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] 911 830 245 977 432 418 250 193 382 119 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 911 264 359 957 370 224 129 175 877 997
## [2,] 830 952 330 514 70 464 367 61 747 575
## [3,] 245 707 689 565 763 996 228 553 559 384
## [4,] 977 466 999 889 746 27 760 568 565 912
## [5,] 432 136 102 593 769 282 756 663 517 954
## [6,] 418 171 361 825 563 75 334 733 80 897
## [7,] 250 152 279 160 85 891 161 368 878 332
## [8,] 193 167 705 957 886 57 613 656 205 10
## [9,] 382 498 933 306 584 949 917 242 317 204
## [10,] 119 205 194 377 705 77 145 166 789 860
## [11,] 153 345 768 116 405 655 980 694 844 484
## [12,] 931 503 893 605 834 363 936 964 598 461
## [13,] 778 700 666 846 720 283 750 268 144 900
## [14,] 205 611 719 180 161 80 616 252 119 957
## [15,] 586 380 618 215 921 195 416 824 500 661
## [16,] 665 142 945 914 705 152 703 324 270 57
## [17,] 878 719 213 842 179 457 860 185 14 58
## [18,] 911 530 957 164 31 708 549 611 892 457
## [19,] 591 369 146 329 902 635 712 212 950 227
## [20,] 200 900 316 434 823 368 530 172 57 880## num [1:1000, 1:30] 3.59 4.68 2.17 3.17 3.85 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 3.586537 3.729572 3.891359 3.900928 3.931624 3.992877 4.018576 4.058339
## [2,] 4.682708 4.821225 4.938197 5.065203 5.100889 5.122794 5.168790 5.189825
## [3,] 2.166226 2.417205 2.533775 2.565229 2.582945 2.602986 2.670389 2.696508
## [4,] 3.166097 3.250618 3.331922 3.377122 3.404695 3.511901 3.516361 3.519392
## [5,] 3.850334 4.131262 4.191703 4.206895 4.236631 4.281859 4.301663 4.440255
## [6,] 3.865158 4.031615 4.159029 4.364631 4.475063 4.544673 4.559770 4.573801
## [7,] 3.717725 3.845536 3.920553 3.951215 3.991113 4.008832 4.059086 4.157371
## [8,] 3.160703 3.264204 3.437360 3.555439 3.595931 3.616157 3.619084 3.624309
## [9,] 3.980797 4.139264 4.217798 4.391363 4.427340 4.482436 4.516196 4.527090
## [10,] 2.979887 3.003804 3.013099 3.063431 3.129153 3.193783 3.221482 3.252045
## [11,] 3.155625 3.266114 3.397197 3.643457 3.748741 3.849347 3.886697 3.905109
## [12,] 3.862040 4.085290 4.089073 4.104447 4.200690 4.310497 4.355745 4.516146
## [13,] 3.104207 3.332471 3.463413 3.522466 3.580990 3.669855 3.684584 3.713510
## [14,] 2.686813 3.203988 3.206925 3.218411 3.229940 3.268903 3.350932 3.380427
## [15,] 3.348177 3.504714 3.789941 3.832154 3.942463 3.982561 4.035097 4.164026
## [16,] 3.284649 3.550185 3.659957 3.815576 3.850592 3.939764 4.082309 4.109970
## [17,] 3.172684 3.333152 3.565380 3.609622 3.643893 3.675832 3.679565 3.695514
## [18,] 3.802872 4.107313 4.148939 4.291177 4.387384 4.400052 4.444378 4.654596
## [19,] 2.936871 3.195534 3.589198 3.615391 3.704460 3.735535 3.926506 3.958820
## [20,] 3.772988 3.919439 4.108895 4.182027 4.257397 4.347584 4.360755 4.477520
## [,9] [,10]
## [1,] 4.160121 4.175377
## [2,] 5.272487 5.285888
## [3,] 2.709469 2.734650
## [4,] 3.594389 3.642005
## [5,] 4.451459 4.516786
## [6,] 4.624760 4.713728
## [7,] 4.173900 4.177814
## [8,] 3.690309 3.705418
## [9,] 4.554737 4.620931
## [10,] 3.262275 3.278747
## [11,] 3.916193 3.954783
## [12,] 4.533751 4.551000
## [13,] 3.761874 3.770497
## [14,] 3.390087 3.412378
## [15,] 4.165959 4.167254
## [16,] 4.173615 4.177621
## [17,] 3.698709 3.707276
## [18,] 4.717707 4.720670
## [19,] 3.968345 3.985265
## [20,] 4.479676 4.490707This 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 0.999 0.886 0.827
## 2 0.999 0.824 0.782
## 3 0.999 0.933 0.773
## 4 0.999 0.824 0.994
## 5 0.999 0.824 0.973
## 6 0.999 0.750 0.873
## 7 0.999 0.833 0.993
## 8 0.999 0.961 0.953
## 9 0.999 0.796 0.994
## 10 0.954 0.926 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 0.0567 0.718 0.428 0.899
## 2 -0.215 -0.250 -0.0990 -0.294
## 3 -0.131 -0.106 -0.294 0.683
## 4 -0.143 -0.484 -0.289 -0.892
## 5 0.733 -0.0173 0.321 0.889
## 6 -0.641 -0.621 -0.456 0.695
## 7 -0.139 -0.148 -0.652 -0.308
## 8 -0.299 -0.862 -0.699 -0.587
## 9 -0.263 -0.0122 -0.107 0.281
## 10 -0.326 -0.0343 1.04 0.157
## # 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] 0.238 0.18 0.348 0.27 0.215 ...