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] 647 892 652 230 231 825 284 679 239 65 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 647 969 582 941 1000 257 843 589 625 695
## [2,] 892 771 450 577 835 601 830 484 681 265
## [3,] 652 719 360 56 704 162 481 309 670 744
## [4,] 230 603 801 338 402 907 243 715 496 700
## [5,] 231 754 411 963 273 145 230 412 530 676
## [6,] 825 835 267 503 894 830 536 379 242 165
## [7,] 284 278 360 782 122 839 704 905 719 907
## [8,] 679 775 238 84 132 783 308 697 191 234
## [9,] 239 356 786 413 201 923 236 946 867 146
## [10,] 65 820 698 776 9 212 311 839 763 968
## [11,] 260 574 854 663 691 951 203 502 724 28
## [12,] 617 763 237 862 753 908 545 877 917 375
## [13,] 1000 235 647 35 332 582 589 172 356 860
## [14,] 949 561 988 518 896 683 117 938 639 711
## [15,] 198 846 545 588 617 303 586 307 725 474
## [16,] 113 591 945 577 560 937 892 993 450 656
## [17,] 841 959 169 738 664 335 391 47 202 546
## [18,] 605 399 722 410 249 374 466 586 501 327
## [19,] 141 723 414 978 885 936 952 739 51 513
## [20,] 77 267 675 427 349 524 394 787 253 656# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 2.95 2.81 3.52 4.02 2.69 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 2.953928 3.318031 3.472346 3.605232 3.619428 3.632531 3.720944 3.751212
## [2,] 2.808518 2.889098 3.166375 3.285978 3.290163 3.348788 3.396739 3.419711
## [3,] 3.522686 3.535084 3.620843 3.706292 3.745903 3.781679 3.814151 3.904025
## [4,] 4.020063 4.071853 4.076856 4.272803 4.321498 4.365737 4.415725 4.430108
## [5,] 2.690266 3.146843 3.165143 3.254884 3.265306 3.270347 3.271496 3.273412
## [6,] 2.994517 3.033252 3.037809 3.087345 3.143790 3.172041 3.321594 3.338371
## [7,] 3.215614 3.715483 4.269376 4.396100 4.592158 4.668892 4.735616 4.783465
## [8,] 4.110670 4.276462 4.326114 4.336576 4.381202 4.509305 4.545038 4.549587
## [9,] 3.331167 3.550177 3.553320 3.565500 3.607025 3.639282 3.687836 3.698943
## [10,] 4.518016 4.651768 5.611126 5.624392 5.637483 5.647521 5.703896 5.744498
## [11,] 3.417272 3.775577 3.830728 4.315870 4.484796 4.617598 4.639104 4.670535
## [12,] 4.181574 4.639681 4.659546 5.078659 5.102001 5.177237 5.255882 5.292716
## [13,] 3.735717 3.868038 3.902536 4.004290 4.026934 4.030179 4.096880 4.204724
## [14,] 2.644720 2.818289 3.022139 3.071176 3.107513 3.124147 3.231125 3.308493
## [15,] 4.192058 4.199042 4.338093 4.365472 4.380111 4.395456 4.500361 4.699085
## [16,] 2.718018 3.048336 3.268406 3.330299 3.434021 3.442078 3.445528 3.455785
## [17,] 5.277550 5.322125 5.372345 5.380729 5.407068 5.442963 5.544765 5.550085
## [18,] 3.652176 4.199195 4.441109 4.449110 4.477519 4.547056 4.596414 4.733855
## [19,] 4.048453 4.331377 4.763687 4.834264 4.968252 4.996791 5.014418 5.065214
## [20,] 3.514509 3.544002 3.652081 3.765027 3.845087 3.865452 3.912850 3.968964
## [,9] [,10]
## [1,] 3.784599 3.909524
## [2,] 3.428941 3.435176
## [3,] 3.920367 3.966544
## [4,] 4.521247 4.573941
## [5,] 3.320257 3.329156
## [6,] 3.380381 3.381674
## [7,] 4.785975 4.798028
## [8,] 4.587822 4.607493
## [9,] 3.743360 3.757137
## [10,] 5.752139 5.759549
## [11,] 4.775378 4.780274
## [12,] 5.372905 5.383269
## [13,] 4.245660 4.250724
## [14,] 3.318214 3.323475
## [15,] 4.703711 4.707577
## [16,] 3.483882 3.485682
## [17,] 5.610981 5.626681
## [18,] 4.788771 4.796307
## [19,] 5.089879 5.107419
## [20,] 3.987274 4.017990This 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 0.908 0.727 1
## 2 0.908 0.762 0.826
## 3 0.975 0.727 0.806
## 4 0.995 0.982 0.929
## 5 0.908 0.870 0.477
## 6 0.999 0.982 0.922
## 7 0.975 0.964 1
## 8 0.975 0.727 0.983
## 9 0.975 0.990 0.781
## 10 0.975 0.936 0.938
## # 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 0.689 1.67 1.65 0.479
## 2 -0.0619 0.102 1.21 0.496
## 3 -0.337 0.202 -0.859 0.888
## 4 -0.871 -0.433 -0.124 0.780
## 5 -0.110 0.373 0.316 0.891
## 6 -0.326 1.53 -0.579 0.724
## 7 -0.441 -0.274 -0.333 -0.704
## 8 -0.0949 0.966 0.577 -1.15
## 9 0.802 1.13 0.913 -1.45
## 10 -0.0144 -0.262 -0.116 -0.640
## # 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] 0.254 0.28 0.247 0.215 0.294 ...