deswish {fishpond} | R Documentation

## deswish: DESeq2-apeglm With Inferential Samples Helps

### Description

The DESeq2-apeglm With Inferential Samples implementation supposes a hierarchical distribution of log2 fold changes. The final posterior standard deviation is calculated by adding the posterior variance from modeling biological replicates computed by `apeglm`, and the observed variance on the posterior mode over inferential replicates. This function requires the DESeq2 and apeglm packages to be installed and will print an error if they are not found.

### Usage

    deswish(y, x, coef)

### Arguments

`y` | a SummarizedExperiment containing the inferential replicate matrices, as output by `tximeta`, and then with `labelKeep` applied. One does not need to run `scaleInfReps` as scaling is done internally via DESeq2.
`x` | the design matrix
`coef` | the coefficient to test (see `lfcShrink`)

### Value

a SummarizedExperiment with metadata columns added: the log2 fold change and posterior SD using inferential replicates, and the original log2 fold change (apeglm) and its posterior SD

### References

The `DESeq` and `lfcShrink` function in the `DESeq2` package:

Zhu, Ibrahim, Love "Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences" Bioinformatics (2018).

Love, Huber, Anders "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2" Genome Biology (2014).

### Examples

    # a small example... 500 genes, 10 inf reps
    y <- makeSimSwishData(m=500, numReps=10)
    y <- labelKeep(y)
    y <- deswish(y, ~condition, "condition_2_vs_1")

[Package fishpond version 1.4.1 Index]