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] 846 584 891 149 566 112 128 152 76 976 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 846 504 995 948 688 493 887 77 731 547
## [2,] 584 952 917 119 601 498 468 291 700 459
## [3,] 891 943 4 676 771 149 27 750 425 641
## [4,] 149 361 3 438 583 956 83 515 641 943
## [5,] 566 692 882 626 322 730 76 888 486 253
## [6,] 112 535 918 353 981 579 325 540 223 446
## [7,] 128 238 154 157 993 602 513 360 585 266
## [8,] 152 133 499 797 535 954 999 522 784 333
## [9,] 76 119 896 698 204 441 359 297 239 429
## [10,] 976 85 607 502 97 60 718 676 823 832
## [11,] 654 54 484 586 112 722 108 401 490 709
## [12,] 923 953 361 109 873 942 198 690 669 970
## [13,] 373 230 208 925 841 198 484 54 20 635
## [14,] 319 713 903 567 680 629 170 545 591 138
## [15,] 696 845 944 659 159 589 200 512 727 263
## [16,] 498 123 555 601 952 917 429 91 462 337
## [17,] 415 493 938 504 505 89 761 77 715 948
## [18,] 241 384 468 608 479 630 29 318 615 555
## [19,] 808 775 246 701 878 328 741 157 298 275
## [20,] 230 524 784 442 756 198 208 963 23 732## num [1:1000, 1:30] 4.4 3.31 2.53 2.33 3.22 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 4.398798 4.578948 4.627504 4.640163 4.708752 4.783559 5.046358 5.234115
## [2,] 3.306132 3.480990 3.584387 3.655244 3.697649 3.709681 3.756065 3.784321
## [3,] 2.533775 2.582945 2.670389 2.671984 2.709469 2.734650 2.760390 2.777761
## [4,] 2.326419 2.552153 2.670389 2.767063 2.811830 2.876781 2.899300 2.920069
## [5,] 3.221757 3.361387 3.546973 3.550768 3.582753 3.626467 3.626786 3.634353
## [6,] 2.544907 3.250859 3.397247 3.437658 3.565004 3.565658 3.637125 3.729441
## [7,] 3.391197 4.109364 4.116124 4.241636 4.276462 4.285681 4.306341 4.326114
## [8,] 3.344829 3.395116 3.411248 3.412474 3.550818 3.585570 3.591775 3.599950
## [9,] 3.036909 3.091061 3.561725 3.564839 3.611303 3.636292 3.660321 3.694385
## [10,] 3.067959 3.227690 3.528960 3.552619 3.598409 3.685850 3.693053 3.739082
## [11,] 4.013864 4.066876 4.076489 4.101227 4.117041 4.142000 4.157371 4.205308
## [12,] 3.203656 3.545104 3.588573 3.598605 3.627669 3.680677 3.736156 3.737150
## [13,] 3.985561 4.153654 4.163283 4.254944 4.264703 4.374184 4.423634 4.502160
## [14,] 3.468951 3.515847 3.640917 4.036209 4.062286 4.331332 4.381185 4.456790
## [15,] 2.892078 3.092743 3.359153 3.507238 3.608953 3.639802 3.649601 3.658235
## [16,] 2.809988 2.946328 3.107255 3.217970 3.230808 3.244798 3.304969 3.308979
## [17,] 4.737435 4.788924 4.797339 4.807582 4.938139 5.012716 5.105142 5.178775
## [18,] 4.126217 4.368160 4.372758 4.444069 4.471064 4.522414 4.570687 4.653829
## [19,] 2.488492 3.327672 3.339597 3.556047 3.662678 3.712260 3.712295 3.793516
## [20,] 3.560172 3.610449 3.680563 3.793470 3.817142 3.833874 3.837056 3.846906
## [,9] [,10]
## [1,] 5.312010 5.376992
## [2,] 3.850989 3.855120
## [3,] 2.781966 2.790082
## [4,] 2.923723 2.934396
## [5,] 3.719566 3.807734
## [6,] 3.753837 3.777968
## [7,] 4.509305 4.587822
## [8,] 3.617930 3.633145
## [9,] 3.706666 3.713536
## [10,] 3.742533 3.783128
## [11,] 4.227726 4.241303
## [12,] 3.748853 3.779984
## [13,] 4.530583 4.544072
## [14,] 4.483626 4.552660
## [15,] 3.671037 3.701915
## [16,] 3.341227 3.358280
## [17,] 5.308749 5.338287
## [18,] 4.691917 4.706558
## [19,] 4.005549 4.016861
## [20,] 3.864883 3.873455This 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 0.902 1 0.934
## 2 1 1 1
## 3 0.902 1 1
## 4 0.665 1 0.955
## 5 0.930 1 0.692
## 6 0.996 1 0.880
## 7 0.996 1 1
## 8 1 1 0.692
## 9 0.797 1 1
## 10 0.902 1 1
## # 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 -0.0300 -0.0665 0.406 0.365
## 2 -0.0390 -0.100 0.438 -0.509
## 3 -0.0559 -0.157 0.375 -0.539
## 4 -0.136 -0.242 0.532 0.0579
## 5 -0.126 -0.207 0.611 -0.307
## 6 0.189 -0.299 0.639 0.736
## 7 0.862 -0.0213 -0.235 0.232
## 8 -0.0598 -0.0786 -0.276 0.582
## 9 -0.00581 -0.00893 0.254 0.0550
## 10 0.678 0.724 0.488 -0.258
## # 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 0.257 0.342 0.335 0.257 ...