get_precision_weights {mpra} | R Documentation

## Get precision weights from the copy number-variance relationship.

### Description

Estimates the variability of the supplied log-ratios across samples as a function of copy number (DNA count levels).

### Usage

    get_precision_weights(logr, design, log_dna, span = 0.4, plot = TRUE, ...)

### Arguments

`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 `logr`.
`span` | The smoothing span for `lowess` in estimating the copy number-variance relationship. Default: 0.4.
`plot` | If `TRUE`, plot the copy number-variance relationship.
`...` | Further arguments to be passed to `lmFit` for obtaining residual standard deviations used in estimating the copy number-variance relationship.

### Details

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.

### Value

A matrix of precision weights of the same dimension as `logr` and `log_dna`.

### References

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.

### Examples

    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)

[Package mpra version 1.16.0 Index]