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] 224 638 885 336 226 486 274 499 90 189 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 224 662 903 777 857 97 995 423 759 450
## [2,] 638 479 779 558 689 826 613 814 890 310
## [3,] 885 865 165 271 941 453 456 185 386 422
## [4,] 336 322 195 813 480 472 426 572 464 525
## [5,] 226 635 688 23 952 119 847 689 423 335
## [6,] 486 707 76 108 717 188 949 628 869 531
## [7,] 274 732 76 256 486 949 381 833 60 785
## [8,] 499 97 477 71 133 483 498 335 1 777
## [9,] 90 417 754 919 948 367 282 427 940 152
## [10,] 189 485 339 198 850 246 308 567 50 12
## [11,] 550 182 986 211 720 287 369 742 256 819
## [12,] 822 567 734 155 178 485 50 651 682 189
## [13,] 369 869 628 258 918 751 924 774 486 748
## [14,] 413 869 735 283 369 550 374 545 268 77
## [15,] 968 429 58 634 563 32 29 131 814 584
## [16,] 361 475 158 28 755 62 281 796 707 904
## [17,] 553 6 486 532 622 303 599 35 47 818
## [18,] 976 548 157 1 510 559 260 277 337 952
## [19,] 486 780 774 258 374 13 949 875 751 707
## [20,] 647 550 158 707 281 83 475 981 112 28# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 2.79 3.15 4.68 3.41 3.42 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 2.789880 3.055646 3.132630 3.165508 3.252539 3.326228 3.361317 3.367862
## [2,] 3.148988 3.259271 3.285041 3.339632 3.387981 3.426064 3.466001 3.557338
## [3,] 4.682335 4.897369 4.966901 4.999660 5.039738 5.046384 5.056376 5.080678
## [4,] 3.413191 3.932257 3.983984 4.103935 4.231706 4.375022 4.487338 4.490640
## [5,] 3.422646 3.630493 3.733845 3.978432 3.993102 4.009889 4.012787 4.093523
## [6,] 2.314570 3.058306 3.192741 3.211358 3.282797 3.304618 3.323551 3.338076
## [7,] 2.355356 2.908774 2.919602 2.931802 2.977181 3.087312 3.093901 3.129033
## [8,] 2.808429 3.262643 3.579560 3.805075 3.820019 3.855877 3.929064 3.949969
## [9,] 4.741335 4.751820 4.903699 4.918202 4.938301 5.049838 5.110778 5.178555
## [10,] 4.376914 4.381185 4.453382 4.556114 4.574178 4.590487 4.769049 4.790982
## [11,] 2.715516 3.031190 3.071348 3.181328 3.194773 3.252136 3.287926 3.291665
## [12,] 3.939728 4.022139 4.126250 4.184798 4.201195 4.271325 4.335344 4.385860
## [13,] 2.866192 2.883953 2.918729 2.920141 3.038580 3.053570 3.063518 3.064875
## [14,] 2.501066 2.749921 2.937049 2.994278 2.995655 3.025830 3.037645 3.049261
## [15,] 3.395493 3.514509 3.544002 3.652081 3.968964 3.986126 3.987274 3.998168
## [16,] 3.107219 3.339896 3.593604 3.609493 3.673527 3.675341 3.694217 3.706624
## [17,] 4.008753 4.190589 4.403804 4.451535 4.502277 4.552564 4.580753 4.603309
## [18,] 3.064093 3.278002 3.458017 3.550795 3.611620 3.653345 3.700618 3.732034
## [19,] 3.350017 3.367601 3.408984 3.460197 3.493728 3.498565 3.556694 3.568768
## [20,] 3.205045 3.301887 3.354461 3.396483 3.488228 3.498656 3.529567 3.567202
## [,9] [,10]
## [1,] 3.372474 3.377001
## [2,] 3.569509 3.578715
## [3,] 5.085931 5.087400
## [4,] 4.493344 4.552603
## [5,] 4.099559 4.115680
## [6,] 3.388791 3.422527
## [7,] 3.211902 3.252272
## [8,] 4.022563 4.057465
## [9,] 5.336097 5.384740
## [10,] 4.798983 4.907949
## [11,] 3.318469 3.367012
## [12,] 4.560742 4.592491
## [13,] 3.180808 3.225316
## [14,] 3.158268 3.218674
## [15,] 4.116360 4.191713
## [16,] 3.797831 3.852141
## [17,] 4.639045 4.661527
## [18,] 3.774769 3.785342
## [19,] 3.573260 3.610366
## [20,] 3.583937 3.618509This 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… `pCREB(Yb176)Di… `pBTK(Yb171)Di.… `pS6(Yb172)Di.I…
## <dbl> <dbl> <dbl> <dbl>
## 1 0.977 0.984 1 0.610
## 2 0.932 0.984 1 0.887
## 3 0.925 0.984 1 0.670
## 4 0.967 0.984 1 0.624
## 5 0.991 0.984 1 0.608
## 6 0.977 0.984 1 0.910
## 7 0.925 0.984 1 0.961
## 8 0.925 0.984 1 0.851
## 9 0.973 0.984 1 0.515
## 10 0.959 0.997 1 0.630
## # … 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(… `CD3(Cd114)Di`
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.227 -0.185 1.51 -1.48 -0.473
## 2 0.304 -0.136 -0.260 0.390 -0.0503
## 3 -0.326 -0.189 -0.584 -1.09 1.24
## 4 0.127 -0.0391 -0.00847 -1.46 -0.175
## 5 0.0509 -0.254 0.947 -0.608 -0.125
## 6 -0.167 0.549 1.49 -0.830 0.266
## 7 -0.0132 -0.487 -0.389 -0.832 -0.0537
## 8 0.689 -0.238 -0.215 -2.99 0.0304
## 9 -0.541 0.434 -0.508 -0.981 -0.361
## 10 -0.165 -0.0847 -0.159 -1.76 -0.178
## # … 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] 0.289 0.271 0.189 0.216 0.229 ...