Crescendo for single-gene correction in single-cell RNA-sequencing data
Implementation of the Crescendo algorithm, which allows investigators to remove the effects of confounding factors by directly correcting gene expression count data.
Install the current version of Crescendo from GitHub with:
# install.packages("devtools")
devtools::install_github("immunogenomics/crescendo")The following code uses Crescendo to correct gene expression from a public 10X dataset containing 3 batches (3pV1, 3pV2, and 5p)
library(crescendo)
# Load dataset with metadata, raw gene counts, and Harmony clusters (result from running the Harmony algorithm)
obj <- readRDS(system.file("extdata", "pbmc_4gene_obj.rds", package = "crescendo"))
# Set which genes to correct and parameters for coorrection
batch_var <- 'batch'
genes_use <- c('TRAC', 'MS4A1')
prop <- 0.05
min_cells <- 50
mc.cores <- NULL
lambda <- NULL
alpha <- 0
# Run Crescendo
corr_counts <- crescendo(
Ycounts = obj$exprs_raw,
meta_data = obj$meta_data,
R = obj$R,
batch_var = 'batch',
genes_use = genes_use,
prop = prop,
min_cells = min_cells,
lambda = lambda,
alpha = alpha,
mc.cores = mc.cores,
return_obj = FALSE,
verbose = FALSE
# verbose = TRUE
)