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"
## [3] "CD3(Cd112)Di" "CD235-61-7-15(In113)Di"
## [5] "CD3(Cd114)Di" "CD45(In115)Di"
## [7] "CD19(Nd142)Di" "CD22(Nd143)Di"
## [9] "IgD(Nd145)Di" "CD79b(Nd146)Di"
## [11] "CD20(Sm147)Di" "CD34(Nd148)Di"
## [13] "CD179a(Sm149)Di" "CD72(Eu151)Di"
## [15] "IgM(Eu153)Di" "Kappa(Sm154)Di"
## [17] "CD10(Gd156)Di" "Lambda(Gd157)Di"
## [19] "CD24(Dy161)Di" "TdT(Dy163)Di"
## [21] "Rag1(Dy164)Di" "PreBCR(Ho165)Di"
## [23] "CD43(Er167)Di" "CD38(Er168)Di"
## [25] "CD40(Er170)Di" "CD33(Yb173)Di"
## [27] "HLA-DR(Yb174)Di"
##
## $functional
## [1] "pCrkL(Lu175)Di" "pCREB(Yb176)Di" "pBTK(Yb171)Di"
## [4] "pS6(Yb172)Di" "cPARP(La139)Di" "pPLCg2(Pr141)Di"
## [7] "pSrc(Nd144)Di" "Ki67(Sm152)Di" "pErk12(Gd155)Di"
## [10] "pSTAT3(Gd158)Di" "pAKT(Tb159)Di" "pBLNK(Gd160)Di"
## [13] "pP38(Tm169)Di" "pSTAT5(Nd150)Di" "pSyk(Dy162)Di"
## [16] "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] 750 68 433 452 554 273 608 802 172 596 ...
wand.nn[[1]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 750 658 709 702 575 170 796 68 730 347
## [2,] 68 484 64 166 538 97 382 345 803 683
## [3,] 433 549 645 65 420 104 667 284 331 115
## [4,] 452 843 669 948 769 564 961 791 26 212
## [5,] 554 690 692 249 492 694 842 34 671 885
## [6,] 273 315 357 762 381 382 783 829 688 960
## [7,] 608 814 822 167 900 96 941 240 792 86
## [8,] 802 769 905 553 753 783 332 190 763 105
## [9,] 172 713 79 883 121 687 610 107 671 776
## [10,] 596 317 825 88 640 501 840 515 122 597
## [11,] 696 607 188 775 777 455 97 583 463 180
## [12,] 23 384 870 224 520 74 73 368 14 844
## [13,] 440 922 436 580 792 380 822 662 608 565
## [14,] 461 74 744 523 224 765 146 925 151 959
## [15,] 316 588 365 341 481 419 351 811 447 649
## [16,] 338 143 866 331 575 660 104 509 930 755
## [17,] 135 33 121 981 687 499 568 776 813 394
## [18,] 367 670 77 204 51 229 991 42 130 685
## [19,] 299 363 254 803 661 484 647 2 84 307
## [20,] 277 177 42 71 112 956 986 407 460 448
# Distance
str(wand.nn[[2]])
## num [1:1000, 1:30] 3.95 3.05 2.9 2.92 2.34 ...
wand.nn[[2]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 3.951312 4.078269 4.090827 4.128266 4.291013 4.299045 4.310667
## [2,] 3.045367 3.072616 3.165143 3.169765 3.390994 3.406348 3.425642
## [3,] 2.902729 2.917114 3.200399 3.226009 3.545225 3.557851 3.612108
## [4,] 2.921099 2.936044 2.995608 3.135549 3.151006 3.396874 3.437473
## [5,] 2.336095 2.998369 3.052081 3.105921 3.361319 3.499439 3.511252
## [6,] 3.380156 3.595051 3.611403 3.650910 3.651106 3.702436 3.792771
## [7,] 3.732467 3.943752 4.170143 4.336712 4.493268 4.539107 4.560543
## [8,] 3.583994 3.698748 3.718052 3.739959 3.770411 3.835693 3.860846
## [9,] 3.503807 3.571807 3.728724 3.833117 3.873347 3.918159 3.927282
## [10,] 3.844795 3.888269 3.976899 4.009838 4.053995 4.224075 4.347911
## [11,] 3.320610 3.457156 3.457709 3.470432 3.543018 3.623170 3.695119
## [12,] 3.174668 3.262967 3.307915 3.411497 3.595608 3.611575 3.612689
## [13,] 4.161973 4.374602 4.714625 4.768746 4.972629 4.979035 5.152720
## [14,] 2.183445 2.213633 2.467871 2.548933 2.564574 2.624695 2.696517
## [15,] 3.664541 4.140936 4.241979 4.269572 4.278298 4.292908 4.325495
## [16,] 2.740607 2.853686 3.536080 3.781405 3.902603 3.916976 3.954297
## [17,] 3.526604 4.241187 4.311942 4.366695 4.371984 4.421682 4.429282
## [18,] 3.198415 3.234716 3.302682 3.345636 3.426301 3.445440 3.484984
## [19,] 4.317703 4.344680 4.522058 4.589563 4.601528 4.613518 4.684042
## [20,] 2.399169 2.767063 2.896945 2.896975 3.015529 3.070590 3.089206
## [,8] [,9] [,10]
## [1,] 4.314095 4.341169 4.344483
## [2,] 3.447915 3.454167 3.571054
## [3,] 3.623875 3.856667 4.187781
## [4,] 3.468788 3.475168 3.487585
## [5,] 3.523420 3.583414 3.628449
## [6,] 3.829575 3.833993 3.834789
## [7,] 4.679822 4.687230 4.718514
## [8,] 3.907122 3.981942 3.998136
## [9,] 3.936384 3.937841 3.992439
## [10,] 4.381019 4.428430 4.444949
## [11,] 3.696015 3.697610 3.706676
## [12,] 3.623273 3.628592 3.660328
## [13,] 5.219919 5.325664 5.327789
## [14,] 2.865699 2.873438 2.878373
## [15,] 4.392679 4.401311 4.449122
## [16,] 4.026121 4.067289 4.124898
## [17,] 4.597442 4.629036 4.741965
## [18,] 3.611451 3.629190 3.651992
## [19,] 4.722269 4.745259 4.746871
## [20,] 3.115053 3.180848 3.202088
This 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… `pCREB(Yb176)Di… `pBTK(Yb171)Di.… `pS6(Yb172)Di.I…
## <dbl> <dbl> <dbl> <dbl>
## 1 0.933 0.850 1 0.934
## 2 0.615 0.987 1 1
## 3 0.933 0.784 1 0.867
## 4 1 1 1 1
## 5 1 0.913 1 0.989
## 6 0.776 0.904 1 0.949
## 7 0.980 0.851 1 0.989
## 8 0.776 0.816 0.933 0.521
## 9 0.794 0.751 1 0.985
## 10 0.913 0.751 1 0.892
## # ... 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(…
## <dbl> <dbl> <dbl> <dbl>
## 1 -0.659 1.29 -0.955 -0.209
## 2 -0.189 -0.369 -0.587 0.291
## 3 -0.138 -0.155 -0.0782 0.305
## 4 -0.105 0.773 0.645 -0.587
## 5 0.237 -0.0447 -0.119 -0.717
## 6 -0.00438 -0.304 -0.248 0.347
## 7 0.504 -0.0952 -0.0422 0.453
## 8 -0.0363 -0.184 -0.0884 0.733
## 9 -0.438 -0.0758 -0.118 -0.678
## 10 -0.491 1.75 -0.411 -0.945
## # ... with 20 more rows, and 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>, `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] 0.226 0.277 0.238 0.277 0.266 ...