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] 850 648 342 53 523 415 564 863 695 522 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 850 277 68 611 733 555 144 411 357 36
## [2,] 648 14 387 43 17 95 912 66 420 810
## [3,] 342 667 325 828 940 326 905 630 403 17
## [4,] 53 817 192 541 575 724 299 467 492 215
## [5,] 523 683 935 250 905 627 818 294 679 854
## [6,] 415 704 453 924 977 118 662 558 709 191
## [7,] 564 748 939 81 919 686 140 392 992 756
## [8,] 863 556 760 908 20 676 664 649 798 623
## [9,] 695 931 162 106 354 353 467 209 509 215
## [10,] 522 607 66 195 611 55 480 437 552 394
## [11,] 424 692 522 54 784 674 608 611 460 55
## [12,] 224 766 603 997 535 488 904 765 31 461
## [13,] 732 953 405 652 692 595 349 74 697 234
## [14,] 2 648 996 320 810 47 821 912 646 460
## [15,] 336 586 388 516 881 504 902 452 754 231
## [16,] 335 613 764 592 698 904 362 690 868 654
## [17,] 421 912 262 273 955 977 611 672 437 95
## [18,] 373 569 513 511 343 862 979 375 851 960
## [19,] 455 304 504 601 257 436 909 429 336 563
## [20,] 908 8 507 676 863 649 664 198 760 167# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 4.13 3.01 2.77 4.13 3.4 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 4.130523 4.167087 4.170654 4.445916 4.510949 4.515071 4.533419 4.589469
## [2,] 3.013207 3.018629 3.105979 3.122978 3.126406 3.142318 3.152739 3.212486
## [3,] 2.766774 2.910265 2.969226 3.123300 3.168725 3.195781 3.200762 3.208651
## [4,] 4.130042 4.286646 4.348400 4.379537 4.383952 4.411795 4.514507 4.624474
## [5,] 3.395219 3.589995 3.604157 3.641887 3.719833 3.781183 3.793780 3.794525
## [6,] 4.465635 4.540669 4.569023 4.577746 4.637489 4.664614 4.666003 4.741700
## [7,] 2.634033 2.881735 2.887600 2.927257 2.972622 3.021849 3.030286 3.043581
## [8,] 3.318566 3.564479 3.593188 3.612330 3.640663 3.747639 3.985513 4.258449
## [9,] 4.082943 4.173731 4.230231 4.238371 4.385018 4.530642 4.598379 4.672785
## [10,] 3.056680 3.485689 3.573676 3.814698 3.836351 4.000317 4.004756 4.048655
## [11,] 3.370914 3.582281 3.620696 3.654803 3.694001 3.696477 3.748353 3.851958
## [12,] 3.471128 3.631353 3.834755 3.849196 3.913398 3.935926 3.962134 4.009265
## [13,] 3.568042 3.610267 3.667464 3.784714 3.934576 3.960539 4.056113 4.063669
## [14,] 3.018629 3.182182 3.297410 3.328056 3.335644 3.412268 3.438324 3.482824
## [15,] 3.127203 3.167518 3.324695 3.324986 3.445876 3.477833 3.527446 3.536305
## [16,] 4.797162 5.096301 5.203686 5.312953 5.387677 5.455912 5.542862 5.551291
## [17,] 2.310442 2.464186 2.637243 2.637653 2.697258 2.701062 2.735473 2.753542
## [18,] 3.067721 3.165625 3.175566 3.215821 3.244561 3.260511 3.262475 3.276575
## [19,] 3.732413 4.051274 4.217811 4.381576 4.400542 4.409877 4.423352 4.465460
## [20,] 3.323634 3.640663 3.655973 3.820059 3.861440 3.898579 4.009023 4.009327
## [,9] [,10]
## [1,] 4.624781 4.649591
## [2,] 3.238886 3.278219
## [3,] 3.223906 3.293347
## [4,] 4.726387 4.841182
## [5,] 3.829402 3.884063
## [6,] 4.774678 4.807101
## [7,] 3.043799 3.075083
## [8,] 4.285443 4.324350
## [9,] 4.711693 4.781220
## [10,] 4.072125 4.114151
## [11,] 3.944160 4.029057
## [12,] 4.033075 4.171481
## [13,] 4.104889 4.123284
## [14,] 3.524066 3.532004
## [15,] 3.607147 3.650100
## [16,] 5.561407 5.604068
## [17,] 2.797390 2.865239
## [18,] 3.285467 3.376237
## [19,] 4.474524 4.488485
## [20,] 4.052655 4.142594This 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 0.943 0.999 0.920
## 2 0.541 0.999 0.656
## 3 0.830 0.952 0.534
## 4 0.830 0.944 0.739
## 5 0.830 0.999 0.831
## 6 0.703 0.999 0.534
## 7 0.930 0.967 0.873
## 8 0.989 1 0.674
## 9 0.932 0.999 0.460
## 10 0.967 0.999 0.992
## # 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 -0.0527 -0.0208 0.460 0.588
## 2 -0.0120 -0.240 -0.215 -0.0888
## 3 -0.434 -0.170 -0.150 0.905
## 4 0.540 -0.181 0.0845 0.266
## 5 -0.257 -0.215 0.205 -0.996
## 6 -0.259 0.765 -0.193 -0.342
## 7 -0.219 -0.126 0.0327 0.456
## 8 -0.0951 0.290 -0.0805 0.143
## 9 -0.477 -0.489 -0.486 0.880
## 10 -0.288 0.929 0.653 0.654
## # 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] 0.214 0.296 0.299 0.204 0.255 ...