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] 832 817 38 471 797 758 807 999 165 75 ...
wand.nn[[1]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 832 666 302 130 625 468 939 912 594 504
## [2,] 817 273 134 69 73 549 314 707 488 210
## [3,] 38 200 225 10 639 52 637 75 281 319
## [4,] 471 57 13 946 337 69 418 131 652 473
## [5,] 797 464 653 998 28 34 615 201 82 544
## [6,] 758 30 608 708 903 870 413 229 833 853
## [7,] 807 238 724 581 480 25 863 11 214 184
## [8,] 999 827 273 475 63 186 768 269 355 301
## [9,] 165 900 687 316 96 757 178 329 23 958
## [10,] 75 685 323 92 38 3 794 667 281 52
## [11,] 807 887 7 238 762 970 422 581 441 61
## [12,] 214 264 916 298 950 181 51 326 819 425
## [13,] 496 471 102 564 131 764 265 867 326 348
## [14,] 201 615 280 710 628 636 653 677 190 991
## [15,] 696 240 938 520 666 335 888 477 250 33
## [16,] 227 618 283 856 74 232 840 513 77 208
## [17,] 304 954 461 453 801 985 100 846 508 845
## [18,] 260 666 611 702 692 625 939 916 912 554
## [19,] 210 707 770 817 881 371 293 2 297 312
## [20,] 606 431 784 644 844 297 436 878 770 371
# Distance
str(wand.nn[[2]])
## num [1:1000, 1:30] 2.76 2.87 3.48 2.84 3.73 ...
wand.nn[[2]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 2.755977 2.766653 2.809017 2.869076 2.926942 2.985558 3.030535 3.106700
## [2,] 2.869122 3.292420 3.386856 3.444082 3.674934 3.693634 3.697950 3.715950
## [3,] 3.482542 3.669566 4.183429 4.395033 4.589795 4.603207 4.672440 4.740314
## [4,] 2.835728 2.840489 3.429947 3.508064 3.564146 3.601238 3.706476 3.749412
## [5,] 3.732467 4.391361 4.393850 4.493268 4.514198 4.610233 4.687230 4.755811
## [6,] 3.997052 4.150343 4.262393 4.370475 4.532881 4.559057 4.600119 4.707549
## [7,] 2.941805 2.962524 3.005583 3.187454 3.208096 3.214857 3.250388 3.265534
## [8,] 3.738085 3.799218 3.931162 3.947595 4.080164 4.089714 4.090986 4.136228
## [9,] 4.599705 4.955823 5.273953 5.333594 5.451349 5.498463 5.515224 5.595893
## [10,] 3.679249 3.904158 3.942074 4.236226 4.313055 4.395033 4.456942 4.469473
## [11,] 2.852235 3.064180 3.265534 3.401118 3.516531 3.597213 3.641495 3.725871
## [12,] 2.973577 2.973670 3.013207 3.018629 3.025042 3.105979 3.122978 3.126406
## [13,] 2.698241 2.742485 2.924695 2.947488 2.951141 2.960109 3.015123 3.046227
## [14,] 4.543723 4.584606 4.681733 4.706540 4.730245 4.753758 4.798590 4.813634
## [15,] 2.310230 2.809737 3.018988 3.051324 3.072755 3.098712 3.110989 3.114917
## [16,] 2.849562 3.087177 3.229654 3.268915 3.320396 3.401931 3.417045 3.620138
## [17,] 4.658434 4.907575 4.908853 5.142862 5.181301 5.275184 5.375401 5.404872
## [18,] 2.242857 2.465524 2.541607 2.646095 2.681402 2.687919 2.795479 2.946453
## [19,] 4.478560 4.660287 4.682335 4.897369 5.028948 5.046384 5.056376 5.085931
## [20,] 3.844795 3.888269 3.976899 4.005640 4.224075 4.341237 4.428430 4.510202
## [,9] [,10]
## [1,] 3.111678 3.145789
## [2,] 3.744294 3.761022
## [3,] 4.924132 4.950767
## [4,] 3.752011 3.754423
## [5,] 4.774496 4.776929
## [6,] 4.722819 4.759005
## [7,] 3.292698 3.319018
## [8,] 4.141041 4.145359
## [9,] 5.597697 5.634922
## [10,] 4.472910 4.473370
## [11,] 3.882224 3.914558
## [12,] 3.152739 3.238886
## [13,] 3.186649 3.203498
## [14,] 4.896688 5.032718
## [15,] 3.139106 3.156406
## [16,] 3.639266 3.681633
## [17,] 5.518690 5.564337
## [18,] 2.955116 3.032133
## [19,] 5.087400 5.114662
## [20,] 4.527934 4.557106
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.IL… `pCREB(Yb176)Di.IL… `pBTK(Yb171)Di.IL… `pS6(Yb172)Di.IL7…
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0.999 0.967 0.998
## 2 0.968 0.999 0.727 0.973
## 3 0.968 0.999 0.823 0.920
## 4 0.957 0.999 0.474 0.881
## 5 1 0.999 1 0.730
## 6 0.968 0.999 0.907 0.843
## 7 0.968 0.999 0.986 0.968
## 8 1 0.999 0.908 0.969
## 9 1 0.999 0.871 0.966
## 10 0.897 0.999 0.973 0.928
## # … 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(I… `CD3(Cd114)Di`
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -0.442 -0.271 -0.854 -1.83 -0.126
## 2 -0.181 0.134 0.679 0.0851 -0.235
## 3 -0.243 -0.0594 -0.402 -1.19 -0.225
## 4 -0.126 -0.0820 -0.0859 -1.75 -0.132
## 5 -0.248 -0.217 -0.352 0.628 -0.400
## 6 -0.113 -0.176 0.456 -0.695 -0.0897
## 7 0.0523 -0.379 -0.550 -0.632 -0.604
## 8 -0.908 -0.813 -0.287 -1.83 -0.271
## 9 -0.254 -0.193 -0.121 -0.267 -0.0189
## 10 -0.443 0.648 -0.354 -2.12 -0.0684
## # … 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] 0.311 0.259 0.196 0.262 0.204 ...