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] 558 790 878 565 919 619 881 622 544 399 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 558 651 766 154 627 729 74 546 712 762
## [2,] 790 161 694 200 550 545 894 757 441 282
## [3,] 878 557 131 836 909 804 278 948 577 142
## [4,] 565 496 135 785 964 113 899 462 184 136
## [5,] 919 605 780 127 230 703 544 900 992 517
## [6,] 619 360 312 538 11 699 846 959 37 485
## [7,] 881 949 319 757 741 803 674 518 790 942
## [8,] 622 697 311 795 285 902 349 594 345 229
## [9,] 544 371 517 660 146 788 667 459 236 856
## [10,] 399 946 699 850 923 875 705 513 885 604
## [11,] 699 399 289 793 155 513 312 360 10 570
## [12,] 946 699 215 861 535 549 10 538 814 405
## [13,] 623 810 609 233 460 747 176 641 582 625
## [14,] 684 277 411 807 240 823 129 709 628 389
## [15,] 833 385 784 27 453 822 486 317 790 282
## [16,] 379 803 154 976 969 450 239 562 693 402
## [17,] 888 144 935 19 664 739 368 768 47 363
## [18,] 710 699 11 642 289 431 846 861 372 104
## [19,] 368 888 24 17 416 272 771 664 298 707
## [20,] 150 598 792 542 34 192 825 743 663 646# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 2.83 2.35 3.39 5.32 4.07 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 2.827524 2.894920 3.079853 3.154524 3.174339 3.363991 3.394503 3.399172
## [2,] 2.349204 2.639848 2.737095 2.754134 2.757017 2.811023 2.812476 2.862488
## [3,] 3.392868 3.395322 3.581615 3.708442 3.768758 3.777349 3.828076 3.847425
## [4,] 5.324985 5.355285 5.520704 5.607854 5.638222 5.672028 5.698508 5.703252
## [5,] 4.070849 4.318927 4.369049 4.394431 4.458590 4.520748 4.557760 4.666088
## [6,] 4.047676 4.059920 4.098572 4.114642 4.216622 4.230339 4.280281 4.305231
## [7,] 2.424238 2.700455 2.933224 3.001413 3.010443 3.070859 3.106239 3.126310
## [8,] 4.050647 4.296608 4.339992 4.404500 4.637186 5.001013 5.307969 5.445113
## [9,] 3.988519 4.119575 4.349510 4.398724 4.533777 4.803351 4.834455 4.839624
## [10,] 2.134410 2.205577 2.279481 2.373197 2.570987 2.682794 2.695323 2.699456
## [11,] 2.608587 2.620092 2.626894 2.649969 2.665047 2.731307 2.763009 2.867010
## [12,] 2.583246 2.614677 2.914022 2.948166 3.087103 3.134600 3.164791 3.165685
## [13,] 3.616236 3.965962 4.168260 4.186605 4.213913 4.259835 4.284406 4.294126
## [14,] 4.057206 4.335591 4.338888 4.385952 4.634958 4.675726 4.695690 4.711066
## [15,] 3.638352 3.684760 3.706393 3.729572 3.783449 3.810258 3.822452 3.835881
## [16,] 2.706844 2.852736 2.911103 2.970434 3.026926 3.050343 3.092596 3.156216
## [17,] 3.160619 3.206958 3.276811 3.382013 3.404873 3.461562 3.521048 3.566801
## [18,] 2.994185 3.127637 3.165548 3.295153 3.327055 3.331643 3.340350 3.524829
## [19,] 2.974989 3.139709 3.358817 3.382013 3.487800 3.513848 3.535705 3.621205
## [20,] 4.388845 4.793897 4.827571 4.879548 4.976408 5.034941 5.067369 5.175346
## [,9] [,10]
## [1,] 3.446175 3.457934
## [2,] 2.881418 2.927191
## [3,] 3.956950 3.974883
## [4,] 5.714204 5.739920
## [5,] 4.673122 4.786557
## [6,] 4.307037 4.333251
## [7,] 3.146414 3.168111
## [8,] 5.536845 5.646037
## [9,] 4.914221 4.962969
## [10,] 2.717498 2.759566
## [11,] 2.874532 2.888542
## [12,] 3.260139 3.281108
## [13,] 4.304534 4.354315
## [14,] 4.717029 4.782837
## [15,] 3.884427 3.914170
## [16,] 3.163865 3.184915
## [17,] 3.576753 3.578287
## [18,] 3.548353 3.636292
## [19,] 3.678958 3.706706
## [20,] 5.224433 5.341541This 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 0.942 1 1
## 2 0.772 1 0.934
## 3 1 1 1
## 4 0.951 1 1
## 5 0.999 1 0.540
## 6 1 1 1
## 7 0.755 1 1
## 8 1 1 1
## 9 0.999 1 1
## 10 0.755 1 1
## # 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 -0.697 -0.789 -0.706 -1.01
## 2 -0.0101 -0.406 -0.128 -0.603
## 3 -0.0656 -0.471 -0.0767 0.202
## 4 0.272 -0.133 0.572 0.471
## 5 -0.173 -0.127 -0.149 -0.246
## 6 -0.967 -0.424 -1.12 -0.497
## 7 -0.230 -0.227 -0.117 0.240
## 8 0.291 -0.161 -0.104 -1.50
## 9 -0.121 -0.0609 -0.297 0.445
## 10 -0.113 -0.0204 -0.118 -0.894
## # 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] 0.29 0.325 0.239 0.171 0.203 ...