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] 119 97 696 943 170 33 726 231 631 411 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 119 186 664 97 428 630 29 980 949 594
## [2,] 97 714 352 726 133 205 7 949 630 493
## [3,] 696 369 527 140 744 253 443 119 320 714
## [4,] 943 51 891 523 420 328 433 374 494 52
## [5,] 170 17 184 435 275 850 557 928 319 623
## [6,] 33 71 964 885 485 196 56 51 825 930
## [7,] 726 442 27 744 185 834 97 725 207 154
## [8,] 231 643 351 753 450 815 820 393 467 458
## [9,] 631 675 822 258 413 652 676 989 878 262
## [10,] 411 271 420 104 176 750 151 117 195 561
## [11,] 834 136 910 702 567 701 251 294 401 613
## [12,] 525 329 687 963 173 858 903 494 591 473
## [13,] 447 502 485 962 71 514 196 32 761 312
## [14,] 413 631 989 337 244 808 262 565 764 542
## [15,] 356 217 947 785 458 254 66 698 259 93
## [16,] 185 624 11 593 776 21 538 64 974 499
## [17,] 783 209 578 928 727 435 275 978 855 882
## [18,] 439 815 376 307 338 80 790 246 678 231
## [19,] 565 334 14 262 890 878 232 631 265 141
## [20,] 938 701 645 985 905 705 834 446 997 955## num [1:1000, 1:30] 4.86 3.16 3.5 3.22 2.81 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 4.862226 4.930693 4.975502 4.982312 5.150431 5.219101 5.220080 5.220268
## [2,] 3.159057 3.688778 3.740716 3.797004 3.855780 3.891089 3.995146 4.012733
## [3,] 3.496553 3.522506 3.559725 3.707276 3.808543 3.821651 3.969425 4.045849
## [4,] 3.221538 3.651744 3.735856 3.857558 3.867068 3.914426 4.048233 4.084207
## [5,] 2.806825 2.998118 3.415443 3.459202 3.573208 3.577782 3.586295 3.620091
## [6,] 3.166559 3.241059 3.314776 3.318765 3.333096 3.340232 3.350924 3.386908
## [7,] 3.130829 3.256615 3.325420 3.432159 3.510688 3.622510 3.628469 3.643350
## [8,] 4.543026 4.554202 4.578876 4.659204 4.707463 4.748999 4.752195 4.833016
## [9,] 3.520498 3.598385 3.600803 3.817606 3.921847 3.932091 3.947861 4.035223
## [10,] 3.707049 3.725396 3.817305 4.086391 4.178302 4.306932 4.366362 4.408984
## [11,] 2.839993 2.905517 2.933055 2.955616 3.063677 3.084615 3.090575 3.118784
## [12,] 3.263144 3.501768 3.571807 3.833117 3.867082 3.918159 3.927282 3.936384
## [13,] 3.174668 3.233375 3.262967 3.285306 3.380307 3.380331 3.411497 3.526652
## [14,] 3.391197 4.062841 4.081626 4.145820 4.230996 4.241192 4.241636 4.273084
## [15,] 2.046750 2.735375 2.994343 3.030267 3.109112 3.114050 3.122590 3.148475
## [16,] 2.907990 3.349961 3.497916 3.535462 3.562311 3.653628 3.661115 3.726571
## [17,] 2.532573 2.548095 2.692719 2.721244 2.791295 2.798292 2.829769 2.843118
## [18,] 3.968356 3.991153 4.477519 4.547056 4.579580 4.596414 4.605214 4.665375
## [19,] 3.746514 4.233413 4.336576 4.432759 4.459076 4.489870 4.493819 4.707209
## [20,] 3.039226 3.156356 3.183396 3.195779 3.215493 3.307687 3.434380 3.484379
## [,9] [,10]
## [1,] 5.291917 5.389369
## [2,] 4.108843 4.119357
## [3,] 4.053177 4.115151
## [4,] 4.142253 4.192146
## [5,] 3.629711 3.654687
## [6,] 3.494126 3.507926
## [7,] 3.654135 3.698499
## [8,] 4.884382 4.938888
## [9,] 4.052411 4.054034
## [10,] 4.536312 4.565656
## [11,] 3.167195 3.212876
## [12,] 3.937841 3.938787
## [13,] 3.537164 3.611575
## [14,] 4.285681 4.306341
## [15,] 3.172517 3.250618
## [16,] 3.813664 3.864313
## [17,] 2.853085 2.936514
## [18,] 4.733855 4.770703
## [19,] 4.762073 4.864674
## [20,] 3.498521 3.524622This 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 0.949 0.533
## 2 0.994 0.995 0.785
## 3 0.922 0.985 1
## 4 0.930 1 0.972
## 5 0.958 1 0.565
## 6 1 1 0.972
## 7 1 0.985 1
## 8 0.907 0.985 1
## 9 1 0.985 0.781
## 10 0.921 1 0.907
## # 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 -0.0957 -0.176 -0.199 -0.493
## 2 -0.174 -0.613 -0.0850 -0.783
## 3 -0.674 -0.635 -0.565 -0.594
## 4 0.697 -0.115 0.278 -0.896
## 5 -0.0403 -0.496 -0.108 -1.01
## 6 -0.202 -0.0294 -0.289 -0.534
## 7 -0.289 -0.490 -0.121 0.646
## 8 -0.741 -0.805 -1.12 -1.02
## 9 -0.00552 -0.187 -0.142 -1.89
## 10 0.372 -0.138 0.107 0.194
## # 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] 0.183 0.234 0.242 0.24 0.265 ...