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] 317 563 497 698 772 997 168 81 195 767 ...wand.nn[[1]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 317 664 74 839 453 313 632 535 69 92
## [2,] 563 539 753 640 926 353 623 862 49 780
## [3,] 497 937 882 503 968 89 90 205 165 499
## [4,] 698 416 551 435 131 86 544 946 377 906
## [5,] 772 377 286 416 679 935 133 999 542 136
## [6,] 997 168 191 387 549 167 649 899 376 216
## [7,] 168 736 376 427 997 76 185 6 510 371
## [8,] 81 924 933 368 128 64 619 362 72 509
## [9,] 195 491 70 585 987 986 217 153 178 54
## [10,] 767 130 174 962 346 791 765 818 423 749
## [11,] 153 651 840 485 981 961 148 847 550 489
## [12,] 589 314 91 170 549 349 525 687 216 622
## [13,] 806 115 38 993 512 865 259 495 336 823
## [14,] 642 959 627 775 145 352 430 369 908 401
## [15,] 76 928 589 207 768 413 914 539 670 371
## [16,] 304 643 222 300 933 355 538 842 187 924
## [17,] 622 521 63 841 881 100 71 371 978 956
## [18,] 376 736 387 678 899 204 564 265 6 577
## [19,] 691 433 304 46 424 187 738 1000 988 458
## [20,] 389 660 782 776 219 806 96 843 790 186# Distance
str(wand.nn[[2]])## num [1:1000, 1:30] 2.62 3.71 2.53 3.22 2.94 ...wand.nn[[2]][1:20, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 2.622165 2.792039 2.857515 2.919274 2.920141 2.985558 3.054258 3.104714
## [2,] 3.706606 3.783263 3.993242 4.153796 4.176097 4.333011 4.371364 4.436539
## [3,] 2.531550 2.680070 2.901289 2.959863 2.990677 3.067011 3.181871 3.206544
## [4,] 3.216050 3.676839 3.708048 3.810186 3.813260 3.887431 3.911686 3.918353
## [5,] 2.936505 3.175475 3.397790 3.442462 3.499053 3.511781 3.523775 3.630752
## [6,] 3.642676 3.863808 3.958279 4.019967 4.050739 4.106526 4.110042 4.214606
## [7,] 3.645440 4.285236 4.359537 4.392323 4.445316 4.449738 4.500594 4.578365
## [8,] 4.085345 4.093142 4.241363 4.243222 4.253033 4.306909 4.309967 4.333221
## [9,] 3.656178 3.762669 3.980797 4.156667 4.228298 4.262527 4.266058 4.354946
## [10,] 2.967065 3.094524 3.149332 3.290095 3.307555 3.352743 3.356033 3.449334
## [11,] 3.577952 3.590617 3.695503 3.787307 3.798852 3.998825 4.011109 4.017709
## [12,] 3.379999 3.632355 3.786198 3.924678 3.957302 3.969359 3.974060 4.005788
## [13,] 2.710660 2.824527 2.880800 2.924190 2.935566 2.967586 3.107892 3.113533
## [14,] 3.714277 4.095550 4.996903 5.021013 5.183568 5.193347 5.433805 5.434760
## [15,] 3.197880 3.920033 4.024391 4.148769 4.154574 4.308642 4.343474 4.401813
## [16,] 3.132644 3.478325 3.576664 3.612197 3.754792 3.778793 3.809381 3.829707
## [17,] 3.631353 3.836526 3.935926 3.967010 4.009265 4.033075 4.040213 4.171481
## [18,] 4.859045 5.031070 5.231935 5.243134 5.332073 5.610731 5.686843 5.714301
## [19,] 3.541029 3.548345 4.059065 4.151482 4.213986 4.230856 4.438286 4.565029
## [20,] 3.031373 3.182182 3.190289 3.335644 3.394667 3.438324 3.450229 3.482824
## [,9] [,10]
## [1,] 3.109715 3.134536
## [2,] 4.487755 4.575667
## [3,] 3.222205 3.262160
## [4,] 4.008407 4.012537
## [5,] 3.637565 3.664416
## [6,] 4.353556 4.404047
## [7,] 4.611345 4.796276
## [8,] 4.350494 4.360575
## [9,] 4.393399 4.422883
## [10,] 3.564171 3.575291
## [11,] 4.069016 4.165658
## [12,] 4.134983 4.169041
## [13,] 3.161823 3.241092
## [14,] 5.522612 5.808574
## [15,] 4.410626 4.419296
## [16,] 3.842770 3.880138
## [17,] 4.269381 4.294548
## [18,] 5.769977 5.792674
## [19,] 4.579494 4.588447
## [20,] 3.515227 3.532004This 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 0.995 0.961 0.937
## 2 0.998 0.961 0.981
## 3 0.998 0.974 0.870
## 4 0.995 0.974 0.944
## 5 0.995 0.974 0.722
## 6 0.998 0.974 0.937
## 7 0.995 0.974 0.953
## 8 0.998 0.974 1
## 9 0.995 1 0.937
## 10 0.995 0.974 0.937
## # 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 -0.138 -0.170 0.651 -0.824
## 2 -0.0648 -0.0166 -0.0818 -0.681
## 3 -0.0498 0.201 0.573 -0.355
## 4 -0.302 -0.677 0.955 -1.54
## 5 -0.262 -0.454 -0.110 -0.447
## 6 -0.0604 -0.246 -0.243 -0.914
## 7 -1.44 -0.308 -0.640 0.571
## 8 0.610 -0.718 1.59 -1.71
## 9 -0.823 -1.07 -1.29 -0.0692
## 10 -0.610 -0.613 1.13 -2.39
## # 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] 0.313 0.206 0.298 0.247 0.272 ...