mpralm: Linear models for differential analysis of MPRA data

Description Usage Arguments Details Value References Examples

View source: R/fit.R

Description

Fits weighted linear models to test for differential activity in MPRA data.

Usage

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mpralm(object, design, aggregate = c("mean", "sum", "none"), normalize = TRUE,
       block = NULL, model_type = c("indep_groups", "corr_groups"),
       plot = TRUE, ...)

Arguments

object

An object of class MPRASet.

design

Design matrix specifying comparisons of interest. The number of rows of this matrix should equal the number of columns in object. The number of columns in this design matrix has no constraints and should correspond to the experimental design.

aggregate

Aggregation method over barcodes: "mean" to use the average of barcode-specific log ratios, "sum" to use the log ratio of summed RNA and DNA counts, "none" to perform no aggregation (counts have already been summarized over barcodes).

normalize

If TRUE, perform total count normalization before model fitting.

block

A vector giving the sample designations of the columns of object. The default, NULL, indicates that all columns are separate samples.

model_type

Indicates whether an unpaired model fit ("indep_groups") or a paired mixed-model fit (("corr_groups")) should be used.

plot

If TRUE, plot the mean-variance relationship.

...

Further arguments to be passed to lmFit for obtaining residual standard deviations used in estimating the mean-variance relationship.

Details

Using method_type = "corr_groups" use the duplicateCorrelation function from the limma package to estimate the intra-replicate correlation of log-ratio values.

Value

An object of class MArrayLM resulting from the eBayes function.

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

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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)

mpra documentation built on Feb. 28, 2021, 2:01 a.m.