mpra-package | R Documentation |
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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/gb-2014-15-2-r29")}.
Smyth, Gordon K., Joelle Michaud, and Hamish S. Scott. Use of within-Array Replicate Spots for Assessing Differential Expression in Microarray Experiments. Bioinformatics 2005, 21 (9): 2067-75. \Sexpr[results=rd]{tools:::Rd_expr_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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.