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] 892 358 625 144 491 948 818 305 751 191 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 892 737 85 274 251 17 364 921 790 479
## [2,] 358 515 610 689 664 672 801 646 679 143
## [3,] 625 598 745 459 275 677 638 511 204 203
## [4,] 144 945 374 811 520 514 305 895 528 884
## [5,] 491 558 392 980 757 733 298 110 276 687
## [6,] 948 427 899 695 830 724 934 804 504 516
## [7,] 818 187 572 401 323 669 92 430 948 181
## [8,] 305 646 517 56 895 194 494 73 897 873
## [9,] 751 958 682 810 661 344 298 467 728 343
## [10,] 191 791 696 501 805 76 306 650 633 643
## [11,] 452 14 771 546 972 873 751 936 227 146
## [12,] 948 76 733 320 191 108 516 196 569 315
## [13,] 518 998 701 496 682 220 17 230 126 87
## [14,] 527 862 558 11 452 569 721 704 101 426
## [15,] 463 671 523 79 304 446 691 546 930 11
## [16,] 960 527 985 642 131 874 546 469 643 564
## [17,] 524 998 446 41 855 523 671 274 479 737
## [18,] 663 219 319 620 822 726 973 947 223 464
## [19,] 714 483 638 542 272 217 391 393 507 44
## [20,] 557 633 650 576 564 743 922 76 141 73## num [1:1000, 1:30] 3.6 3.86 3.41 4.14 2.89 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.597517 3.682086 3.770940 3.786448 3.816526 3.884957 3.929530 4.022458
## [2,] 3.862287 3.893723 4.004221 4.090277 4.118483 4.155429 4.189914 4.436641
## [3,] 3.406636 3.923319 4.167996 4.173893 4.206920 4.214784 4.240712 4.265978
## [4,] 4.140878 4.398263 4.408850 4.423437 4.455586 4.496290 4.547326 4.549342
## [5,] 2.885137 2.964646 3.013319 3.151578 3.200521 3.228751 3.276451 3.285030
## [6,] 2.614362 2.766335 2.814126 2.987915 3.039159 3.042328 3.060159 3.068015
## [7,] 2.694178 3.209440 3.298959 3.421240 3.475168 3.478957 3.524819 3.612299
## [8,] 4.067759 4.291209 4.310931 4.323342 4.452250 4.491660 4.515605 4.541525
## [9,] 3.417541 3.597990 3.686504 3.704716 3.759019 3.768494 3.779582 3.784145
## [10,] 3.438563 3.653781 3.686222 3.736796 3.782037 3.837499 3.994575 4.078010
## [11,] 2.310442 2.740851 2.798149 2.815045 2.882021 2.882089 2.973589 2.977038
## [12,] 2.811354 2.848022 2.859005 3.049587 3.056745 3.091457 3.164357 3.198239
## [13,] 3.163784 3.495967 3.598992 3.599732 3.733317 3.767352 3.802625 3.807877
## [14,] 2.485015 2.653001 2.684383 2.740851 2.865239 2.991803 3.011045 3.107181
## [15,] 3.160703 3.399980 3.437360 3.569888 3.645062 3.705418 3.766952 3.782933
## [16,] 2.680930 2.854281 2.895732 2.899957 2.970063 3.027255 3.042406 3.115715
## [17,] 2.795223 2.973167 2.979887 3.063779 3.090327 3.105851 3.151453 3.156564
## [18,] 3.958606 3.980797 4.051932 4.076361 4.217798 4.343024 4.467726 4.516196
## [19,] 2.491643 3.037649 3.331466 3.392959 3.399999 3.511985 3.579136 3.615328
## [20,] 4.241486 4.334695 4.397172 4.456708 4.506987 4.588290 4.665674 4.690217
## [,9] [,10]
## [1,] 4.054630 4.067000
## [2,] 4.512263 4.578121
## [3,] 4.269086 4.292833
## [4,] 4.557152 4.564612
## [5,] 3.302682 3.311190
## [6,] 3.157734 3.166407
## [7,] 3.658984 3.715041
## [8,] 4.623213 4.641192
## [9,] 3.823646 3.839488
## [10,] 4.089239 4.103829
## [11,] 2.985225 3.006233
## [12,] 3.233157 3.251773
## [13,] 3.866195 3.878691
## [14,] 3.110235 3.142318
## [15,] 3.818428 3.829799
## [16,] 3.116108 3.164540
## [17,] 3.262575 3.267078
## [18,] 4.554737 4.620931
## [19,] 3.702612 3.732389
## [20,] 4.716477 4.720992This 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 0.958 1 0.925
## 2 0.999 1 0.959
## 3 0.938 1 0.972
## 4 0.956 0.819 1
## 5 0.884 1 0.972
## 6 0.880 1 0.990
## 7 0.570 1 0.792
## 8 0.999 1 1
## 9 0.999 1 0.959
## 10 0.841 1 0.792
## # 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 -0.204 -0.0512 -0.240 -1.90
## 2 -0.147 -0.264 -0.845 -1.25
## 3 -0.768 -0.580 -0.264 -1.63
## 4 -0.174 -0.0635 -0.219 -0.776
## 5 -0.207 -1.14 -0.779 -0.737
## 6 -0.658 -0.0448 0.580 -1.21
## 7 -0.645 -0.311 -0.677 -0.544
## 8 -0.650 -0.110 -0.254 -1.31
## 9 -0.937 -1.11 0.362 -0.782
## 10 -0.204 -0.113 -0.156 -0.0615
## # 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] 0.242 0.213 0.231 0.208 0.293 ...