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] 59 9 194 890 846 83 331 314 981 875 ...
wand.nn[[1]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 59 928 714 649 15 943 715 284 925 27
## [2,] 9 350 749 149 689 48 934 455 864 453
## [3,] 194 943 136 27 511 710 284 928 892 56
## [4,] 890 638 422 708 672 364 701 95 496 789
## [5,] 846 667 862 856 201 607 125 297 825 42
## [6,] 83 313 718 579 525 845 835 925 688 486
## [7,] 331 347 128 369 641 421 717 287 899 285
## [8,] 314 592 565 575 251 528 949 912 516 13
## [9,] 981 2 876 934 623 732 48 454 389 864
## [10,] 875 506 458 527 545 132 417 832 590 958
## [11,] 213 449 866 278 858 774 178 983 578 157
## [12,] 820 297 967 632 286 5 415 856 862 387
## [13,] 942 565 189 592 70 8 912 370 683 255
## [14,] 422 133 820 856 725 902 987 890 828 1000
## [15,] 843 497 70 928 184 67 137 565 463 711
## [16,] 163 2 515 953 9 149 738 934 864 811
## [17,] 363 59 1 661 604 566 81 817 649 271
## [18,] 846 346 967 476 772 101 247 856 133 339
## [19,] 487 925 598 835 751 525 592 353 405 898
## [20,] 57 840 497 131 252 488 31 205 626 144
# Distance
str(wand.nn[[2]])
## num [1:1000, 1:30] 3.58 2.66 3.66 3.58 2.31 ...
wand.nn[[2]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.577959 3.828365 3.850349 3.865013 3.866061 3.869837 3.883219 3.900658
## [2,] 2.661384 2.674326 2.816362 2.846001 2.881318 2.931912 2.940155 2.948449
## [3,] 3.656178 3.873374 3.963417 3.998168 4.032484 4.137645 4.147744 4.174068
## [4,] 3.578335 3.581049 3.601583 3.614318 3.638779 3.703243 3.734166 3.735610
## [5,] 2.306681 2.808949 3.124734 3.127478 3.199996 3.220559 3.341306 3.366557
## [6,] 2.370303 3.388649 3.473889 3.486982 3.594484 3.613140 3.651005 3.686943
## [7,] 3.356851 3.359119 3.549906 3.786363 3.876545 3.906281 3.954731 3.985128
## [8,] 2.539880 2.702144 2.722951 2.749710 2.789880 2.806574 2.874492 2.967493
## [9,] 2.266397 2.661384 2.847252 2.858227 2.941750 2.964757 2.986400 3.093295
## [10,] 3.204930 3.438415 3.719250 3.862740 3.894480 3.896668 3.907548 3.919718
## [11,] 3.741816 4.348160 4.356210 4.569023 4.577746 4.637489 4.664614 4.671925
## [12,] 3.517541 3.651199 3.817298 3.860959 3.900910 3.921677 3.951490 3.963566
## [13,] 2.744202 2.887557 2.921166 2.921370 2.979355 2.991175 3.000049 3.001232
## [14,] 2.818500 2.896909 2.917864 2.976726 2.997273 3.005976 3.022129 3.140557
## [15,] 2.931016 3.037768 3.105474 3.230213 3.401931 3.423346 3.530482 3.550263
## [16,] 3.018629 3.079598 3.182182 3.190289 3.412268 3.450229 3.451099 3.482824
## [17,] 3.526604 3.982846 4.241187 4.429282 4.597442 4.597678 4.627651 4.758651
## [18,] 2.810474 2.843630 3.101917 3.197762 3.302193 3.323135 3.329606 3.331071
## [19,] 2.994119 3.411322 3.522647 3.782354 3.818311 3.880291 3.886062 3.922244
## [20,] 3.461351 3.645975 4.444098 4.448529 4.470466 4.543469 4.677396 4.684257
## [,9] [,10]
## [1,] 3.905342 3.912850
## [2,] 2.955938 2.958371
## [3,] 4.223608 4.257679
## [4,] 3.787883 3.790119
## [5,] 3.377360 3.409308
## [6,] 3.706352 3.708585
## [7,] 3.999606 4.052769
## [8,] 2.989109 2.991175
## [9,] 3.138187 3.146696
## [10,] 3.961922 3.967638
## [11,] 4.774678 4.807101
## [12,] 3.970520 4.021386
## [13,] 3.073528 3.275839
## [14,] 3.164171 3.188933
## [15,] 3.573400 3.605165
## [16,] 3.515227 3.524066
## [17,] 4.760091 4.780274
## [18,] 3.333952 3.349297
## [19,] 4.096030 4.122375
## [20,] 4.717027 4.830975
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 × 34
## `pCrkL(Lu175)Di.IL7.qvalue` pCREB(Yb176)Di.IL7.qvalu…¹ pBTK(Yb171)Di.IL7.qv…²
## <dbl> <dbl> <dbl>
## 1 1 1 0.987
## 2 1 1 0.993
## 3 1 1.00 0.993
## 4 1 1 0.973
## 5 1 1.00 0.866
## 6 1 1.00 0.882
## 7 1 1 1
## 8 1 1 0.927
## 9 1 1 0.976
## 10 1 1 0.993
## # 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 -0.194 -0.262 -0.0632 0.868
## 2 -0.102 -0.118 -0.230 0.193
## 3 -0.728 -0.322 -0.524 0.465
## 4 -0.163 0.432 -0.403 0.189
## 5 -0.541 0.434 -0.508 -0.981
## 6 -0.188 -0.622 -0.355 0.142
## 7 -0.0674 0.695 -0.229 0.867
## 8 -0.0681 -0.0129 0.280 0.574
## 9 -0.0504 -0.252 0.912 -0.790
## 10 -0.515 -0.501 -0.675 0.417
## # 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] 0.241 0.326 0.229 0.255 0.291 ...