Checking out the presto package
2025-04-07
Last updated: 2025-04-07
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| Rmd | 417c64f | Dave Tang | 2025-04-07 | Checking out presto |
Presto scales Wilcoxon and auROC analyses to millions of observations
The related Wilcoxon rank sum test and area under the receiver operator curve are ubiquitous in high dimensional biological data analysis. Current implementations do not scale readily to the increasingly large datasets generated by novel high-throughput technologies, such as single cell RNAseq. We introduce a simple and scalable implementation of both analyses, available through the R package Presto. Presto scales to big datasets, with functions optimized for both dense and sparse matrices. On a sparse dataset of 1 million observations, 10 groups, and 1,000 features, Presto performed both rank-sum and auROC analyses in only 17 seconds, compared to 6.4 hours with base R functions. Presto also includes functions to seamlessly integrate with the Seurat single cell analysis pipeline and the Bioconductor SingleCellExperiment class. Presto enables the use of robust classical analyses on big data with a simple interface and optimized implementation.
Install the following packages, if necessary.
remotes::install_github("immunogenomics/presto")Load {presto}.
suppressPackageStartupMessages(library("presto"))
suppressPackageStartupMessages(library("Seurat"))Seurat object
Import raw pbmc3k dataset from my server.
seurat_obj <- readRDS(url("https://davetang.org/file/pbmc3k_seurat.rds", "rb"))
seurat_objAn object of class Seurat
32738 features across 2700 samples within 1 assay
Active assay: RNA (32738 features, 0 variable features)
1 layer present: countsFilter.
pbmc3k <- CreateSeuratObject(
counts = seurat_obj@assays$RNA$counts,
min.cells = 3,
min.features = 200,
project = "pbmc3k"
)
pbmc3kAn object of class Seurat
13714 features across 2700 samples within 1 assay
Active assay: RNA (13714 features, 0 variable features)
1 layer present: countsNormalise.
seurat_obj <- NormalizeData(seurat_obj, normalization.method = "LogNormalize", scale.factor = 1e4, verbose = FALSE)Wilcoxon Rank Sum
Calculate gene variance and use gene with the highest variance for our testing.
gene_var <- apply(seurat_obj@assays$RNA$data, 1, var)
head(sort(gene_var, decreasing = TRUE)) LYZ S100A9 HLA-DRA CST3 TYROBP S100A8
3.598973 3.307156 3.234347 2.901707 2.791768 2.585039 Get the gene expression.
my_gene <- names(head(sort(gene_var, decreasing = TRUE), 1))
gene_exp <- seurat_obj@assays$RNA$data[my_gene, ]
head(gene_exp)AAACATACAACCAC-1 AAACATTGAGCTAC-1 AAACATTGATCAGC-1 AAACCGTGCTTCCG-1
1.635208 1.962726 1.994867 4.521175
AAACCGTGTATGCG-1 AAACGCACTGGTAC-1
0.000000 1.726522 Create two (random) groups.
ngenes <- length(gene_exp)
set.seed(1984)
g1 <- sample(names(gene_exp), ngenes/2)
g2 <- setdiff(names(gene_exp), g1)
stopifnot(length(unique(c(g1, g2))) == ngenes)Plot expression.
x <- gene_exp[g1]
y <- gene_exp[g2]
my_df <- data.frame(
barcode = c(g1, g2),
group = c(rep('g1', ngenes/2), rep('g2', ngenes/2)),
exp = c(x, y)
)
boxplot(
exp~group,
data = my_df,
main = my_gene
)
Perform Wilcoxon Rank Sum and Signed Rank Tests using
wilcox.test.
res <- wilcox.test(exp~group, data = my_df)
res
Wilcoxon rank sum test with continuity correction
data: exp by group
W = 884528, p-value = 0.1731
alternative hypothesis: true location shift is not equal to 0Fast Wilcoxon rank sum test and auROC using
presto::wilcoxauc().
my_mat <- matrix(my_df$exp, nrow = 1)
colnames(my_mat) <- my_df$barcode
rownames(my_mat) <- my_gene
y <- factor(my_df$group)
presto_res <- wilcoxauc(my_mat, y)
presto_res feature group avgExpr logFC statistic auc pval padj
1 LYZ g1 1.802939 -0.08384549 884528.5 0.485338 0.1731151 0.1731151
2 LYZ g2 1.886785 0.08384549 937971.5 0.514662 0.1731151 0.1731151
pct_in pct_out
1 59.62963 61.18519
2 61.18519 59.62963Compare p-values.
res$p.value == presto_res$pval[1][1] TRUEAverage expression.
my_df |>
dplyr::summarise(avgExpr = mean(exp), .by = group) group avgExpr
1 g1 1.802939
2 g2 1.886785Log fold change.
my_df |>
dplyr::summarise(avgExpr = mean(exp), .by = group) |>
dplyr::summarise(across(avgExpr, ~ log(.x[1] / .x[2]))) avgExpr
1 -0.04545594The difference between logFC calculations is a known issue; use the fix suggested by slowkow.
wilcoxauc_mod <- function(X, y, groups_use = NULL, verbose = TRUE, ...) {
## Check and possibly correct input values
if (is(X, "dgeMatrix")) X <- as.matrix(X)
if (is(X, "data.frame")) X <- as.matrix(X)
if (is(X, "dgTMatrix")) X <- as(X, "dgCMatrix")
if (is(X, "TsparseMatrix")) X <- as(X, "dgCMatrix")
if (ncol(X) != length(y)) stop("number of columns of X does not
match length of y")
if (!is.null(groups_use)) {
idx_use <- which(y %in% intersect(groups_use, y))
y <- y[idx_use]
X <- X[, idx_use]
}
y <- factor(y)
idx_use <- which(!is.na(y))
if (length(idx_use) < length(y)) {
y <- y[idx_use]
X <- X[, idx_use]
if (verbose)
message("Removing NA values from labels")
}
group.size <- as.numeric(table(y))
if (length(group.size[group.size > 0]) < 2) {
stop("Must have at least 2 groups defined.")
}
if (is.null(row.names(X))) {
row.names(X) <- paste0("Feature", seq_len(nrow(X)))
}
## Compute primary statistics
group.size <- as.numeric(table(y))
n1n2 <- group.size * (ncol(X) - group.size)
if (is(X, "dgCMatrix")) {
rank_res <- rank_matrix(Matrix::t(X))
} else {
rank_res <- rank_matrix(X)
}
ustat <- presto:::compute_ustat(rank_res$X_ranked, y, n1n2, group.size)
auc <- t(ustat / n1n2)
pvals <- presto:::compute_pval(ustat, rank_res$ties, ncol(X), n1n2)
fdr <- apply(pvals, 2, function(x) p.adjust(x, "BH"))
### Auxiliary Statistics (AvgExpr, PctIn, LFC, etc)
group_sums <- sumGroups(X, y, 1)
group_nnz <- nnzeroGroups(X, y, 1)
group_pct <- sweep(group_nnz, 1, as.numeric(table(y)), "/") %>% t()
group_pct_out <- -group_nnz %>%
sweep(2, colSums(group_nnz) , "+") %>%
sweep(1, as.numeric(length(y) - table(y)), "/") %>% t()
group_means <- sweep(group_sums, 1, as.numeric(table(y)), "/") %>% t()
cs <- colSums(group_sums)
gs <- as.numeric(table(y))
lfc <- Reduce(cbind, lapply(seq_len(length(levels(y))), function(g) {
group_means[, g] / ((cs - group_sums[g, ]) / (length(y) - gs[g]))
})) |> log()
res_list <- list(auc = auc,
pval = pvals,
padj = fdr,
pct_in = 100 * group_pct,
pct_out = 100 * group_pct_out,
avgExpr = group_means,
statistic = t(ustat),
logFC = lfc)
return(presto:::tidy_results(res_list, row.names(X), levels(y)))
}
assignInNamespace("wilcoxauc.default", wilcoxauc_mod, ns = "presto")
wilcoxauc(my_mat, y) feature group avgExpr logFC statistic auc pval padj
1 LYZ g1 1.802939 -0.04545594 884528.5 0.485338 0.1731151 0.1731151
2 LYZ g2 1.886785 0.04545594 937971.5 0.514662 0.1731151 0.1731151
pct_in pct_out
1 59.62963 61.18519
2 61.18519 59.62963
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Seurat_5.2.1 SeuratObject_5.0.2 sp_2.2-0 presto_1.0.0
[5] data.table_1.16.2 Rcpp_1.0.13 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_1.8.9
[4] magrittr_2.0.3 spatstat.utils_3.1-2 farver_2.1.2
[7] rmarkdown_2.28 fs_1.6.4 vctrs_0.6.5
[10] ROCR_1.0-11 spatstat.explore_3.3-4 htmltools_0.5.8.1
[13] sass_0.4.9 sctransform_0.4.1 parallelly_1.38.0
[16] KernSmooth_2.23-24 bslib_0.8.0 htmlwidgets_1.6.4
[19] ica_1.0-3 plyr_1.8.9 plotly_4.10.4
[22] zoo_1.8-13 cachem_1.1.0 whisker_0.4.1
[25] igraph_2.1.4 mime_0.12 lifecycle_1.0.4
[28] pkgconfig_2.0.3 Matrix_1.7-0 R6_2.5.1
[31] fastmap_1.2.0 fitdistrplus_1.2-2 future_1.34.0
[34] shiny_1.10.0 digest_0.6.37 colorspace_2.1-1
[37] patchwork_1.3.0 ps_1.8.1 rprojroot_2.0.4
[40] tensor_1.5 RSpectra_0.16-2 irlba_2.3.5.1
[43] progressr_0.15.0 spatstat.sparse_3.1-0 httr_1.4.7
[46] polyclip_1.10-7 abind_1.4-8 compiler_4.4.1
[49] withr_3.0.2 fastDummies_1.7.5 highr_0.11
[52] MASS_7.3-60.2 tools_4.4.1 lmtest_0.9-40
[55] httpuv_1.6.15 future.apply_1.11.3 goftest_1.2-3
[58] glue_1.8.0 callr_3.7.6 nlme_3.1-164
[61] promises_1.3.2 grid_4.4.1 Rtsne_0.17
[64] getPass_0.2-4 cluster_2.1.6 reshape2_1.4.4
[67] generics_0.1.3 gtable_0.3.6 spatstat.data_3.1-4
[70] tidyr_1.3.1 spatstat.geom_3.3-5 RcppAnnoy_0.0.22
[73] ggrepel_0.9.6 RANN_2.6.2 pillar_1.10.1
[76] stringr_1.5.1 spam_2.11-1 RcppHNSW_0.6.0
[79] later_1.3.2 splines_4.4.1 dplyr_1.1.4
[82] lattice_0.22-6 survival_3.6-4 deldir_2.0-4
[85] tidyselect_1.2.1 miniUI_0.1.1.1 pbapply_1.7-2
[88] knitr_1.48 git2r_0.35.0 gridExtra_2.3
[91] scattermore_1.2 xfun_0.48 matrixStats_1.5.0
[94] stringi_1.8.4 lazyeval_0.2.2 yaml_2.3.10
[97] evaluate_1.0.1 codetools_0.2-20 tibble_3.2.1
[100] cli_3.6.3 uwot_0.2.3 xtable_1.8-4
[103] reticulate_1.41.0 munsell_0.5.1 processx_3.8.4
[106] jquerylib_0.1.4 globals_0.16.3 spatstat.random_3.3-2
[109] png_0.1-8 spatstat.univar_3.1-2 parallel_4.4.1
[112] ggplot2_3.5.1 dotCall64_1.2 listenv_0.9.1
[115] viridisLite_0.4.2 scales_1.3.0 ggridges_0.5.6
[118] purrr_1.0.2 rlang_1.1.4 cowplot_1.1.3