show_result_haystack.RdShows the results of the 'haystack' analysis in various ways, sorted by significance. Priority of params is genes > p.value.threshold > n.
show_result_haystack(
res.haystack,
n = NULL,
p.value.threshold = NULL,
gene = NULL
)
# S3 method for haystack
show_result_haystack(
res.haystack,
n = NULL,
p.value.threshold = NULL,
gene = NULL
)A 'haystack' result object.
If defined, the top "n" significant genes will be returned. Default: NA, which shows all results.
If defined, genes passing this p-value threshold will be returned.
If defined, the results of this (these) gene(s) will be returned.
A data.frame with 'haystack' results sorted by log.p.vals.
The output is a data.frame with the following columns: * D_KL the calculated KL divergence. * log.p.vals log10 p.values calculated from randomization. * log.p.adj log10 p.values adjusted by Bonferroni correction.
# using the toy example of the singleCellHaystack package
# running haystack
res <- haystack(dat.tsne, dat.expression)
#> ### calling haystack_continuous_highD()...
#> ### Using package sparseMatrixStats to speed up statistics in sparse matrices.
#> ### Calculating row-wise mean and SD...
#> ### Filtered 0 genes with zero variance...
#> ### Using 100 randomizations...
#> ### Using 100 genes to randomize...
#> Warning: The value of 'grid.points' appears to be very high (> No. of cells / 10). You can set the number of grid points using the 'grid.points' parameter.
#> ### scaling input data...
#> ### deciding grid points...
#> ### calculating Kullback-Leibler divergences...
#> ### performing randomizations...
#> ### estimating p-values...
#> ### picking model for mean D_KL...
#> ### using natural splines
#> ### best RMSD : 0.093
#> ### best df : 3
#> ### picking model for stdev D_KL...
#> ### using natural splines
#> ### best RMSD : 0.02
#> ### best df : 3
#> ### returning result...
# below are variations for showing the results in a table
# 1. list top 10 biased genes
show_result_haystack(res.haystack = res, n =10)
#> D_KL log.p.vals log.p.adj
#> gene_62 2.167619 -37.49534 -34.79637
#> gene_351 1.952468 -36.33302 -33.63405
#> gene_275 1.869558 -35.86746 -33.16849
#> gene_497 1.990614 -35.30553 -32.60656
#> gene_339 1.848716 -34.94200 -32.24303
#> gene_79 2.401003 -34.49255 -31.79358
#> gene_242 1.709774 -32.72927 -30.03030
#> gene_71 2.618959 -31.62696 -28.92799
#> gene_213 1.756021 -31.21381 -28.51484
#> gene_325 1.874556 -30.88724 -28.18827
# 2. list genes with p value below a certain threshold
show_result_haystack(res.haystack = res, p.value.threshold=1e-10)
#> D_KL log.p.vals log.p.adj
#> gene_62 2.167619 -37.49534 -34.796370
#> gene_351 1.952468 -36.33302 -33.634046
#> gene_275 1.869558 -35.86746 -33.168493
#> gene_497 1.990614 -35.30553 -32.606562
#> gene_339 1.848716 -34.94200 -32.243033
#> gene_79 2.401003 -34.49255 -31.793578
#> gene_242 1.709774 -32.72927 -30.030295
#> gene_71 2.618959 -31.62696 -28.927990
#> gene_213 1.756021 -31.21381 -28.514842
#> gene_325 1.874556 -30.88724 -28.188271
#> gene_479 2.462010 -29.63185 -26.932879
#> gene_244 1.634253 -29.13092 -26.431949
#> gene_137 1.680041 -29.09412 -26.395152
#> gene_429 1.615984 -28.98509 -26.286125
#> gene_99 1.970877 -28.89432 -26.195353
#> gene_61 1.887008 -28.40971 -25.710744
#> gene_444 1.856239 -28.19758 -25.498606
#> gene_313 1.755483 -28.00972 -25.310752
#> gene_458 1.646122 -27.88507 -25.186098
#> gene_24 2.503064 -25.57603 -22.877061
#> gene_381 2.351667 -25.32954 -22.630567
#> gene_317 1.524015 -25.03824 -22.339265
#> gene_300 2.025872 -24.48818 -21.789212
#> gene_155 2.020291 -24.12024 -21.421271
#> gene_321 2.161541 -23.46944 -20.770465
#> gene_424 2.377840 -22.99977 -20.300805
#> gene_400 2.647292 -22.88906 -20.190094
#> gene_78 3.088231 -22.82156 -20.122586
#> gene_57 2.407546 -22.52389 -19.824916
#> gene_463 2.921912 -22.33673 -19.637764
#> gene_51 2.642381 -22.25577 -19.556797
#> gene_59 2.024199 -22.14167 -19.442696
#> gene_340 2.120703 -21.87009 -19.171118
#> gene_112 1.932236 -20.45545 -17.756477
#> gene_291 3.045928 -20.14897 -17.449995
#> gene_295 2.532697 -18.37318 -15.674210
#> gene_135 2.254088 -18.02469 -15.325721
#> gene_449 3.513541 -17.26738 -14.568413
#> gene_13 2.361233 -16.72864 -14.029672
#> gene_488 3.629939 -16.22173 -13.522755
#> gene_136 2.840323 -16.17383 -13.474857
#> gene_156 2.708703 -15.48886 -12.789891
#> gene_10 2.203270 -15.39893 -12.699962
#> gene_216 2.814330 -14.61612 -11.917146
#> gene_201 2.644354 -13.24347 -10.544500
#> gene_176 3.105065 -13.01437 -10.315395
#> gene_225 2.915940 -12.48868 -9.789706
#> gene_9 3.167582 -10.04909 -7.350124
# 3. list a set of specified genes
set <- c("gene_497","gene_386", "gene_275")
show_result_haystack(res.haystack = res, gene = set)
#> D_KL log.p.vals log.p.adj
#> gene_275 1.8695579 -35.8674630 -33.16849
#> gene_497 1.9906140 -35.3055316 -32.60656
#> gene_386 0.6926282 -0.3668113 0.00000