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] 213 503 979 665 326 259 918 237 110 563 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 213 320 133 331 694 761 724 201 520 963
## [2,] 503 138 592 881 934 900 219 255 804 922
## [3,] 979 749 777 988 252 310 246 900 422 687
## [4,] 665 222 397 614 547 555 709 129 12 696
## [5,] 326 911 879 717 669 782 427 682 231 210
## [6,] 259 653 675 227 747 826 342 670 990 150
## [7,] 918 707 11 306 630 226 960 233 135 595
## [8,] 237 460 492 151 473 235 886 883 933 52
## [9,] 110 331 457 113 721 598 83 27 839 816
## [10,] 563 864 928 585 593 723 857 854 272 374
## [11,] 31 27 336 590 960 226 111 966 248 875
## [12,] 796 397 344 651 165 210 77 884 529 95
## [13,] 285 939 203 212 325 363 482 192 891 244
## [14,] 754 95 326 466 938 316 917 529 637 793
## [15,] 684 141 219 484 794 493 900 385 582 752
## [16,] 414 730 289 988 881 412 951 219 123 517
## [17,] 66 489 931 442 54 153 661 347 934 168
## [18,] 949 323 228 999 607 511 177 639 849 140
## [19,] 861 314 843 181 136 531 194 287 523 578
## [20,] 523 74 175 161 691 765 529 80 651 314# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 3.22 2.54 3.6 3.87 3.32 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.224171 3.781679 3.814151 3.842906 3.896887 3.920367 3.966544 3.984069
## [2,] 2.543838 3.007084 3.043255 3.048118 3.063609 3.099520 3.120598 3.134536
## [3,] 3.597990 3.612581 3.631417 3.704716 3.724610 3.768494 3.784145 3.817330
## [4,] 3.873524 3.966638 4.099818 4.118173 4.164315 4.335471 4.449264 4.475207
## [5,] 3.319166 3.454767 3.462312 3.480990 3.495325 3.584387 3.591139 3.678692
## [6,] 4.744116 4.759412 4.822521 5.034340 5.223982 5.343851 5.401241 5.423766
## [7,] 3.285076 3.341014 3.380446 3.427433 3.608696 3.615235 3.721213 3.757769
## [8,] 3.182050 3.887973 3.985128 4.163017 4.234621 4.265337 4.358083 4.419198
## [9,] 3.664541 4.241979 4.278298 4.292908 4.392679 4.449122 4.465376 4.487642
## [10,] 3.644204 4.122683 4.217476 4.265636 4.293315 4.331980 4.333862 4.351938
## [11,] 2.698241 2.799801 3.059686 3.062834 3.103149 3.136298 3.193783 3.253652
## [12,] 2.187504 2.392560 2.960871 2.998460 3.019414 3.058477 3.093719 3.101004
## [13,] 3.719667 3.857160 4.026514 4.204732 4.270014 4.276164 4.436698 4.460467
## [14,] 3.564791 3.737485 3.807738 3.855312 3.898207 4.008878 4.041281 4.049655
## [15,] 2.943314 3.121772 3.328729 3.355104 3.361507 3.422021 3.442892 3.488347
## [16,] 2.935189 3.105588 3.114884 3.277301 3.282398 3.374517 3.390027 3.402984
## [17,] 3.149844 3.670407 3.732428 3.792964 3.796161 3.916240 3.916421 3.966286
## [18,] 4.681334 4.841910 4.849899 4.903387 4.996874 5.035517 5.197326 5.323472
## [19,] 2.660734 2.744103 2.972224 3.222712 3.230650 3.262778 3.298726 3.299002
## [20,] 3.580379 3.956137 3.959542 3.988474 4.294317 4.355036 4.360842 4.368750
## [,9] [,10]
## [1,] 3.991925 4.057609
## [2,] 3.188910 3.199834
## [3,] 3.883986 3.923170
## [4,] 4.595665 4.611223
## [5,] 3.697357 3.709681
## [6,] 5.513702 5.565066
## [7,] 3.869995 4.024257
## [8,] 4.434484 4.529207
## [9,] 4.521537 4.523671
## [10,] 4.357092 4.415192
## [11,] 3.255873 3.257816
## [12,] 3.102715 3.181871
## [13,] 4.474204 4.538710
## [14,] 4.059009 4.135997
## [15,] 3.503308 3.545867
## [16,] 3.460375 3.461995
## [17,] 4.014342 4.037680
## [18,] 5.579534 5.621358
## [19,] 3.410273 3.489173
## [20,] 4.384493 4.401984This 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 0.602 0.981
## 2 0.875 0.774 1
## 3 1 0.882 0.982
## 4 0.939 0.937 0.969
## 5 0.800 0.949 0.981
## 6 0.758 0.882 0.999
## 7 1 0.980 0.847
## 8 1 0.712 0.847
## 9 1 0.666 1
## 10 1 0.649 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 -0.0492 -0.161 -0.220 -0.807
## 2 -0.157 -0.262 -0.0956 -0.293
## 3 -0.00169 -0.00767 -0.207 -1.19
## 4 -0.236 -0.0121 -0.0770 0.169
## 5 -0.547 0.474 -0.983 -0.494
## 6 -0.185 -0.101 -0.350 -0.128
## 7 -0.202 -0.0294 -0.289 -0.534
## 8 -0.142 -0.171 -0.0665 0.220
## 9 -0.00819 0.104 -0.155 0.131
## 10 -0.741 -0.805 -1.12 -1.02
## # 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] 0.244 0.306 0.253 0.217 0.265 ...