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] 340 569 680 873 194 593 268 861 606 300 ...
wand.nn[[1]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 340 276 449 632 721 652 241 98 637 413
## [2,] 569 215 593 203 710 515 394 550 626 143
## [3,] 680 397 217 804 792 293 713 961 879 736
## [4,] 873 664 217 680 119 195 622 814 529 609
## [5,] 194 452 653 104 353 582 435 232 320 973
## [6,] 593 957 203 975 637 22 580 906 720 595
## [7,] 268 717 219 562 397 945 757 293 764 638
## [8,] 861 942 38 352 679 420 498 839 433 964
## [9,] 606 757 726 609 252 541 505 464 4 999
## [10,] 300 992 600 289 770 537 504 925 987 454
## [11,] 455 786 122 660 503 584 805 117 480 833
## [12,] 319 786 443 685 553 32 440 122 615 805
## [13,] 141 829 418 244 654 779 451 926 781 372
## [14,] 607 392 919 882 822 975 591 507 436 658
## [15,] 685 698 220 492 116 518 595 964 531 534
## [16,] 370 843 185 994 35 846 583 59 41 278
## [17,] 963 322 944 718 608 451 466 25 837 864
## [18,] 50 957 447 615 758 763 75 201 617 959
## [19,] 76 238 293 719 743 365 871 361 432 987
## [20,] 891 745 691 828 388 37 807 876 118 110
# Distance
str(wand.nn[[2]])
## num [1:1000, 1:30] 3.02 3.3 2.47 2.88 4.05 ...
wand.nn[[2]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.018629 3.031373 3.079598 3.182182 3.190289 3.270748 3.311817 3.328056
## [2,] 3.300249 3.364253 3.408951 3.410414 3.434031 3.439393 3.477720 3.530245
## [3,] 2.469631 2.539880 2.551225 2.670193 2.722951 2.789880 2.842039 2.967493
## [4,] 2.884935 3.034378 3.075735 3.093143 3.129549 3.252635 3.268616 3.300838
## [5,] 4.045851 4.171463 4.480685 4.521321 4.545654 4.591897 4.603009 4.651798
## [6,] 2.715516 2.818171 3.031190 3.031923 3.071348 3.181328 3.254148 3.291665
## [7,] 3.244079 3.849132 4.107612 4.123144 4.597226 4.665406 4.742255 4.899291
## [8,] 2.880464 2.975964 2.992927 3.134780 3.149033 3.209440 3.351930 3.369912
## [9,] 3.200608 3.556273 3.599590 3.632178 3.644647 3.651096 3.681008 3.797230
## [10,] 2.370303 3.255099 3.340992 3.388649 3.473889 3.524301 3.559977 3.613140
## [11,] 3.672853 3.877440 4.191101 4.233251 4.250197 4.308944 4.417027 4.431338
## [12,] 3.786465 3.840168 3.903663 3.909107 3.990217 3.995549 4.123284 4.136472
## [13,] 5.449527 6.001808 6.480283 6.545356 6.729681 6.852658 6.872787 7.032034
## [14,] 3.400413 3.497519 3.581049 3.614318 3.734166 3.760073 3.790119 3.792057
## [15,] 2.649857 2.839818 2.862886 3.061995 3.185397 3.191899 3.209112 3.237000
## [16,] 3.719502 4.187192 4.364447 4.372877 4.395679 4.407047 4.466695 4.473370
## [17,] 3.824482 4.020163 4.291948 4.329155 4.420129 4.502649 4.511740 4.530550
## [18,] 3.284251 3.747092 3.929797 3.964912 3.971378 3.973010 3.995078 4.044295
## [19,] 3.084338 3.222549 3.367862 3.374281 3.427643 3.432455 3.635662 3.717587
## [20,] 3.647931 3.910030 3.994346 4.355517 4.392176 4.588122 4.601240 4.661430
## [,9] [,10]
## [1,] 3.335644 3.412268
## [2,] 3.574460 3.619868
## [3,] 2.989109 3.013243
## [4,] 3.315502 3.385059
## [5,] 4.657912 4.670793
## [6,] 3.338679 3.359385
## [7,] 4.938847 4.947225
## [8,] 3.573674 3.599806
## [9,] 3.803833 3.873390
## [10,] 3.686943 3.708585
## [11,] 4.456726 4.488361
## [12,] 4.189893 4.233011
## [13,] 7.064499 7.080149
## [14,] 3.860964 3.907350
## [15,] 3.259374 3.323875
## [16,] 4.603207 4.617451
## [17,] 4.533160 4.560717
## [18,] 4.071243 4.081719
## [19,] 3.760651 3.762696
## [20,] 4.747423 4.753867
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 × 34
## `pCrkL(Lu175)Di.IL7.qvalue` pCREB(Yb176)Di.IL7.qvalu…¹ pBTK(Yb171)Di.IL7.qv…²
## <dbl> <dbl> <dbl>
## 1 1 0.982 0.989
## 2 1 0.982 0.978
## 3 1 0.998 0.978
## 4 1 0.982 0.989
## 5 1 0.982 0.985
## 6 1 0.896 0.978
## 7 1 0.982 0.989
## 8 1 0.982 1
## 9 1 0.982 0.978
## 10 1 0.941 0.989
## # 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 -0.202 -0.0294 -0.289 -0.534
## 2 -0.142 -0.171 -0.0665 0.220
## 3 -0.252 -0.144 -0.0596 -0.140
## 4 -0.125 -0.148 -0.00157 0.554
## 5 -0.00887 0.180 -0.0171 0.365
## 6 -0.161 -0.470 -0.558 0.230
## 7 -0.326 -0.358 1.02 0.296
## 8 -0.108 -0.00340 -0.0878 -1.03
## 9 -0.0416 -0.182 -0.232 -1.03
## 10 -0.0759 -0.478 0.314 -0.159
## # 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] 0.287 0.267 0.323 0.288 0.211 ...