9,] 3.943092 4.052555 4.059438 4.343641 4.346685 4.350506 4.434867 4.452189
## [10,] 3.687519 3.692020 3.713328 3.923946 3.965478 4.055321 4.062498 4.117792
## [11,] 4.500377 4.751897 4.788519 4.830815 4.834286 4.874713 4.883767 4.945858
## [12,] 3.005773 3.035394 3.050291 3.142457 3.183187 3.205621 3.277666 3.304815
## [13,] 2.595939 2.603422 2.929799 3.247262 3.265940 3.325128 3.457625 3.496824
## [14,] 3.012112 3.230329 3.304815 3.340812 3.447265 3.597304 3.606928 3.667046
## [15,] 2.874834 3.130284 3.171814 3.190768 3.247821 3.271862 3.284615 3.492266
## [16,] 4.327196 4.371005 4.375177 4.386573 4.464914 4.471504 4.476672 4.510921
## [17,] 4.163650 4.354378 4.371849 4.394360 4.411843 4.465548 4.471538 4.574524
## [18,] 3.954770 3.972435 4.125692 4.142760 4.291489 4.324602 4.352732 4.356745
## [19,] 2.948288 3.793528 3.826311 3.846804 3.909770 3.933604 3.940481 3.956164
## [20,] 2.501847 2.937926 2.968273 3.089898 3.124067 3.127436 3.149268 3.172053
## [,9] [,10]
## [1,] 4.144787 4.154771
## [2,] 4.227181 4.247132
## [3,] 3.015582 3.040671
## [4,] 3.818075 3.820665
## [5,] 4.021386 4.034945
## [6,] 4.635994 4.650246
## [7,] 3.411293 3.427433
## [8,] 3.798916 3.826682
## [9,] 4.472377 4.480609
## [10,] 4.181831 4.195806
## [11,] 4.954369 4.969762
## [12,] 3.456875 3.474798
## [13,] 3.555845 3.558326
## [14,] 3.716201 3.727787
## [15,] 3.523420 3.553985
## [16,] 4.525895 4.544326
## [17,] 4.599718 4.607520
## [18,] 4.372232 4.403383
## [19,] 4.003848 4.013492
## [20,] 3.186410 3.265923This 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 1 0.815 0.901
## 2 1 0.690 0.984
## 3 1 0.937 0.927
## 4 1 0.937 0.901
## 5 1 0.908 0.773
## 6 1 0.937 0.862
## 7 1 0.908 0.862
## 8 1 0.908 0.995
## 9 1 0.972 0.773
## 10 1 0.974 0.999
## # 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 0.0567 0.718 0.428 0.899
## 2 -0.131 -0.106 -0.294 0.683
## 3 -0.143 -0.484 -0.289 -0.892
## 4 -0.180 -0.216 -0.202 0.0174
## 5 -0.299 -0.862 -0.699 -0.587
## 6 -0.305 -0.594 0.262 -0.411
## 7 -0.326 -0.0343 1.04 0.157
## 8 -0.234 -0.0633 -0.104 -0.579
## 9 -0.0210 -0.0601 0.432 -0.906
## 10 -0.239 -0.194 -0.237 -0.850
## # 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] 0.233 0.226 0.326 0.259 0.245 ...