plot_sexcheck_pca: Explore metadata by gene expression on the sex chromosomes...

In Sage-Bionetworks/sageseqr: Identify differentially expressed genes counts data

Explore metadata by gene expression on the sex chromosomes (PCA)

Description

Principal Component Analysis (PCA) of sex-specific genes. Samples greater than z standard deviations (SDs) from the mean of sample sub-groups identified as outliers.

Usage

plot_sexcheck_pca(clean_metadata, count_df, biomart_results, sex_var)

Arguments

clean_metadata	A data frame with sample identifiers as rownames and variables as factors or numeric as determined by "sageseqr::clean_covariates()".
count_df	A counts data frame with sample identifiers as column names and gene Ids are rownames.
biomart_results	Output of "sageseqr::get_biomart()". Gene Ids are stored as rownames.
sex_var	Column name of the sex or gender-specific metadata.

Sage-Bionetworks/sageseqr documentation built on June 13, 2024, 2:11 p.m.