mpralm {mpra} | R Documentation

## Linear models for differential analysis of MPRA data

### Description

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

### Usage

    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. bioRxiv 2017, 196394. doi: 10.1101/196394.

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 1.2.0 Index]