lm_adjust: Linear Adjustment Normalization
In scone: Single Cell Overview of Normalized Expression data

Description Usage Arguments Details Value Examples

Given a matrix with log expression values and a design matrix, this function fits a linear model and removes the effects of the batch factor as well as of the linear variables encoded in W.

1	lm_adjust(log_expr, design_mat, batch = NULL, weights = NULL)

`log_expr`	matrix. The log gene expression (genes in row, samples in columns).
`design_mat`	matrix. The design matrix (usually the result of make_design).
`batch`	factor. A factor with the batch information, identifying batch effect to be removed.
`weights`	matrix. A matrix of weights.

The function assumes that the columns of the design matrix corresponding to the variable for which expression needs to be adjusted, start with either the word "batch" or the letter "W" (case sensitive). Any other covariate (including the intercept) is kept.

The corrected log gene expression.

set.seed(141)
bio = as.factor(rep(c(1,2),each = 2))
batch = as.factor(rep(c(1,2),2))
design_mat = make_design(bio,batch, W = NULL)

log_expr = matrix(rnorm(20),ncol = 4)
adjusted_log_expr = lm_adjust(log_expr = log_expr,
  design_mat = design_mat,
  batch = batch)