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] 418 1000 290 535 614 314 238 523 195 109 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 418 874 628 942 71 156 788 577 399 124
## [2,] 1000 733 767 821 727 866 755 107 81 806
## [3,] 290 590 488 821 607 806 208 404 37 733
## [4,] 535 613 801 672 627 366 914 451 163 573
## [5,] 614 119 999 755 767 821 139 173 773 866
## [6,] 314 733 607 153 664 1000 65 529 56 2
## [7,] 238 250 55 955 506 717 331 796 113 722
## [8,] 523 725 160 39 730 9 896 436 431 522
## [9,] 195 12 523 126 431 511 8 205 910 730
## [10,] 109 267 231 111 346 701 946 397 271 592
## [11,] 853 760 612 399 186 729 532 505 405 176
## [12,] 9 744 511 491 823 195 384 963 187 126
## [13,] 905 183 514 264 33 339 439 749 836 731
## [14,] 208 837 655 675 722 929 135 787 147 415
## [15,] 812 710 40 89 402 979 530 952 19 450
## [16,] 961 795 773 189 821 685 119 727 767 139
## [17,] 498 769 1000 717 555 660 65 929 656 117
## [18,] 370 33 905 264 749 220 13 495 72 265
## [19,] 638 380 40 710 686 284 473 718 657 770
## [20,] 689 674 68 743 486 82 559 801 451 778## num [1:1000, 1:30] 3.63 3.29 2.37 3.79 2.23 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.632355 3.708491 3.728559 3.751613 3.786198 3.924678 3.957302 4.005788
## [2,] 3.287073 3.462682 3.478402 3.508446 3.514234 3.534949 3.584064 3.621060
## [3,] 2.370303 3.255099 3.421620 3.473889 3.486982 3.559977 3.595751 3.670150
## [4,] 3.787672 4.033836 4.193843 4.444538 4.472436 4.587822 4.676960 4.722959
## [5,] 2.231078 3.030025 3.032456 3.263179 3.305017 3.320233 3.403182 3.618338
## [6,] 2.908461 3.375307 3.408716 3.474126 3.483984 3.569294 3.597304 3.612274
## [7,] 2.574222 2.620092 2.626894 2.649969 2.712833 2.720363 2.763009 2.966334
## [8,] 3.259553 3.409890 3.587018 3.638739 3.739323 3.781679 4.033151 4.083570
## [9,] 3.063779 3.229940 3.535084 3.620843 3.706292 3.745903 3.781679 3.842906
## [10,] 4.076489 4.274600 4.424466 4.680639 4.921531 4.925839 4.953778 5.138321
## [11,] 3.079625 3.232337 3.580175 3.626039 3.797314 3.973965 3.977959 4.034255
## [12,] 3.229940 3.327614 3.350932 3.380427 3.383156 3.390087 3.411248 3.412378
## [13,] 2.519572 2.564950 2.587125 2.637243 2.644419 2.854140 2.968647 2.970473
## [14,] 2.993627 3.185238 3.501026 3.503116 3.596555 3.600127 3.638911 3.682761
## [15,] 2.815737 2.827644 2.919770 3.015529 3.082324 3.129668 3.150681 3.221705
## [16,] 3.130284 3.247821 3.271862 3.284615 3.474307 3.479933 3.553985 3.554226
## [17,] 3.114888 3.213654 3.222439 3.414547 3.510649 3.544157 3.567269 3.587427
## [18,] 3.101829 3.145621 3.160221 3.163256 3.253455 3.267101 3.285374 3.286599
## [19,] 2.673262 2.709035 2.739427 2.762795 2.786634 2.825403 2.898502 2.936392
## [20,] 4.342825 4.718955 4.846609 4.975868 4.977152 5.050559 5.195131 5.289988
## [,9] [,10]
## [1,] 4.128160 4.134983
## [2,] 3.624421 3.625465
## [3,] 3.708585 3.738727
## [4,] 4.848288 4.860880
## [5,] 3.646421 3.664331
## [6,] 3.615328 3.630796
## [7,] 3.039027 3.101229
## [8,] 4.099165 4.119141
## [9,] 3.896887 3.902276
## [10,] 5.238911 5.265439
## [11,] 4.054686 4.057822
## [12,] 3.428945 3.449180
## [13,] 3.015428 3.015582
## [14,] 3.682817 3.740260
## [15,] 3.235812 3.250871
## [16,] 3.555144 3.779061
## [17,] 3.589238 3.631749
## [18,] 3.288427 3.327889
## [19,] 3.024934 3.076591
## [20,] 5.333017 5.337030This 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 0.790 1 0.902
## 2 0.953 1 0.854
## 3 0.984 1 0.953
## 4 0.909 1 0.979
## 5 0.834 1 0.979
## 6 0.984 1 0.991
## 7 0.790 1 0.839
## 8 0.809 1 0.717
## 9 0.984 1 0.849
## 10 0.984 1 0.749
## # 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 0.396 -0.107 -0.0873 -0.144
## 2 -0.0527 -0.0208 0.460 0.588
## 3 -0.306 -0.664 -0.507 -1.23
## 4 -0.443 0.648 -0.354 -2.12
## 5 -0.259 0.765 -0.193 -0.342
## 6 -0.0291 -0.152 0.0859 -0.389
## 7 -0.589 -0.573 0.995 -1.22
## 8 -0.154 1.04 0.596 -1.28
## 9 -0.0291 0.994 0.234 0.594
## 10 -0.434 -0.170 -0.150 0.905
## # 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] 0.234 0.267 0.26 0.203 0.27 ...