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] 744 32 856 51 608 632 879 4 318 18 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 744 818 728 335 934 22 762 306 202 587
## [2,] 32 306 886 818 475 314 161 819 124 671
## [3,] 856 948 391 359 281 497 895 692 707 659
## [4,] 51 127 757 583 324 964 340 581 771 240
## [5,] 608 645 426 650 493 422 502 917 903 73
## [6,] 632 839 271 857 455 977 61 300 234 438
## [7,] 879 911 552 226 547 750 372 541 567 492
## [8,] 4 126 456 298 581 237 583 393 324 127
## [9,] 318 166 310 343 360 647 48 280 635 653
## [10,] 18 99 370 980 555 315 903 365 731 502
## [11,] 226 879 678 359 461 922 734 39 5 7
## [12,] 596 269 98 690 282 978 95 60 243 148
## [13,] 63 904 754 61 625 910 462 434 203 938
## [14,] 672 644 763 29 297 278 909 296 821 52
## [15,] 130 418 407 927 846 166 900 232 181 688
## [16,] 615 822 138 169 92 341 938 486 175 791
## [17,] 6 999 866 782 889 500 429 102 89 455
## [18,] 731 715 10 418 543 422 493 650 76 468
## [19,] 82 766 967 710 550 146 783 971 22 574
## [20,] 878 497 522 563 935 968 416 452 415 702# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 2.95 3.21 2.18 2.58 3.78 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 2.950659 3.083706 3.457156 3.623170 3.656035 3.695119 3.696015 3.706676
## [2,] 3.211234 3.292320 3.303263 3.333152 3.481355 3.604993 3.643893 3.656955
## [3,] 2.184495 2.398957 2.551225 2.853031 2.968610 2.996549 3.028090 3.065379
## [4,] 2.582158 2.709808 2.750523 2.797013 2.802767 2.803004 2.860145 2.862379
## [5,] 3.783014 3.896379 4.052049 4.075601 4.124767 4.156030 4.205589 4.221158
## [6,] 3.208516 3.210629 3.229654 3.233527 3.312319 3.317971 3.346789 3.406806
## [7,] 3.526917 3.758880 3.810975 4.070028 4.117044 4.181184 4.394502 4.414028
## [8,] 2.954442 3.082324 3.125124 3.219256 3.324642 3.434119 3.465046 3.471081
## [9,] 4.398899 4.409877 4.465460 4.466785 4.593583 4.642551 4.674345 4.681658
## [10,] 3.534774 4.171182 4.314909 4.434783 4.436591 4.496290 4.554011 4.588079
## [11,] 4.337072 4.456780 4.458585 4.781641 4.913038 5.052050 5.072518 5.078147
## [12,] 5.330974 5.433805 5.611114 5.650293 5.661905 5.672119 5.723587 5.768794
## [13,] 3.672089 3.697218 3.779669 3.922627 4.048636 4.073322 4.128494 4.260912
## [14,] 3.249785 3.733174 4.238197 4.365529 4.599008 4.628928 4.658578 4.697713
## [15,] 5.719366 5.841000 6.146462 6.209535 6.234971 6.276826 6.287511 6.336659
## [16,] 3.831873 4.309240 4.472985 4.843249 4.881974 4.915724 4.999745 5.157341
## [17,] 4.969233 5.021415 5.088030 5.140528 5.145146 5.260817 5.330267 5.359440
## [18,] 3.220076 3.493143 3.534774 3.593361 3.619259 3.627505 3.660338 3.722327
## [19,] 2.102466 2.659198 3.344346 3.348197 3.484666 3.491188 3.584454 3.662005
## [20,] 3.166222 3.461670 3.602633 3.628453 3.713589 3.724005 3.732426 3.750626
## [,9] [,10]
## [1,] 3.711048 3.789614
## [2,] 3.675832 3.679565
## [3,] 3.078487 3.127765
## [4,] 2.894769 2.900045
## [5,] 4.293006 4.394186
## [6,] 3.500036 3.514821
## [7,] 4.488203 4.548506
## [8,] 3.519632 3.592783
## [9,] 4.714946 4.722383
## [10,] 4.620060 4.677227
## [11,] 5.208353 5.235435
## [12,] 5.863151 5.879997
## [13,] 4.331258 4.365954
## [14,] 4.760430 4.769281
## [15,] 6.423392 6.558309
## [16,] 5.198759 5.239382
## [17,] 5.399785 5.416715
## [18,] 3.818443 3.862997
## [19,] 3.678749 3.697780
## [20,] 3.796046 3.819036This 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 x 34
## `pCrkL(Lu175)Di.IL… `pCREB(Yb176)Di.IL… `pBTK(Yb171)Di.IL… `pS6(Yb172)Di.IL7…
## <dbl> <dbl> <dbl> <dbl>
## 1 0.985 1 0.935 0.847
## 2 1 1 0.673 0.982
## 3 0.985 1 0.784 1
## 4 0.985 0.858 0.433 0.954
## 5 0.985 1 1 1
## 6 0.985 0.969 0.590 1
## 7 0.985 1 0.784 1
## 8 0.985 0.969 0.331 0.888
## 9 0.985 0.949 0.798 1
## 10 0.985 1 0.806 0.718
## # … with 990 more rows, and 30 more variables: 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>, pAKT(Tb159)Di.IL7.qvalue <dbl>,
## # pBLNK(Gd160)Di.IL7.qvalue <dbl>, pP38(Tm169)Di.IL7.qvalue <dbl>,
## # pSTAT5(Nd150)Di.IL7.qvalue <dbl>, pSyk(Dy162)Di.IL7.qvalue <dbl>,
## # tIkBa(Er166)Di.IL7.qvalue <dbl>, pCrkL(Lu175)Di.IL7.change <dbl>,
## # pCREB(Yb176)Di.IL7.change <dbl>, pBTK(Yb171)Di.IL7.change <dbl>,
## # pS6(Yb172)Di.IL7.change <dbl>, cPARP(La139)Di.IL7.change <dbl>,
## # pPLCg2(Pr141)Di.IL7.change <dbl>, pSrc(Nd144)Di.IL7.change <dbl>,
## # Ki67(Sm152)Di.IL7.change <dbl>, pErk12(Gd155)Di.IL7.change <dbl>,
## # pSTAT3(Gd158)Di.IL7.change <dbl>, pAKT(Tb159)Di.IL7.change <dbl>,
## # pBLNK(Gd160)Di.IL7.change <dbl>, pP38(Tm169)Di.IL7.change <dbl>,
## # pSTAT5(Nd150)Di.IL7.change <dbl>, pSyk(Dy162)Di.IL7.change <dbl>,
## # tIkBa(Er166)Di.IL7.change <dbl>, IL7.fraction.cond.2 <dbl>, density <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 x 51
## `CD3(Cd110)Di` `CD3(Cd111)Di` `CD3(Cd112)Di` `CD235-61-7-15(I… `CD3(Cd114)Di`
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -0.0595 -0.264 -0.454 -1.16 -0.416
## 2 -0.362 -0.423 -1.01 -0.787 -1.07
## 3 0.247 -0.498 -0.639 -0.0719 -0.495
## 4 -0.00552 -0.187 -0.142 -1.89 0.102
## 5 -0.0759 -0.478 0.314 -0.159 -0.314
## 6 -0.278 -0.00435 -0.131 -1.06 -0.292
## 7 -0.241 -0.0568 -0.148 -0.0476 -0.0679
## 8 -0.0980 -0.00653 -0.116 -0.826 -0.133
## 9 -0.158 -0.0951 -0.235 -0.369 -0.246
## 10 -0.0957 -0.176 -0.199 -0.493 -0.0135
## # … with 20 more rows, and 46 more variables: 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>,
## # PreBCR(Ho165)Di <dbl>, CD43(Er167)Di <dbl>, CD38(Er168)Di <dbl>,
## # CD40(Er170)Di <dbl>, CD33(Yb173)Di <dbl>, HLA-DR(Yb174)Di <dbl>,
## # Time <dbl>, Cell_length <dbl>, cPARP(La139)Di <dbl>, pPLCg2(Pr141)Di <dbl>,
## # pSrc(Nd144)Di <dbl>, pSTAT5(Nd150)Di <dbl>, Ki67(Sm152)Di <dbl>,
## # pErk12(Gd155)Di <dbl>, pSTAT3(Gd158)Di <dbl>, pAKT(Tb159)Di <dbl>,
## # pBLNK(Gd160)Di <dbl>, pSyk(Dy162)Di <dbl>, tIkBa(Er166)Di <dbl>,
## # pP38(Tm169)Di <dbl>, pBTK(Yb171)Di <dbl>, pS6(Yb172)Di <dbl>,
## # pCrkL(Lu175)Di <dbl>, pCREB(Yb176)Di <dbl>, DNA1(Ir191)Di <dbl>,
## # DNA2(Ir193)Di <dbl>, Viability1(Pt195)Di <dbl>, Viability2(Pt196)Di <dbl>,
## # wanderlust <dbl>, condition <chr># 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] 0.262 0.268 0.32 0.332 0.22 ...