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] 957 435 988 659 563 714 541 655 411 374 ...
wand.nn[[1]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 957 335 758 314 848 700 769 605 511 665
## [2,] 435 152 766 709 798 951 49 936 935 710
## [3,] 988 906 385 219 465 582 569 552 498 353
## [4,] 659 277 183 498 552 514 743 894 757 602
## [5,] 563 106 856 470 21 742 960 545 287 455
## [6,] 714 722 195 756 186 699 117 99 397 737
## [7,] 541 235 189 643 616 441 220 394 973 359
## [8,] 655 67 359 909 485 300 791 369 555 643
## [9,] 411 899 605 386 84 336 261 947 813 344
## [10,] 374 366 576 104 433 882 440 580 763 393
## [11,] 552 832 864 98 894 183 353 514 31 4
## [12,] 357 962 615 381 942 266 482 850 791 515
## [13,] 772 592 486 745 562 664 758 646 961 583
## [14,] 423 656 952 324 859 91 829 445 20 517
## [15,] 592 800 583 490 13 934 772 714 840 558
## [16,] 402 907 564 223 226 983 497 176 628 977
## [17,] 725 770 631 314 689 393 580 213 215 83
## [18,] 120 746 811 190 639 434 897 766 248 999
## [19,] 491 715 316 869 309 226 44 71 834 862
## [20,] 445 233 77 517 91 612 473 462 769 423
# Distance
str(wand.nn[[2]])
## num [1:1000, 1:30] 3.07 3.38 3.3 3.31 3.12 ...
wand.nn[[2]][1:20, 1:10]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.065316 3.397660 3.437216 3.484488 3.553685 3.605198 3.611376 3.613591
## [2,] 3.377001 3.505477 3.516507 3.542438 3.574119 3.607632 3.622953 3.668460
## [3,] 3.297777 3.950556 4.178218 4.348766 4.390887 4.517866 4.518523 4.522039
## [4,] 3.311331 3.339767 3.352972 3.390046 3.541037 3.625683 3.642903 3.687154
## [5,] 3.122905 3.367718 3.475313 3.748741 3.752576 3.771008 3.863596 3.912126
## [6,] 2.877301 3.061208 3.094085 3.148103 3.155099 3.199561 3.211280 3.218700
## [7,] 3.900709 4.031615 4.370300 4.377665 4.533079 4.557106 4.571585 4.573960
## [8,] 3.372035 3.378054 3.417346 3.489331 3.500729 3.507048 3.508005 3.512798
## [9,] 2.740607 3.046171 3.101700 3.273305 3.285297 3.357260 3.393762 3.447957
## [10,] 4.430561 4.630947 4.673146 4.808819 4.952011 5.082232 5.083216 5.207046
## [11,] 3.395928 3.815551 3.986953 4.111590 4.117455 4.124254 4.140898 4.259279
## [12,] 2.102466 2.969952 3.090734 3.121466 3.129891 3.145059 3.193380 3.291733
## [13,] 2.382848 2.787093 2.816422 2.880935 2.887437 2.911389 2.963617 2.971006
## [14,] 7.031263 7.277038 7.884798 7.958540 8.151665 8.187366 8.216847 8.282400
## [15,] 3.524509 3.619611 3.697266 3.750548 3.794082 3.915970 3.976458 4.012887
## [16,] 4.138642 4.227155 4.456780 4.519103 4.781641 4.874225 4.914206 4.943459
## [17,] 2.306681 2.788849 3.124734 3.341306 3.377360 3.435826 3.458431 3.465291
## [18,] 2.994119 3.411322 3.782354 3.811089 3.880291 3.971995 4.028750 4.085494
## [19,] 3.707049 4.129631 4.178302 4.200740 4.366362 4.408984 4.602414 4.616120
## [20,] 4.012866 4.047417 4.093232 4.216919 4.362413 4.413734 4.465344 4.518486
## [,9] [,10]
## [1,] 3.659387 3.665259
## [2,] 3.726756 3.741228
## [3,] 4.529301 4.624106
## [4,] 3.696173 3.712973
## [5,] 4.016532 4.021432
## [6,] 3.235612 3.248603
## [7,] 4.582614 4.605655
## [8,] 3.553182 3.591305
## [9,] 3.576495 3.606970
## [10,] 5.226651 5.229419
## [11,] 4.282645 4.302765
## [12,] 3.292718 3.314765
## [13,] 3.057822 3.060117
## [14,] 8.537099 8.647748
## [15,] 4.046038 4.125926
## [16,] 4.972149 4.985732
## [17,] 3.471657 3.473259
## [18,] 4.098819 4.122375
## [19,] 4.673308 4.686859
## [20,] 4.529640 4.552861
This 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 1.00 1 0.968
## 2 0.982 1 0.748
## 3 0.991 1 0.708
## 4 0.982 1 0.887
## 5 1 1 1
## 6 0.982 1 0.812
## 7 0.982 1 0.926
## 8 0.950 1 0.932
## 9 0.991 1 0.690
## 10 0.982 1 0.708
## # 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 -0.188 -0.479 1.54 -0.974
## 2 -0.166 -0.476 -0.304 -1.55
## 3 -0.908 -0.813 -0.287 -1.83
## 4 -0.204 -0.0232 -0.140 0.0694
## 5 -0.326 -0.0343 1.04 0.157
## 6 -0.262 -0.454 -0.110 -0.447
## 7 -0.349 -0.171 -0.0710 0.432
## 8 -0.0477 -0.0514 -0.0339 -0.474
## 9 -0.473 -0.140 -0.487 -0.122
## 10 -0.185 0.101 -0.121 -0.442
## # 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] 0.267 0.259 0.211 0.253 0.243 ...