mpra-package {mpra} | R Documentation

## Analyze massively parallel reporter assays

### Description

Tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA).

### Details

This package provides tools for the analysis of MPRA data. The primary purpose is to enable powerful differential analysis of activity measures, but the package can also be used to generate precision weights useful in regression analyses of activity scores on sequence features. The main workhorse is the `mpralm` function which draws on the previously proposed `voom` framework for RNA-seq analysis in the `limma` package.

### Author(s)

NA

Maintainer: NA

### References

Myint, Leslie, Dimitrios G. Avramopoulos, Loyal A. Goff, and Kasper D. Hansen. Linear models enable powerful differential activity analysis in massively parallel reporter assays. BMC Genomics 2019, 209. doi: 10.1186/s12864-019-5556-x.

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.

Smyth, Gordon K., Jo\"elle Michaud, and Hamish S. Scott. Use of within-Array Replicate Spots for Assessing Differential Expression in Microarray Experiments. Bioinformatics 2005, 21 (9): 2067-75. doi: 10.1093/bioinformatics/bti270.

### Examples

    data(mpraSetAggExample)
    design <- data.frame(intcpt = 1,
                         episomal = grepl("MT", colnames(mpraSetAggExample)))
    mpralm_fit <- mpralm(object = mpraSetAggExample, design = design,
                         aggregate = "none", normalize = TRUE,
                         model_type = "indep_groups", plot = FALSE)
    toptab <- topTable(mpralm_fit, coef = 2, number = Inf)
    head(toptab)

[Package mpra version 1.16.0 Index]