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] 365 962 501 430 540 670 431 485 897 737 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 365 824 975 797 566 750 424 754 965 45
## [2,] 962 819 554 379 598 431 812 103 180 7
## [3,] 501 242 332 450 299 724 131 354 427 789
## [4,] 430 601 297 605 984 26 247 346 701 497
## [5,] 540 707 716 466 827 664 918 619 813 629
## [6,] 670 113 920 207 296 794 842 493 947 127
## [7,] 431 554 962 67 2 902 607 180 276 812
## [8,] 485 514 975 24 409 824 63 274 992 882
## [9,] 897 177 677 608 560 808 582 378 23 921
## [10,] 737 974 160 943 611 297 445 346 223 895
## [11,] 61 335 366 471 216 100 161 226 204 953
## [12,] 87 859 425 770 791 688 874 779 816 710
## [13,] 987 627 48 16 367 861 783 239 14 213
## [14,] 426 987 372 318 191 213 13 16 779 653
## [15,] 695 233 293 889 968 450 63 195 702 661
## [16,] 987 783 13 625 676 627 14 48 589 598
## [17,] 508 427 60 501 999 132 212 238 549 794
## [18,] 938 338 541 459 407 698 604 361 514 58
## [19,] 522 404 282 413 771 515 753 322 692 680
## [20,] 368 678 106 335 314 872 704 862 221 693## num [1:1000, 1:30] 3.32 3.38 4.09 3.57 3.13 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.318934 3.401067 3.444134 3.715850 3.755753 3.785314 3.804993 3.825436
## [2,] 3.382410 3.581189 3.627823 3.668141 3.703606 3.902774 3.989778 4.034297
## [3,] 4.093142 4.243222 4.277992 4.306909 4.350494 4.360575 4.372952 4.381893
## [4,] 3.569947 3.583181 3.637451 3.663413 3.748994 3.908166 3.910539 3.939806
## [5,] 3.126276 4.091058 4.097302 4.552861 4.853895 4.958662 5.075716 5.155080
## [6,] 3.264471 3.327889 3.328334 3.405819 3.455521 3.475804 3.496189 3.497169
## [7,] 3.491925 3.688802 3.958095 4.057822 4.120862 4.172630 4.193240 4.205990
## [8,] 4.423634 4.953778 5.048789 5.065909 5.138321 5.190719 5.197353 5.219531
## [9,] 4.162920 4.471731 4.698248 4.738115 4.778760 4.819611 4.875379 4.924781
## [10,] 2.206203 2.829583 3.075735 3.093143 3.137864 3.176760 3.184080 3.241631
## [11,] 2.289549 2.752907 2.977494 3.039642 3.178267 3.193419 3.201240 3.233633
## [12,] 3.810307 3.862040 3.869815 3.961965 4.085290 4.089073 4.104447 4.200690
## [13,] 3.736119 3.951824 3.964374 4.017572 4.059307 4.074490 4.426026 4.471956
## [14,] 3.419116 3.626014 4.277316 4.286301 4.430295 4.443091 4.495380 4.525554
## [15,] 3.297419 3.340251 3.412893 3.484926 3.497966 3.574911 3.632760 3.884876
## [16,] 3.073766 3.880396 4.017572 4.251542 4.341929 4.411343 4.525554 4.796342
## [17,] 3.285076 3.380446 3.427433 3.721213 3.722028 3.757769 3.770940 3.816174
## [18,] 3.770685 3.868862 4.019431 4.023260 4.188502 4.243499 4.556795 4.572322
## [19,] 3.495569 3.645782 3.758190 3.818980 4.289584 4.318885 4.370202 4.381893
## [20,] 2.635397 2.736980 2.759300 2.767063 3.015529 3.064830 3.070590 3.089206
## [,9] [,10]
## [1,] 3.880909 3.903274
## [2,] 4.087599 4.120862
## [3,] 4.382291 4.554148
## [4,] 3.977939 4.027999
## [5,] 5.342173 5.420322
## [6,] 3.513007 3.530486
## [7,] 4.277440 4.320054
## [8,] 5.265439 5.281255
## [9,] 4.928045 4.993592
## [10,] 3.266114 3.335019
## [11,] 3.257728 3.261959
## [12,] 4.344866 4.355745
## [13,] 4.495380 4.586409
## [14,] 4.564862 4.599099
## [15,] 3.921310 3.944253
## [16,] 4.849083 4.948623
## [17,] 3.849029 3.912379
## [18,] 4.622136 4.761092
## [19,] 4.390743 4.393045
## [20,] 3.099248 3.185518This 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 0.933 1 0.806
## 2 0.811 0.842 1
## 3 0.936 1 0.772
## 4 0.997 0.895 1
## 5 0.933 1 0.772
## 6 0.674 0.981 0.675
## 7 0.901 0.981 0.986
## 8 0.874 0.899 0.956
## 9 0.811 0.826 0.786
## 10 0.720 0.899 0.899
## # 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 -0.722 -0.570 -1.80 -1.15
## 2 -0.442 -0.271 -0.854 -1.83
## 3 -0.226 0.115 -0.957 0.138
## 4 -1.08 -0.915 -1.30 -2.13
## 5 -0.402 -0.356 -0.397 -0.883
## 6 0.0523 -0.379 -0.550 -0.632
## 7 -0.223 -0.865 -1.16 -1.42
## 8 -0.341 -0.0113 -1.50 -0.0668
## 9 -1.28 -1.09 -2.00 -0.803
## 10 -1.31 -1.77 -2.67 0.773
## # 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] 0.254 0.239 0.214 0.241 0.178 ...