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] 125 783 191 294 580 414 169 126 225 545 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 125 977 252 384 306 500 191 676 330 974
## [2,] 783 691 290 277 79 751 833 661 759 652
## [3,] 191 201 583 371 790 373 113 63 633 53
## [4,] 294 790 237 229 157 155 746 768 108 886
## [5,] 580 870 15 830 581 59 764 345 782 115
## [6,] 414 746 568 958 237 826 89 253 449 942
## [7,] 169 712 547 450 245 401 915 447 776 761
## [8,] 126 887 257 89 297 683 513 637 580 368
## [9,] 225 750 538 440 634 870 198 115 768 201
## [10,] 545 459 367 233 661 102 320 490 310 40
## [11,] 306 321 826 843 693 863 620 855 12 372
## [12,] 904 516 786 615 577 850 929 552 732 808
## [13,] 894 34 393 47 98 844 437 52 365 622
## [14,] 376 747 716 368 568 843 692 621 485 919
## [15,] 642 981 870 568 5 581 830 115 750 527
## [16,] 737 684 233 823 727 964 928 193 535 2
## [17,] 290 256 332 661 579 343 751 298 816 755
## [18,] 838 605 261 79 727 290 535 403 87 953
## [19,] 375 743 858 818 278 761 232 116 576 584
## [20,] 901 257 397 126 394 263 297 655 681 632## num [1:1000, 1:30] 3.45 2.73 3.64 3.76 2.58 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.449080 4.050947 4.052120 4.224166 4.354567 4.379335 4.388616 4.409965
## [2,] 2.726731 2.818536 3.048675 3.281805 3.343012 3.425534 3.571135 3.599752
## [3,] 3.637631 4.045851 4.369343 4.394792 4.481937 4.545654 4.603009 4.651798
## [4,] 3.755041 3.760528 4.192822 4.335475 4.354378 4.394360 4.411843 4.465548
## [5,] 2.579818 2.747512 2.933224 2.972554 3.011654 3.056611 3.059462 3.222036
## [6,] 3.549076 3.657143 3.755292 3.771055 3.861940 3.881868 4.065656 4.101676
## [7,] 3.756475 3.958137 3.981918 3.998771 4.028293 4.057891 4.090333 4.314644
## [8,] 2.954442 3.190342 3.219256 3.434119 3.519632 3.542688 3.640804 3.651357
## [9,] 2.756064 3.102051 3.152072 3.208460 3.331179 3.340364 3.380470 3.409709
## [10,] 3.662806 3.745176 3.818838 3.835806 3.862861 3.970704 4.026576 4.103007
## [11,] 3.348207 3.380156 3.399359 3.590860 3.614056 3.635727 3.640107 3.650910
## [12,] 2.382848 2.787093 2.880935 2.887437 2.911389 2.972881 2.973513 3.018678
## [13,] 4.076361 4.139264 4.330786 4.337176 4.391363 4.516196 4.582723 4.620931
## [14,] 2.951655 3.014610 3.126766 3.162042 3.204783 3.226841 3.277175 3.345964
## [15,] 2.424238 2.700455 2.844470 2.859183 2.933224 2.956409 3.070859 3.104648
## [16,] 3.708425 4.258050 4.305388 4.807121 5.064622 5.106599 5.161567 5.424645
## [17,] 3.565462 3.595170 3.661371 3.822280 3.836570 3.837056 3.846906 3.864883
## [18,] 3.116733 3.116881 3.448237 3.629561 3.632146 3.771961 3.913452 3.922810
## [19,] 2.183445 2.467871 2.656266 2.840044 2.873438 2.878373 2.903163 2.956495
## [20,] 2.707680 2.821755 2.962524 3.292698 3.319018 3.340254 3.392029 3.418958
## [,9] [,10]
## [1,] 4.417019 4.467850
## [2,] 3.638666 3.642188
## [3,] 4.670793 4.692810
## [4,] 4.574524 4.599718
## [5,] 3.231998 3.253966
## [6,] 4.114466 4.145067
## [7,] 4.357567 4.401240
## [8,] 3.673408 3.675400
## [9,] 3.429700 3.443903
## [10,] 4.175135 4.234616
## [11,] 3.702436 3.751228
## [12,] 3.036737 3.057822
## [13,] 4.624479 4.665781
## [14,] 3.369577 3.371839
## [15,] 3.146414 3.168111
## [16,] 5.459953 5.464197
## [17,] 3.873455 3.877967
## [18,] 3.932644 4.062990
## [19,] 3.022827 3.027098
## [20,] 3.424335 3.521479This 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 0.989 0.908 0.873
## 2 0.989 0.951 0.844
## 3 0.989 0.951 0.844
## 4 0.989 0.906 0.893
## 5 0.993 0.930 1
## 6 0.989 0.885 0.964
## 7 0.989 0.997 1
## 8 0.989 0.970 0.685
## 9 0.989 0.797 0.873
## 10 0.989 0.904 0.495
## # 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 -0.0595 -0.264 -0.454 -1.16
## 2 -0.146 -0.139 -0.167 -0.937
## 3 -0.00552 -0.187 -0.142 -1.89
## 4 -0.278 -0.00435 -0.131 -1.06
## 5 -0.158 -0.0951 -0.235 -0.369
## 6 -0.843 -0.326 -0.320 -0.157
## 7 -0.184 -0.00776 -0.235 -0.850
## 8 -0.116 -0.0150 -0.0765 -0.465
## 9 -0.0179 -0.0305 -0.663 -0.897
## 10 -0.168 -0.0463 -0.177 -0.181
## # 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] 0.221 0.267 0.213 0.219 0.299 ...