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] 662 710 76 995 705 690 283 712 32 322 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 662 391 444 387 60 931 690 614 643 311
## [2,] 710 912 673 138 486 446 267 875 503 992
## [3,] 76 909 74 418 552 836 354 676 215 889
## [4,] 995 501 500 939 596 76 78 418 215 40
## [5,] 705 192 182 247 183 961 704 819 773 360
## [6,] 690 934 769 978 722 902 694 703 60 233
## [7,] 283 688 704 176 433 183 830 321 217 599
## [8,] 712 823 382 21 446 882 673 472 267 710
## [9,] 32 441 95 86 932 795 829 579 90 752
## [10,] 322 333 970 661 51 966 186 585 738 11
## [11,] 740 626 650 518 333 585 714 210 961 841
## [12,] 898 196 688 360 839 705 582 439 764 261
## [13,] 256 491 605 793 152 724 206 712 316 267
## [14,] 792 791 287 414 388 722 694 488 253 402
## [15,] 137 585 974 234 186 518 933 740 738 770
## [16,] 791 691 414 681 282 460 154 448 900 631
## [17,] 54 525 366 421 247 770 217 598 234 186
## [18,] 242 165 986 930 193 528 827 913 520 152
## [19,] 127 170 549 274 1 726 587 289 534 488
## [20,] 907 546 511 500 734 995 579 863 684 441# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 2.47 3.69 4.33 4.08 2.87 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 2.474032 2.789880 2.956495 2.960426 3.008786 3.132630 3.304342 3.348713
## [2,] 3.686659 3.807383 3.898689 3.966064 3.998661 4.073505 4.077283 4.176180
## [3,] 4.325594 4.822521 4.943263 4.989323 5.008097 5.022956 5.034340 5.091783
## [4,] 4.079191 4.104734 4.304730 4.468533 4.847785 4.895267 4.916688 4.932999
## [5,] 2.873100 2.917214 2.930088 2.979536 3.183717 3.235999 3.237961 3.291589
## [6,] 2.798396 3.137523 3.238542 3.266212 3.280094 3.323589 3.367035 3.410949
## [7,] 2.516752 2.739680 3.061777 3.070146 3.093891 3.099083 3.115024 3.122734
## [8,] 3.026220 3.165599 3.368979 3.435780 3.445453 3.531836 3.574098 3.633856
## [9,] 4.484265 5.038568 5.295480 5.352405 5.363247 5.455077 5.476144 5.479535
## [10,] 3.244902 3.370914 3.598682 3.620696 3.654803 3.748353 3.801327 3.851958
## [11,] 2.608074 2.793839 2.895936 2.987004 2.991700 3.122293 3.130170 3.167068
## [12,] 3.601931 3.750548 3.915970 3.955643 3.967931 3.976458 3.988736 3.996245
## [13,] 3.865158 4.159029 4.304882 4.475063 4.505099 4.528031 4.559770 4.588397
## [14,] 3.654436 3.799726 3.876344 3.980986 4.044009 4.127788 4.154500 4.163842
## [15,] 3.267832 3.271425 3.400857 3.555033 3.636335 3.655267 3.685537 3.748522
## [16,] 2.660734 2.972224 3.222712 3.262778 3.298726 3.299002 3.300886 3.330355
## [17,] 2.743782 3.002740 3.269234 3.270726 3.315812 3.330879 3.334825 3.362373
## [18,] 2.614302 2.670281 3.115997 3.133923 3.172732 3.313952 3.369040 3.398042
## [19,] 3.473602 4.064624 4.239851 4.487576 4.645882 4.651978 4.679581 4.835348
## [20,] 3.952653 4.491266 4.665894 4.685829 4.791210 4.898619 4.910816 4.912577
## [,9] [,10]
## [1,] 3.361317 3.434218
## [2,] 4.249133 4.277344
## [3,] 5.138258 5.223982
## [4,] 4.938203 4.990717
## [5,] 3.298971 3.311429
## [6,] 3.469062 3.475971
## [7,] 3.164888 3.221862
## [8,] 3.646836 3.659967
## [9,] 5.486029 5.579526
## [10,] 3.914378 3.923677
## [11,] 3.174836 3.190156
## [12,] 4.012887 4.175864
## [13,] 4.739035 4.801101
## [14,] 4.214410 4.244086
## [15,] 3.750444 3.796365
## [16,] 3.387118 3.410273
## [17,] 3.367654 3.371004
## [18,] 3.399457 3.432905
## [19,] 4.859462 4.880660
## [20,] 4.976591 5.074714This 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 0.892 0.662 0.878
## 2 0.995 0.941 0.878
## 3 0.892 0.902 0.941
## 4 0.810 0.883 0.941
## 5 0.627 0.976 0.759
## 6 0.628 0.870 0.885
## 7 0.855 0.912 0.773
## 8 0.988 0.579 0.998
## 9 0.932 0.769 0.938
## 10 0.876 1 0.934
## # 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 0.152 0.384 0.702 0.529
## 2 0.396 -0.107 -0.0873 -0.144
## 3 -0.929 -0.570 -0.345 -0.446
## 4 -0.306 -0.664 -0.507 -1.23
## 5 -0.251 -0.109 -0.0732 -0.277
## 6 -0.115 -0.219 0.367 -0.444
## 7 -0.420 -0.408 -0.514 -0.637
## 8 -0.151 -0.0659 -0.236 0.309
## 9 -0.263 -0.0122 -0.107 0.281
## 10 -0.0951 0.290 -0.0805 0.143
## # 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] 0.289 0.232 0.183 0.199 0.3 ...