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CS-CORE for cell-type-specific co-expression network inference

CSCORE.Rd

Run CS-CORE on a Seurat object to infer the cell-type-specific co-expression network for a specified set of genes, with optional adjustment for covariates. For more details on the covariate adjustment and the moment-based regression, please refer to CSCORE_IRLS.

Usage

CSCORE(
  object,
  genes,
  seq_depth = NULL,
  covariate_names = NULL,
  adjust_setting = c(mean = T, var = T, covar = T),
  IRLS_version = "Rcpp",
  IRLS_par = list(n_iter = 10, eps = 0.05, verbose = FALSE)
)

Arguments

object

A Seurat object containing single-cell RNA-seq data. The object should be subsetted to cells of a single cell type to ensure cell-type-specific inference. CS-CORE requires raw UMI counts as input, and assumes that the raw count matrix is stored in the "counts" slot of the "RNA" assay (i.e., object[["RNA"]]@counts).

genes

A character vector of gene names (length \(p\)) for which the co-expression network will be estimated.

seq_depth

A numeric vector of sequencing depths (length \(n\)). If NULL, sequencing depth will be computed as the total UMI count per cell. Defaults to NULL.

covariate_names

Optional. A character vector specifying the names of cell-level covariates to adjust for in the regression models. These variables will be extracted from object@meta.data[, covariate_names]. Defaults to NULL.

adjust_setting

Optional. A named logical vector of length 3 indicating whether to adjust for covariates in the estimation of mean, variance, and covariance. Must be named c("mean", "var", "covar"). Defaults to c(mean = TRUE, var = TRUE, covar = TRUE).

IRLS_version

Optional. A character string specifying the IRLS implementation to use: "Rcpp" or "base_R". Only the "Rcpp" version supports covariate adjustment. The "base_R" version does not. When applicable, "Rcpp" offers improved memory efficiency (~10-100 times) but may be slower (~10 times), while "base_R" is faster but more memory intensive. Defaults to "Rcpp".

IRLS_par

Optional. A named list of length 3 specifying parameters for the IRLS algorithm:

n_iter

Maximum number of iterations.

eps

Convergence threshold for log-ratio change delta, computed as abs(log(theta / theta_prev)).

verbose

Logical; whether to print the convergence metric (delta) at each iteration.

Defaults to list(n_iter = 10, eps = 0.05, verbose = FALSE).

Value

A list of three p by p matrices:

est

Matrix of co-expression estimates.

p_value

Matrix of p-values for testing co-expression.

test_stat

Matrix of test statistics for evaluating the significance of co-expression.

See also

CSCORE_IRLS

CS-CORE online tutorial

Examples

# See a full example at:
# https://changsubiostats.github.io/CS-CORE/articles/CSCORE.html