mpra-package: Analyze massively parallel reporter assays
In mpra: Analyze massively parallel reporter assays

Description Details Author(s) References Examples

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

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.

Leslie Myint [cre, aut], Kasper D. Hansen [aut]

Maintainer: Leslie Myint <leslie.myint@gmail.com>

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.

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)