get_precision_weights {mpra} | R Documentation |
Estimates the variability of the supplied log-ratios across samples as a function of copy number (DNA count levels).
get_precision_weights(logr, design, log_dna, span = 0.4, plot = TRUE, ...)
logr |
Matrix of outcome measures: log2 ratio of RNA counts to DNA counts. |
design |
Design matrix specifying comparisons of interest. |
log_dna |
Matrix of log2 aggregated DNA counts of the same dimension as |
span |
The smoothing span for |
plot |
If |
... |
Further arguments to be passed to |
Residual standard deviations are computed using the supplied outcomes and design matrix. The square root of the the residual standard deviations are modeled as a function of the average log2 aggregated DNA counts to estimate the copy number-variance relationship.
A matrix of precision weights of the same dimension as logr
and log_dna
.
Law, Charity W., Yunshun Chen, Wei Shi, and Gordon K. Smyth. Voom: Precision Weights Unlock Linear Model Analysis Tools for RNA-Seq Read Counts. Genome Biology 2014, 15:R29. doi: 10.1186/gb-2014-15-2-r29.
data(mpraSetAggExample) design <- data.frame(intcpt = 1, episomal = grepl("MT", colnames(mpraSetAggExample))) logr <- compute_logratio(mpraSetAggExample, aggregate = "none") log_dna <- log2(getDNA(mpraSetAggExample, aggregate = FALSE) + 1) w <- get_precision_weights(logr = logr, design = design, log_dna = log_dna, plot = FALSE)