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] 768 800 355 145 41 350 311 879 595 595 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 768 242 528 960 491 913 628 281 51 265
## [2,] 800 258 145 672 289 718 4 571 782 891
## [3,] 355 123 771 134 229 17 376 790 646 451
## [4,] 145 162 835 961 258 672 428 483 492 584
## [5,] 41 588 609 951 501 331 919 304 391 935
## [6,] 350 371 577 222 477 609 733 291 775 74
## [7,] 311 361 352 710 763 396 888 285 575 267
## [8,] 879 741 339 84 354 795 368 158 136 655
## [9,] 595 112 446 411 636 784 325 266 177 505
## [10,] 595 84 114 642 354 159 871 822 655 468
## [11,] 662 239 370 759 730 272 859 155 803 652
## [12,] 109 496 953 981 570 978 564 469 356 149
## [13,] 120 239 611 455 503 22 640 859 950 271
## [14,] 535 687 727 302 749 388 985 786 674 483
## [15,] 318 56 714 533 728 807 50 600 225 955
## [16,] 423 60 93 485 442 708 368 606 878 917
## [17,] 355 123 688 57 319 58 415 756 215 376
## [18,] 514 835 48 416 857 629 741 162 114 261
## [19,] 160 997 801 733 470 178 739 885 716 589
## [20,] 875 611 190 493 464 599 707 75 723 561## num [1:1000, 1:30] 3.44 4.13 3.67 3.17 3.26 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.441515 3.774343 3.954770 3.972435 4.142760 4.145777 4.153422 4.157856
## [2,] 4.133917 4.413266 4.526329 4.535665 4.657785 4.838354 4.896079 4.908071
## [3,] 3.667559 3.777098 3.852472 3.863646 3.905320 3.943662 3.977819 4.009312
## [4,] 3.169950 3.454380 3.635642 3.671480 3.689459 3.711450 3.715013 3.748594
## [5,] 3.256717 3.402131 3.673282 3.696853 3.770339 3.796290 3.811141 3.823605
## [6,] 3.501768 3.503807 3.571807 3.728724 3.873347 3.918159 3.927282 3.936384
## [7,] 3.095164 3.098691 3.379533 3.386359 3.394414 3.448939 3.491857 3.653628
## [8,] 2.597415 2.630898 2.737955 2.749611 2.750633 2.907796 2.919281 2.958004
## [9,] 3.795952 3.798232 3.881656 3.921431 4.014543 4.040281 4.065431 4.065983
## [10,] 2.811354 2.848022 2.954676 3.048902 3.049587 3.091457 3.133318 3.156968
## [11,] 3.817037 3.965085 4.018558 4.053127 4.067644 4.130807 4.170941 4.285856
## [12,] 3.588107 3.613840 3.725892 3.837713 4.123938 4.150513 4.193422 4.196769
## [13,] 4.543723 4.616482 4.706540 4.710219 4.730245 4.752023 4.753758 4.779302
## [14,] 3.423775 3.571199 3.661394 3.898330 3.951087 3.974977 3.976495 3.978676
## [15,] 4.032526 4.588079 4.734903 5.160121 5.164474 5.234448 5.237000 5.333032
## [16,] 3.653273 3.850621 3.857852 3.868038 3.929797 3.955489 4.108982 4.131797
## [17,] 2.825372 2.950315 3.119114 3.431811 3.453694 3.478947 3.542899 3.589635
## [18,] 3.095493 3.549029 3.584366 3.636777 3.822604 3.878217 3.879198 3.931665
## [19,] 3.025148 3.255099 3.323617 3.340992 3.388649 3.421620 3.486982 3.559977
## [20,] 3.654629 3.865313 3.918009 3.935926 4.009307 4.065756 4.094772 4.316173
## [,9] [,10]
## [1,] 4.239341 4.243279
## [2,] 4.960942 5.105017
## [3,] 4.047676 4.059920
## [4,] 3.786677 3.796154
## [5,] 3.894711 3.903688
## [6,] 3.937841 3.938787
## [7,] 3.785731 3.820318
## [8,] 2.973164 2.981620
## [9,] 4.073482 4.076129
## [10,] 3.164357 3.198239
## [11,] 4.425686 4.511457
## [12,] 4.241979 4.276869
## [13,] 4.813634 4.844629
## [14,] 3.997601 4.015064
## [15,] 5.377997 5.412234
## [16,] 4.139464 4.149534
## [17,] 3.646179 3.677121
## [18,] 3.963441 4.007886
## [19,] 3.613140 3.651005
## [20,] 4.350931 4.369961This 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 0.248 1 0.641
## 2 0.934 1 0.944
## 3 0.523 1 0.887
## 4 0.943 1 0.990
## 5 0.887 1 0.691
## 6 0.523 1 0.850
## 7 0.823 1 0.662
## 8 0.637 1 0.858
## 9 0.823 1 0.547
## 10 0.677 1 0.929
## # 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 -0.446 -0.204 -0.0761 -0.996
## 2 -0.105 -0.202 -0.202 -0.564
## 3 0.0465 -0.0188 -0.146 -1.15
## 4 -0.411 -0.160 -0.219 -0.816
## 5 -0.180 -0.255 -0.0147 -0.455
## 6 0.632 0.338 -0.451 -0.378
## 7 -0.148 0.816 -0.0442 -1.36
## 8 -0.0749 -0.118 -0.282 -1.01
## 9 -0.458 -0.355 -0.366 0.0316
## 10 0.787 -0.239 0.0882 0.0555
## # 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] 0.232 0.193 0.232 0.256 0.249 ...