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"
## [3] "CD3(Cd112)Di" "CD235-61-7-15(In113)Di"
## [5] "CD3(Cd114)Di" "CD45(In115)Di"
## [7] "CD19(Nd142)Di" "CD22(Nd143)Di"
## [9] "IgD(Nd145)Di" "CD79b(Nd146)Di"
## [11] "CD20(Sm147)Di" "CD34(Nd148)Di"
## [13] "CD179a(Sm149)Di" "CD72(Eu151)Di"
## [15] "IgM(Eu153)Di" "Kappa(Sm154)Di"
## [17] "CD10(Gd156)Di" "Lambda(Gd157)Di"
## [19] "CD24(Dy161)Di" "TdT(Dy163)Di"
## [21] "Rag1(Dy164)Di" "PreBCR(Ho165)Di"
## [23] "CD43(Er167)Di" "CD38(Er168)Di"
## [25] "CD40(Er170)Di" "CD33(Yb173)Di"
## [27] "HLA-DR(Yb174)Di"
##
## $functional
## [1] "pCrkL(Lu175)Di" "pCREB(Yb176)Di" "pBTK(Yb171)Di"
## [4] "pS6(Yb172)Di" "cPARP(La139)Di" "pPLCg2(Pr141)Di"
## [7] "pSrc(Nd144)Di" "Ki67(Sm152)Di" "pErk12(Gd155)Di"
## [10] "pSTAT3(Gd158)Di" "pAKT(Tb159)Di" "pBLNK(Gd160)Di"
## [13] "pP38(Tm169)Di" "pSTAT5(Nd150)Di" "pSyk(Dy162)Di"
## [16] "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] 664 606 621 775 490 108 412 265 580 606 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 664 208 732 152 49 46 44 438 641 24
## [2,] 606 448 819 117 893 647 765 258 490 785
## [3,] 621 504 83 482 872 733 926 942 331 568
## [4,] 775 931 808 392 241 624 474 629 212 281
## [5,] 490 823 700 606 785 117 10 448 453 2
## [6,] 108 397 283 159 403 296 324 797 423 34
## [7,] 412 131 159 282 747 57 753 1000 373 23
## [8,] 265 329 131 582 196 795 302 770 619 977
## [9,] 580 728 677 470 267 497 117 732 700 113
## [10,] 606 830 785 765 2 12 675 969 117 514
## [11,] 117 448 576 298 689 453 362 82 745 528
## [12,] 525 700 670 10 545 433 285 243 459 345
## [13,] 509 976 498 177 537 403 410 270 57 571
## [14,] 333 82 555 512 387 985 488 449 486 337
## [15,] 975 357 670 480 555 856 82 59 811 512
## [16,] 518 2 965 958 785 193 956 692 969 606
## [17,] 401 328 84 983 422 416 96 50 759 791
## [18,] 27 843 865 736 109 707 766 628 446 618
## [19,] 114 531 501 421 516 610 530 664 890 973
## [20,] 807 560 473 445 133 420 219 230 29 585# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 3.52 2.42 3.85 3.94 3.41 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 3.522297 3.585750 3.605184 3.628740 3.693694 3.854822 3.880637
## [2,] 2.421302 2.760060 2.951655 3.012184 3.078784 3.079854 3.080089
## [3,] 3.850458 3.936117 4.201927 4.248591 4.255944 4.289726 4.324657
## [4,] 3.939728 4.022139 4.126250 4.271325 4.335344 4.385860 4.592491
## [5,] 3.410314 3.523987 3.567784 3.761739 3.764044 3.793746 3.847133
## [6,] 4.126217 4.162691 4.316565 4.368160 4.372758 4.469300 4.471064
## [7,] 3.195534 3.503581 3.812188 3.845948 3.968345 4.047296 4.108990
## [8,] 4.393115 4.758992 4.775084 4.863757 4.894715 5.091875 5.134436
## [9,] 2.999932 3.044497 3.223405 3.320653 3.544441 3.549419 3.603670
## [10,] 2.802886 3.000942 3.215899 3.266859 3.319362 3.380427 3.404438
## [11,] 3.129680 3.188596 3.289729 3.336713 3.423831 3.447816 3.462920
## [12,] 2.898468 3.206925 3.327614 3.380427 3.383156 3.409308 3.411248
## [13,] 2.864099 3.357628 3.644647 3.713032 3.830255 3.991679 4.012792
## [14,] 4.090237 4.294670 4.343375 4.409506 4.424466 4.469931 4.507633
## [15,] 3.013031 3.714742 3.779462 3.883724 3.912209 4.089496 4.217696
## [16,] 3.054867 3.664290 3.780762 3.790289 3.814452 3.852749 3.901354
## [17,] 4.418826 4.652209 4.859045 5.096850 5.107900 5.231935 5.444937
## [18,] 3.149285 3.476955 3.595509 3.751721 3.782683 3.851345 3.990705
## [19,] 2.469692 2.544611 2.623631 2.661384 2.847252 2.858227 2.912111
## [20,] 3.831873 4.309240 4.472985 4.639011 4.843249 4.881974 4.892853
## [,8] [,9] [,10]
## [1,] 3.897496 3.942260 3.968413
## [2,] 3.105719 3.207663 3.249908
## [3,] 4.326873 4.421326 4.437259
## [4,] 4.683479 4.758725 4.760341
## [5,] 3.861519 4.022318 4.026012
## [6,] 4.522414 4.618036 4.638863
## [7,] 4.136718 4.186277 4.278127
## [8,] 5.142083 5.156782 5.166006
## [9,] 3.635642 3.671480 3.709553
## [10,] 3.406158 3.435323 3.474796
## [11,] 3.489961 3.527320 3.559208
## [12,] 3.428945 3.513848 3.550266
## [13,] 4.026742 4.089018 4.102203
## [14,] 4.564953 4.658255 4.704461
## [15,] 4.256358 4.264037 4.277128
## [16,] 3.917767 3.971170 3.994460
## [17,] 5.477928 5.585931 5.650160
## [18,] 4.113093 4.154497 4.243374
## [19,] 2.928395 2.994446 3.047483
## [20,] 4.900333 4.915724 4.937156This 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 0.910 1 0.997 0.979
## 2 0.944 1 0.997 0.948
## 3 0.791 1 0.997 0.979
## 4 0.818 1 0.997 0.948
## 5 0.800 1 0.989 1
## 6 0.800 1 0.984 0.955
## 7 0.989 1 0.984 0.968
## 8 0.685 1 0.858 0.955
## 9 0.896 1 0.997 0.955
## 10 0.989 0.981 0.997 0.979
## # … 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(…
## <dbl> <dbl> <dbl> <dbl>
## 1 -0.861 -1.09 -1.17 0.285
## 2 -0.941 -0.661 -1.05 -0.796
## 3 -0.498 -0.506 -0.480 -0.698
## 4 -0.0231 0.389 -0.151 -0.528
## 5 -0.181 -0.206 -0.132 -0.518
## 6 -0.414 -0.0421 -0.158 -0.888
## 7 -0.176 -0.252 -0.0586 -0.0942
## 8 -0.291 -0.0432 -0.0699 -1.46
## 9 -0.541 -0.367 -0.808 0.00252
## 10 -0.226 -0.114 -0.0588 -0.486
## # … with 20 more rows, and 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>, `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] 0.251 0.298 0.22 0.2 0.241 ...