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] 649 94 567 138 14 67 458 298 486 231 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 649 557 775 915 790 33 838 773 602 788
## [2,] 94 569 33 713 133 545 791 416 100 98
## [3,] 567 550 724 308 964 247 5 770 754 722
## [4,] 138 889 911 356 273 885 110 810 236 689
## [5,] 14 308 443 707 54 724 86 986 508 229
## [6,] 67 427 166 119 855 699 378 642 798 610
## [7,] 458 163 453 195 138 923 778 627 513 872
## [8,] 298 510 550 964 842 523 962 299 258 32
## [9,] 486 748 995 912 768 370 71 405 986 342
## [10,] 231 526 230 414 801 471 539 105 247 974
## [11,] 62 472 982 683 594 916 59 506 46 633
## [12,] 199 34 691 816 711 708 712 519 174 149
## [13,] 644 998 853 962 575 367 953 522 1000 620
## [14,] 229 575 42 522 193 962 134 964 550 995
## [15,] 418 846 905 657 48 362 615 350 521 393
## [16,] 979 859 372 606 984 467 799 117 636 494
## [17,] 810 138 911 129 930 498 504 647 195 303
## [18,] 38 364 462 829 978 854 637 111 917 498
## [19,] 508 572 412 768 28 359 929 707 509 993
## [20,] 163 275 195 453 355 403 764 513 138 692## num [1:1000, 1:30] 4.31 3.85 3.6 3.66 3.06 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 4.310167 5.096301 5.203686 5.312953 5.387677 5.441803 5.561407 5.577394
## [2,] 3.849542 3.872867 4.159745 4.359839 4.386872 4.387797 4.409507 4.488994
## [3,] 3.597885 3.627223 3.655385 3.852221 3.936064 3.977396 4.036954 4.141474
## [4,] 3.657222 4.090961 4.222360 4.228240 4.448111 4.578400 4.595127 4.601476
## [5,] 3.061246 3.470828 3.485061 3.543151 3.550177 3.564391 3.607025 3.639282
## [6,] 3.880574 5.335489 5.718757 5.897403 5.927919 6.194792 6.335815 6.551413
## [7,] 3.024043 3.036909 3.067011 3.174962 3.328766 3.401148 3.418359 3.437772
## [8,] 2.668782 2.852806 2.889047 2.959003 3.051892 3.231673 3.357158 3.375428
## [9,] 3.666589 3.836659 3.850942 3.961121 3.997233 4.151868 4.230629 4.235298
## [10,] 3.160703 3.532920 3.536769 3.702470 3.728523 3.842336 3.855961 3.876903
## [11,] 4.590500 5.118697 5.793105 5.869661 5.872313 6.157258 6.158341 6.166119
## [12,] 3.331935 3.548807 3.552619 3.693053 3.739082 3.872594 3.971408 3.984146
## [13,] 2.695595 2.737319 2.886617 2.987053 3.049882 3.061995 3.090920 3.101901
## [14,] 2.277209 2.644167 2.663104 2.810516 2.825349 2.835163 2.864089 2.879802
## [15,] 4.037374 4.387087 4.626051 5.029371 5.034855 5.083421 5.125432 5.158505
## [16,] 6.741890 7.008756 7.031263 7.277038 7.533316 8.187366 8.223200 8.282400
## [17,] 2.533157 2.867010 3.214327 3.572627 3.637745 3.643436 3.653801 3.696293
## [18,] 3.494389 3.631369 3.891933 3.901637 4.194784 4.352691 4.460457 4.470466
## [19,] 3.807383 3.968494 4.077283 4.176180 4.200089 4.281042 4.286627 4.321288
## [20,] 3.190878 3.598313 3.605968 3.737485 3.807738 3.855312 4.041281 4.049655
## [,9] [,10]
## [1,] 5.689126 5.773673
## [2,] 4.493017 4.494916
## [3,] 4.149979 4.243515
## [4,] 4.605055 4.623874
## [5,] 3.643701 3.687836
## [6,] 6.582609 6.690902
## [7,] 3.465128 3.466670
## [8,] 3.382220 3.399978
## [9,] 4.262868 4.271356
## [10,] 3.881098 3.914490
## [11,] 6.224886 6.260775
## [12,] 3.998507 4.014903
## [13,] 3.102828 3.108622
## [14,] 2.886320 2.887425
## [15,] 5.167399 5.167750
## [16,] 8.492539 8.537099
## [17,] 3.749424 3.779568
## [18,] 4.476576 4.545089
## [19,] 4.327995 4.330380
## [20,] 4.058931 4.105608This 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 0.947 1 0.998
## 2 0.947 0.995 0.998
## 3 0.650 0.995 1
## 4 0.784 0.995 1
## 5 0.784 1 0.998
## 6 0.947 0.995 0.975
## 7 0.784 0.995 1
## 8 0.828 0.995 0.998
## 9 0.947 1 1
## 10 0.855 0.995 0.975
## # 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 -0.325 -0.320 -0.906 -0.460
## 2 -0.110 -0.204 -0.00716 0.176
## 3 -0.0699 -0.221 0.959 0.396
## 4 -0.234 -0.0633 -0.104 -0.579
## 5 -0.171 -0.126 -0.0613 0.187
## 6 0.103 -0.250 0.368 0.146
## 7 -0.00931 -0.0592 -0.430 0.207
## 8 -0.180 -0.255 -0.0147 -0.455
## 9 -0.00101 -0.182 -0.0879 0.411
## 10 -0.869 -0.427 0.197 -1.07
## # 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] 0.168 0.214 0.237 0.216 0.263 ...