wilcoxauc: Fast Wilcoxon rank sum test and auROC

In immunogenomics/presto: Fast Functions for Differential Expression using Wilcox and AUC

Fast Wilcoxon rank sum test and auROC

Description

Computes auROC and Wilcoxon p-value based on Gaussian approximation. Inputs can be

• Dense matrix or data.frame

• Sparse matrix, such as dgCMatrix

• Seurat V3 object

• SingleCellExperiment object

For detailed examples, consult the presto vignette.

Usage

wilcoxauc(X, ...)

## S3 method for class 'seurat'
wilcoxauc(X, ...)

## S3 method for class 'Seurat'
wilcoxauc(
  X,
  group_by = NULL,
  assay = "data",
  groups_use = NULL,
  seurat_assay = "RNA",
  ...
)

## S3 method for class 'SingleCellExperiment'
wilcoxauc(X, group_by = NULL, assay = NULL, groups_use = NULL, ...)

## Default S3 method:
wilcoxauc(X, y, groups_use = NULL, verbose = TRUE, ...)

Arguments

X	A feature-by-sample matrix, Seurat object, or SingleCellExperiment object
...	input specific parameters.
group_by	(Seurat & SCE) name of groups variable ('e.g. Cluster').
assay	(Seurat & SCE) name of feature matrix slot (e.g. 'data' or 'logcounts').
groups_use	(optional) which groups from y vector to test.
seurat_assay	(Seurat) name of Seurat Assay (e.g. 'RNA').
y	vector of group labels.
verbose	boolean, TRUE for warnings and messages.

Value

table with the following columns:

• feature - feature name (e.g. gene name).

• group - group name.

• avgExpr - mean value of feature in group.

• logFC - log fold change between observations in group vs out.

• statistic - Wilcoxon rank sum U statistic.

• auc - area under the receiver operator curve.

• pval - nominal p value.

• padj - Benjamini-Hochberg adjusted p value.

• pct_in - Percent of observations in the group with non-zero feature value.

• pct_out - Percent of observations out of the group with non-zero feature value.

Examples

## Not run: 
 data(exprs)
 data(y)

 ## on a dense matrix
 head(wilcoxauc(exprs, y))

 ## with only some groups
 head(wilcoxauc(exprs, y, c('A', 'B')))

 ## on a sparse matrix
 exprs_sparse <- as(exprs, 'dgCMatrix')
 head(wilcoxauc(exprs_sparse, y))

 ## on a Seurat V3 object
 if (requireNamespace("Seurat", quietly = TRUE)) {
     pkg_version <- packageVersion('Seurat')
     if (pkg_version >= "3.0" & pkg_version < "4.0") {
         data(object_seurat)
         head(wilcoxauc(object_seurat, 'cell_type'))
     }
 }

 ## on a SingleCellExperiment object
 if (requireNamespace("SingleCellExperiment", quietly = TRUE)) {
     data(object_sce)
     head(wilcoxauc(object_sce, 'cell_type'))
 }

## End(Not run)

immunogenomics/presto documentation built on March 26, 2024, 8:18 a.m.