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] 898 107 790 756 790 796 476 907 846 282 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 898 938 87 892 544 894 742 994 192 155
## [2,] 107 194 723 593 906 322 876 523 23 39
## [3,] 790 8 907 57 210 308 802 5 454 360
## [4,] 756 657 878 323 344 781 661 520 810 640
## [5,] 790 3 958 454 91 8 907 308 357 210
## [6,] 796 215 900 539 581 289 25 884 147 708
## [7,] 476 908 520 765 678 120 953 456 962 957
## [8,] 907 790 3 451 690 34 169 824 308 526
## [9,] 846 86 722 928 191 436 557 823 569 626
## [10,] 282 107 71 360 876 369 593 194 23 726
## [11,] 86 559 104 503 392 157 626 580 170 185
## [12,] 340 741 60 385 430 510 279 93 622 186
## [13,] 977 647 728 181 52 401 115 361 512 879
## [14,] 616 452 716 695 24 652 858 302 170 928
## [15,] 718 229 641 231 434 235 477 864 333 701
## [16,] 908 499 875 957 274 120 38 96 520 392
## [17,] 267 898 747 275 662 825 892 154 634 707
## [18,] 893 711 566 971 85 888 986 877 395 221
## [19,] 971 138 494 954 912 840 34 986 314 711
## [20,] 870 722 375 312 823 883 56 357 733 476# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 3.21 3.31 2.89 3.4 3.65 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.207374 3.480259 3.599337 3.607481 3.731712 3.736488 3.753828 3.769329
## [2,] 3.313186 3.854078 4.013864 4.044330 4.101227 4.117041 4.142000 4.163028
## [3,] 2.885774 2.974989 3.139709 3.382013 3.487800 3.513848 3.579100 3.678958
## [4,] 3.400857 3.406403 3.505638 3.555033 3.636335 3.639760 3.655267 3.796365
## [5,] 3.654094 3.678958 3.875789 3.893263 4.023613 4.099897 4.116374 4.149045
## [6,] 2.852235 3.265534 3.289348 3.516531 3.597213 3.623875 3.689459 3.725871
## [7,] 2.690266 3.042903 3.085945 3.169237 3.320257 3.320706 3.329156 3.339701
## [8,] 2.453201 2.967051 2.974989 3.104128 3.167093 3.189332 3.191709 3.206891
## [9,] 3.721614 3.842150 3.860959 3.876880 3.900910 3.921677 3.937205 3.963566
## [10,] 3.732413 4.982312 5.064902 5.093407 5.219101 5.220080 5.291917 5.315915
## [11,] 3.327581 3.513273 3.533073 3.561370 3.676124 3.699885 3.785623 3.792353
## [12,] 4.209103 4.245753 4.393115 4.451718 4.656001 4.711475 4.724508 4.775084
## [13,] 3.220076 3.402167 3.425279 3.534260 3.593361 3.627505 3.660338 3.660755
## [14,] 3.510082 3.639932 3.656104 3.679785 3.716740 3.720733 3.912308 3.913865
## [15,] 3.562037 3.579958 3.713589 3.732426 3.750626 3.786793 3.796046 3.819036
## [16,] 2.751027 3.074504 3.120326 3.189666 3.206119 3.285611 3.315812 3.326392
## [17,] 2.183445 2.467871 2.548933 2.564574 2.624695 2.698236 2.840044 2.873438
## [18,] 3.831822 3.974534 4.049181 4.149865 4.341237 4.653371 4.664139 4.741421
## [19,] 4.181244 4.308902 4.516977 4.659617 4.740539 4.981736 4.982850 4.989053
## [20,] 3.629077 3.643743 3.653273 3.743585 3.747092 3.820737 3.850621 3.857852
## [,9] [,10]
## [1,] 3.811161 3.855312
## [2,] 4.191601 4.197095
## [3,] 3.715603 3.740663
## [4,] 3.800983 3.810580
## [5,] 4.195495 4.208327
## [6,] 3.764945 3.794277
## [7,] 3.392670 3.421600
## [8,] 3.218411 3.228432
## [9,] 3.970520 4.036965
## [10,] 5.333945 5.387931
## [11,] 3.793545 3.822110
## [12,] 4.804957 4.851907
## [13,] 3.724096 3.762578
## [14,] 3.916840 3.924472
## [15,] 3.834848 3.871313
## [16,] 3.330879 3.334825
## [17,] 2.878373 2.878891
## [18,] 4.839777 4.985220
## [19,] 5.039231 5.049009
## [20,] 3.971378 3.981675This 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.IL… `pCREB(Yb176)Di.IL… `pBTK(Yb171)Di.IL… `pS6(Yb172)Di.IL7…
## <dbl> <dbl> <dbl> <dbl>
## 1 0.972 0.997 0.996 0.873
## 2 0.972 0.997 0.991 0.855
## 3 0.806 0.997 0.952 0.876
## 4 0.753 0.997 0.991 0.851
## 5 0.783 0.997 1 0.851
## 6 0.932 0.997 0.991 0.924
## 7 0.772 0.997 0.931 0.851
## 8 0.923 0.997 0.996 0.851
## 9 0.997 0.997 0.952 0.977
## 10 0.997 0.997 0.996 0.943
## # … 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>, …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(I… `CD3(Cd114)Di`
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -0.104 -0.0145 0.276 -0.288 -0.628
## 2 -0.0498 0.201 0.573 -0.355 -0.0410
## 3 -0.0603 0.798 0.460 -1.21 -0.694
## 4 -0.254 -0.193 -0.121 -0.267 -0.0189
## 5 0.540 -0.181 0.0845 0.266 -0.235
## 6 -0.166 -0.476 -0.304 -1.55 -0.101
## 7 -0.0300 -0.0665 0.406 0.365 -0.135
## 8 -0.204 -0.0232 -0.140 0.0694 -0.123
## 9 -0.0559 -0.157 0.375 -0.539 -0.307
## 10 -0.0905 -0.346 -0.635 -0.202 -0.920
## # … with 20 more rows, and 46 more variables: 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>, …# 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] 0.256 0.234 0.267 0.259 0.231 ...