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] 918 777 989 36 579 908 924 209 189 917 ...
wand.nn[[1]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 918 280 109 116 628 225 792 794 747 579
## [2,] 777 171 500 567 969 294 629 43 998 108
## [3,] 989 788 885 699 11 366 343 829 825 59
## [4,] 36 755 426 55 873 232 867 623 989 790
## [5,] 579 233 750 152 615 287 661 133 901 866
## [6,] 908 625 138 616 755 467 442 57 829 48
## [7,] 924 269 687 62 44 419 304 458 520 845
## [8,] 209 192 76 552 417 744 326 814 680 602
## [9,] 189 139 987 469 364 666 97 847 118 958
## [10,] 917 702 970 774 28 698 656 665 455 769
## [11,] 827 587 885 3 401 572 332 925 1000 651
## [12,] 93 461 767 349 964 535 634 641 954 616
## [13,] 987 545 123 197 720 73 446 591 696 971
## [14,] 43 662 47 617 369 735 960 819 863 431
## [15,] 621 485 463 703 310 537 879 601 734 235
## [16,] 367 456 387 518 180 784 742 471 439 927
## [17,] 592 89 399 372 877 112 78 23 696 214
## [18,] 580 891 963 374 64 623 775 476 41 943
## [19,] 1000 873 421 613 359 77 107 867 875 232
## [20,] 544 135 90 166 483 582 562 186 33 439
# Distance
str(wand.nn[[2]])
## num [1:1000, 1:30] 3.71 2.62 3.38 2.18 4.84 ...
wand.nn[[2]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.708425 4.305388 4.785147 4.914107 5.064622 5.066507 5.106599 5.137192
## [2,] 2.622504 2.752907 2.845141 2.919913 2.956911 2.977494 2.992832 3.073188
## [3,] 3.375307 3.408716 3.483984 3.595286 3.597304 3.630796 3.642016 3.642499
## [4,] 2.183445 2.648219 2.705975 2.818641 2.843641 3.017980 3.057751 3.116323
## [5,] 4.840401 4.939676 5.022750 5.534483 5.752062 5.794363 5.869359 5.908344
## [6,] 3.733845 3.747977 3.978432 4.012787 4.093523 4.099559 4.115680 4.227425
## [7,] 3.043696 3.169950 3.182576 3.241093 3.337072 3.355986 3.410895 3.443274
## [8,] 3.419766 3.493557 4.073705 4.127432 4.301064 4.312950 4.326322 4.359535
## [9,] 4.481757 4.551554 4.622989 4.637174 4.662373 4.697840 4.699965 4.751231
## [10,] 2.765675 2.832318 2.891351 2.893196 2.943991 3.033790 3.098479 3.119748
## [11,] 3.012112 3.165423 3.418581 3.597304 3.604383 3.638785 3.716201 3.806617
## [12,] 4.118989 4.380745 4.443624 4.497634 4.590462 4.645345 4.680896 4.705178
## [13,] 3.014610 3.128950 3.183438 3.273488 3.277175 3.340385 3.369577 3.371839
## [14,] 2.675868 2.773033 2.790624 3.114887 3.160026 3.223892 3.288548 3.315661
## [15,] 3.660582 3.794746 3.832154 3.888612 4.029559 4.061997 4.062962 4.083400
## [16,] 4.671480 4.702873 4.803985 4.938966 5.021473 5.044814 5.082858 5.095593
## [17,] 3.866841 3.941597 4.072078 4.086691 4.148297 4.220570 4.225712 4.338482
## [18,] 3.071717 3.482856 3.696256 3.756475 3.768423 3.830020 3.909412 3.981704
## [19,] 2.958679 2.969214 3.011940 3.039933 3.119796 3.155625 3.205827 3.246120
## [20,] 3.642712 3.649257 3.664541 3.726866 3.745536 3.764372 3.822912 3.829362
## [,9] [,10]
## [1,] 5.161567 5.332888
## [2,] 3.125862 3.178267
## [3,] 3.727824 3.758921
## [4,] 3.175258 3.197433
## [5,] 5.991524 6.023610
## [6,] 4.444663 4.465577
## [7,] 3.469314 3.535463
## [8,] 4.383109 4.455883
## [9,] 4.795196 4.822890
## [10,] 3.132981 3.162675
## [11,] 3.809217 3.816823
## [12,] 4.783132 4.794852
## [13,] 3.379519 3.400905
## [14,] 3.329536 3.351647
## [15,] 4.195691 4.276164
## [16,] 5.175700 5.176622
## [17,] 4.351993 4.368190
## [18,] 4.016532 4.063349
## [19,] 3.279501 3.300440
## [20,] 3.915433 3.917260
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 x 34
## `pCrkL(Lu175)Di~ `pCREB(Yb176)Di~ `pBTK(Yb171)Di.~ `pS6(Yb172)Di.I~
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0.961 0.981 0.970
## 2 1 1 0.952 0.874
## 3 1 1 1 0.710
## 4 1 1 0.821 0.976
## 5 1 1 0.927 0.945
## 6 1 1 0.985 0.966
## 7 1 1 0.970 0.833
## 8 1 0.858 0.769 0.848
## 9 1 1 0.952 0.966
## 10 1 1 0.606 0.869
## # ... 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>, `pCREB(Yb176)Di.IL7.change` <dbl>,
## # `pBTK(Yb171)Di.IL7.change` <dbl>, `pS6(Yb172)Di.IL7.change` <dbl>,
## # `cPARP(La139)Di.IL7.change` <dbl>, `pPLCg2(Pr141)Di.IL7.change` <dbl>,
## # `pSrc(Nd144)Di.IL7.change` <dbl>, `Ki67(Sm152)Di.IL7.change` <dbl>,
## # `pErk12(Gd155)Di.IL7.change` <dbl>, `pSTAT3(Gd158)Di.IL7.change` <dbl>,
## # `pAKT(Tb159)Di.IL7.change` <dbl>, `pBLNK(Gd160)Di.IL7.change` <dbl>,
## # `pP38(Tm169)Di.IL7.change` <dbl>, `pSTAT5(Nd150)Di.IL7.change` <dbl>,
## # `pSyk(Dy162)Di.IL7.change` <dbl>, `tIkBa(Er166)Di.IL7.change` <dbl>,
## # IL7.fraction.cond.2 <dbl>, density <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 x 51
## `CD3(Cd110)Di` `CD3(Cd111)Di` `CD3(Cd112)Di` `CD235-61-7-15(~ `CD3(Cd114)Di`
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -0.173 -0.127 -0.149 -0.246 -0.175
## 2 -0.140 -0.162 -0.153 0.922 -0.280
## 3 -0.124 0.525 0.280 -0.231 -0.250
## 4 -0.0253 0.406 -0.237 0.565 -0.799
## 5 -0.149 -0.297 -0.542 0.689 -0.0541
## 6 -0.0111 -0.248 0.302 0.587 -0.286
## 7 -0.113 -0.0797 0.697 0.904 -0.119
## 8 -0.171 -0.399 -0.0564 -0.580 -0.215
## 9 -0.0113 -0.0986 -0.181 0.514 -0.0744
## 10 -0.00750 -0.589 -0.810 0.738 0.334
## # ... 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>,
## # `CD40(Er170)Di` <dbl>, `CD33(Yb173)Di` <dbl>, `HLA-DR(Yb174)Di` <dbl>,
## # Time <dbl>, Cell_length <dbl>, `cPARP(La139)Di` <dbl>,
## # `pPLCg2(Pr141)Di` <dbl>, `pSrc(Nd144)Di` <dbl>, `pSTAT5(Nd150)Di` <dbl>,
## # `Ki67(Sm152)Di` <dbl>, `pErk12(Gd155)Di` <dbl>, `pSTAT3(Gd158)Di` <dbl>,
## # `pAKT(Tb159)Di` <dbl>, `pBLNK(Gd160)Di` <dbl>, `pSyk(Dy162)Di` <dbl>,
## # `tIkBa(Er166)Di` <dbl>, `pP38(Tm169)Di` <dbl>, `pBTK(Yb171)Di` <dbl>,
## # `pS6(Yb172)Di` <dbl>, `pCrkL(Lu175)Di` <dbl>, `pCREB(Yb176)Di` <dbl>,
## # `DNA1(Ir191)Di` <dbl>, `DNA2(Ir193)Di` <dbl>, `Viability1(Pt195)Di` <dbl>,
## # `Viability2(Pt196)Di` <dbl>, wanderlust <dbl>, condition <chr>
# 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] 0.188 0.309 0.261 0.309 0.165 ...