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] 966 24 582 319 331 563 714 273 419 606 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 966 751 748 724 227 133 965 763 762 900
## [2,] 24 895 99 606 970 216 533 101 15 953
## [3,] 582 920 401 720 260 9 431 754 266 475
## [4,] 319 290 314 951 368 253 164 585 33 362
## [5,] 331 507 555 744 883 862 75 149 827 436
## [6,] 563 238 66 306 17 538 891 923 750 689
## [7,] 714 933 674 480 979 283 272 70 152 899
## [8,] 273 466 551 507 394 495 685 733 675 262
## [9,] 419 959 688 372 720 909 297 87 796 613
## [10,] 606 931 968 970 99 594 433 882 458 864
## [11,] 366 383 759 454 600 356 637 484 319 469
## [12,] 58 751 411 966 259 447 87 219 631 525
## [13,] 634 458 216 968 723 824 179 56 902 154
## [14,] 398 578 229 481 88 903 508 108 679 289
## [15,] 344 101 558 671 250 633 876 291 741 953
## [16,] 481 971 47 850 88 164 141 823 356 613
## [17,] 923 881 689 93 282 556 794 516 306 780
## [18,] 563 923 233 616 133 956 274 337 61 158
## [19,] 212 99 950 116 659 864 322 757 824 606
## [20,] 556 672 227 505 966 856 978 63 315 751# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 2.41 3.88 3.64 3.29 4.04 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 2.407407 2.637939 2.827644 2.949460 3.015529 3.072693 3.090644 3.150681
## [2,] 3.875179 4.164362 4.191635 4.197356 4.253011 4.358723 4.411838 4.437622
## [3,] 3.641054 4.078285 4.140878 4.146091 4.735282 4.793053 4.803194 4.817959
## [4,] 3.292670 3.944156 4.124803 4.215529 4.235465 4.239000 4.271848 4.274129
## [5,] 4.037374 4.150413 4.262698 4.387087 4.405986 4.626051 4.687488 4.946689
## [6,] 3.094208 3.256243 3.530716 3.530851 3.548810 3.566553 3.636062 3.656537
## [7,] 2.921590 2.936505 2.942758 2.981166 3.147516 3.175475 3.397790 3.425969
## [8,] 2.623359 2.742813 3.339310 3.549813 3.613709 3.668870 3.680584 3.727517
## [9,] 3.289788 3.473815 3.971722 3.990268 4.015470 4.059120 4.076742 4.077997
## [10,] 3.221538 3.735856 3.780094 3.857558 3.867068 4.034457 4.048233 4.180874
## [11,] 3.292420 3.674934 3.693634 3.697950 3.715950 3.744294 3.753055 3.761022
## [12,] 2.873922 2.969204 2.977494 3.072755 3.098712 3.123732 3.156406 3.199029
## [13,] 3.139344 3.498250 3.527949 3.606120 3.693368 3.720628 3.937566 4.061924
## [14,] 3.363588 3.415407 3.453381 3.520534 3.636421 3.643207 3.761648 3.780802
## [15,] 2.527205 2.564574 2.691874 2.718144 2.789527 2.813570 2.926609 2.927257
## [16,] 2.987907 3.286449 3.392992 3.507742 3.536301 3.563211 3.622569 3.718878
## [17,] 2.832318 2.891351 2.893196 2.902310 2.943991 3.013196 3.055931 3.119748
## [18,] 3.823550 3.895688 3.909433 3.959798 4.044857 4.079071 4.087764 4.142940
## [19,] 3.707049 3.817305 3.977319 4.086391 4.129631 4.306932 4.366362 4.565656
## [20,] 2.966159 3.089898 3.099248 3.102709 3.124067 3.200009 3.223002 3.229411
## [,9] [,10]
## [1,] 3.184743 3.186283
## [2,] 4.486098 4.488840
## [3,] 4.843023 4.870586
## [4,] 4.288405 4.351104
## [5,] 5.029371 5.034855
## [6,] 3.667137 3.709151
## [7,] 3.442462 3.468957
## [8,] 3.749202 3.761407
## [9,] 4.078288 4.078329
## [10,] 4.192146 4.193828
## [11,] 3.843806 3.916749
## [12,] 3.210260 3.263643
## [13,] 4.104012 4.176411
## [14,] 3.798015 3.800711
## [15,] 3.014470 3.093675
## [16,] 3.722481 3.758942
## [17,] 3.161492 3.232540
## [18,] 4.181443 4.186960
## [19,] 4.673308 4.686859
## [20,] 3.231976 3.236763This 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 0.652 1 1
## 2 0.864 1 1
## 3 0.989 1 1
## 4 0.892 1 1
## 5 0.954 1 1
## 6 0.858 1 1
## 7 0.877 1 1
## 8 0.690 1 1
## 9 0.925 1 1
## 10 0.664 1 1
## # 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 -0.0638 -0.235 -0.265 -0.240
## 2 -0.257 -0.311 -0.447 -0.920
## 3 -0.0744 0.170 0.0230 -0.481
## 4 -0.0894 -0.309 -0.000283 0.253
## 5 -0.0337 -0.117 -0.583 0.0137
## 6 -0.130 -0.0688 0.433 -0.518
## 7 -0.364 -0.535 -0.490 -0.698
## 8 -0.370 -0.470 -0.638 -0.102
## 9 -0.147 -0.355 0.160 -1.11
## 10 -0.0458 -0.195 -0.180 -0.708
## # 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] 0.308 0.218 0.197 0.225 0.199 ...