cuda_hvg: Select highly variable features

In cudacellr: GPU-Aware Single-Cell Analysis Workflows

Select highly variable features

Description

Select highly variable features

Usage

cuda_hvg(counts, n_top = 2000L, min_mean = 0)

Arguments

counts	Feature-by-cell count or normalized expression matrix.
n_top	Number of features to select.
min_mean	Minimum feature mean.

Value

A data frame ordered by dispersion with feature statistics and a selected indicator. The index column retains the original feature position even when feature names are duplicated.

Examples

set.seed(1)
counts <- Matrix::rsparsematrix(20, 10, density = 0.3)
counts@x <- abs(round(counts@x * 5))
cuda_hvg(counts, n_top = 5)

cudacellr documentation built on Oct. 28, 2026, 1:07 a.m.