run.all.lmers: Function that will run a regression for your variable of...

In syyang93/lmeR: What the Package Does (One Line, Title Case)

Description

Function that will run a regression for your variable of interest across all genes/metabolites/CpGs, in parallel. Adapted from Vamsee (github.com/vkp3/pillalamarRi)

Usage

run.all.lmers(
  tx_expr,
  cov,
  rcov,
  gene.ids,
  SCORE,
  omit.outlier = T,
  num.cores = 10
)

Arguments

tx_expr	Expression matrix in form: genes x samples. Will be converted to a list(!) of gene-expr vectors (if not input as list)
cov	Regression covariates (fixed effects) cov x samples
rcov	Regression covariates to be treated as random effects
gene.ids	Character vector of gene IDs, corresponding to rows in tx_expr genes x samples
SCORE	Main covariate to be permuted (not included in cov)
omit.outlier	Whether or not you want to omit gene expression outliers
num.cores	The number of cores you would like to use

Value

All regression coefficients for lmer(gene expression ~ SCORE + cov + (1|rcov)) for all genes

Examples

tx_expr <- w.mt[,which(colnames(w.mt) %in% make.names(metablist))]
cov <- w.mt[,which(colnames(w.mt) %in% c('sex', 'age1c', 'PC1', 'PC2', 'PC3', 'bmi', 'Exam'))]
rcov <- w.mt[,which(colnames(w.mt) %in% c('idno'))]
SCORE <- w.mt[,which(colnames(w.mt) == "dpcrAdjMetric")]
lm_res.sort <- run.all.lmers(tx_expr, cov, rcov, colnames(tx_expr), SCORE, omit.outlier = T, num.cores = 10)

syyang93/lmeR documentation built on July 16, 2020, 12:49 a.m.