Compiled: October 07, 2020
This vignette demonstrates the use of the Presto package in Seurat. Commands and parameters are based off of the Presto tutorial. If you use Presto in your work, please cite:
Presto scales Wilcoxon and auROC analyses to millions of observations
Ilya Korsunsky, Aparna Nathan, Nghia Millard, Soumya Raychaudhuri
bioRxiv, 2019.
Pre-print: https://www.biorxiv.org/content/10.1101/653253v1.full.pdf
Prerequisites to install:
library(presto)
library(Seurat)
library(SeuratData)
library(SeuratWrappers)
To learn more about this dataset, type ?pbmc3k
InstallData("pbmc3k")
data("pbmc3k")
pbmc3k <- NormalizeData(pbmc3k)
Idents(pbmc3k) <- "seurat_annotations"
diffexp.B.Mono <- RunPresto(pbmc3k, "CD14+ Mono", "B")
head(diffexp.B.Mono, 10)
## p_val avg_logFC pct.1 pct.2 p_val_adj
## CD79A 1.660326e-143 -2.989854 0.042 0.936 2.276972e-139
## TYROBP 3.516407e-138 3.512505 0.994 0.102 4.822401e-134
## S100A9 7.003189e-137 4.293303 0.996 0.134 9.604174e-133
## CST3 1.498348e-135 3.344758 0.992 0.174 2.054834e-131
## S100A4 8.872946e-135 2.854897 1.000 0.360 1.216836e-130
## LYZ 2.720838e-134 3.788514 1.000 0.422 3.731357e-130
## S100A8 3.115452e-133 4.039777 0.975 0.076 4.272530e-129
## CD79B 8.317731e-133 -2.667534 0.083 0.916 1.140694e-128
## S100A6 5.156920e-132 2.541609 0.996 0.352 7.072201e-128
## LGALS1 1.427548e-131 3.002493 0.979 0.131 1.957739e-127
diffexp.all <- RunPrestoAll(pbmc3k)
head(diffexp.all[diffexp.all$cluster == "B", ], 10)
## p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene
## CD79A.3 0.000000e+00 2.933865 0.936 0.044 0.000000e+00 B CD79A
## MS4A1.3 0.000000e+00 2.290577 0.855 0.055 0.000000e+00 B MS4A1
## LINC00926.1 2.998236e-274 1.956493 0.564 0.010 4.111781e-270 B LINC00926
## CD79B.3 1.126919e-273 2.381160 0.916 0.144 1.545457e-269 B CD79B
## TCL1A.3 1.962618e-272 2.463556 0.622 0.023 2.691534e-268 B TCL1A
## HLA-DQA1.2 3.017803e-267 2.104207 0.890 0.119 4.138616e-263 B HLA-DQA1
## VPREB3 2.131575e-238 1.667466 0.488 0.008 2.923242e-234 B VPREB3
## HLA-DQB1.2 2.076231e-230 2.112052 0.863 0.148 2.847343e-226 B HLA-DQB1
## CD74.2 1.000691e-184 2.010688 1.000 0.819 1.372347e-180 B CD74
## HLA-DRA.3 1.813356e-184 1.914531 1.000 0.492 2.486837e-180 B HLA-DRA
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