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] 936 988 396 545 716 275 518 16 616 861 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 936 498 741 638 377 422 22 500 333 137
## [2,] 988 621 258 512 12 489 863 873 950 215
## [3,] 396 801 523 42 331 548 509 734 780 939
## [4,] 545 69 408 224 445 784 601 885 417 853
## [5,] 716 772 823 549 317 594 39 11 432 938
## [6,] 275 700 354 168 807 999 976 592 278 600
## [7,] 518 208 795 492 326 752 526 331 168 910
## [8,] 16 584 339 733 240 601 657 647 686 452
## [9,] 616 217 419 139 310 133 363 756 973 83
## [10,] 861 497 773 767 24 160 805 921 632 982
## [11,] 772 842 594 224 851 408 598 423 845 858
## [12,] 464 810 580 258 433 890 68 873 439 587
## [13,] 520 80 425 635 514 252 418 227 445 346
## [14,] 621 146 569 282 637 587 983 55 873 750
## [15,] 184 107 416 864 73 571 242 399 110 192
## [16,] 584 764 590 8 733 521 663 213 647 957
## [17,] 783 20 758 879 437 235 187 322 681 668
## [18,] 636 504 900 521 906 590 996 133 911 920
## [19,] 706 650 233 708 514 544 904 369 776 561
## [20,] 856 234 559 839 956 298 875 25 713 729# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 2.71 3.91 2.57 2.49 4.08 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 2.707680 2.821755 3.080338 3.187454 3.208096 3.214857 3.265534 3.319018
## [2,] 3.914548 4.268692 4.314909 4.366468 4.555297 4.558253 4.601060 4.609032
## [3,] 2.574222 2.743768 2.923213 3.031135 3.189341 3.235170 3.347033 3.385220
## [4,] 2.485762 3.154017 3.185010 3.527210 3.660883 3.669237 3.785822 3.810819
## [5,] 4.076856 4.116485 4.159575 4.171534 4.172872 4.225500 4.272803 4.321498
## [6,] 2.370303 3.304337 3.411283 3.435706 3.468961 3.524122 3.618231 3.682096
## [7,] 3.819593 3.897873 3.909321 3.998455 4.135402 4.139994 4.206469 4.218315
## [8,] 2.663104 3.128321 3.172732 3.201109 3.256129 3.265436 3.369040 3.408421
## [9,] 3.048366 3.227690 3.285664 3.331935 3.577482 3.598409 3.685850 3.693053
## [10,] 2.977112 3.115130 3.115229 3.125238 3.274541 3.351861 3.415559 3.416516
## [11,] 2.796071 2.898579 3.008502 3.176828 3.521962 3.546036 3.577782 3.585439
## [12,] 2.867231 3.220076 3.493143 3.534774 3.589628 3.591454 3.593361 3.619259
## [13,] 2.898020 3.277176 3.593471 3.601077 3.738085 3.931162 3.947595 4.080164
## [14,] 4.046617 4.106526 4.415533 4.495225 4.720460 4.854444 4.866282 4.933615
## [15,] 4.192822 4.335475 4.370983 4.411843 4.471538 4.474514 4.603385 4.607520
## [16,] 2.454328 2.592222 2.644167 2.663104 2.714878 2.810516 2.825407 2.853666
## [17,] 3.052123 3.924477 4.111991 4.238328 4.247532 4.266171 4.530476 4.645340
## [18,] 3.583994 3.698748 3.739959 3.743319 3.770411 3.835693 3.860176 3.907122
## [19,] 3.685447 3.730388 3.731102 3.779581 3.843437 3.852244 3.906250 3.922677
## [20,] 2.897730 2.899267 3.236289 3.407082 3.498950 3.562572 3.584150 3.589647
## [,9] [,10]
## [1,] 3.331857 3.380915
## [2,] 4.646497 4.767159
## [3,] 3.463256 3.550264
## [4,] 3.918454 3.951798
## [5,] 4.415725 4.425867
## [6,] 3.700618 3.796981
## [7,] 4.223790 4.235914
## [8,] 3.415186 3.432905
## [9,] 3.742533 3.868288
## [10,] 3.486595 3.509272
## [11,] 3.669028 3.717865
## [12,] 3.660755 3.715657
## [13,] 4.089714 4.145359
## [14,] 5.020411 5.024372
## [15,] 4.617145 4.632785
## [16,] 2.879802 2.956599
## [17,] 4.695619 4.725894
## [18,] 3.981942 4.024973
## [19,] 3.968094 3.975874
## [20,] 3.612289 3.675965This 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 0.984 0.951 1
## 2 1 0.951 0.870
## 3 0.998 0.959 0.874
## 4 0.978 0.951 0.981
## 5 0.989 0.951 0.943
## 6 0.978 0.959 0.974
## 7 0.978 1 0.981
## 8 0.978 0.959 0.273
## 9 0.989 0.959 0.836
## 10 0.978 0.959 0.943
## # 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 -0.218 -0.0910 -0.198 0.145
## 2 -0.0209 -0.0434 -0.245 -0.331
## 3 -0.215 -0.250 -0.0990 -0.294
## 4 -0.474 0.346 -0.507 0.964
## 5 -0.869 -0.427 0.197 -1.07
## 6 -0.104 -0.0145 0.276 -0.288
## 7 -0.171 -0.126 -0.0613 0.187
## 8 -0.234 -0.0633 -0.104 -0.579
## 9 -0.0255 -0.414 -0.415 -0.484
## 10 0.0970 0.530 -0.0602 0.280
## # 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] 0.284 0.205 0.282 0.253 0.221 ...