es_meta {crossmeta} | R Documentation

## Effect size combination meta analysis.

### Description

Performs effect-size meta-analyses across all studies and seperately for each tissue source.

### Usage

    es_meta(diff_exprs, cutoff = 0.3, by_source = FALSE)

### Arguments

`diff_exprs` | Previous result of `diff_expr`, which can be reloaded using `load_diff`.
`cutoff` | Minimum fraction of contrasts that must have measured each gene. Between 0 and 1.
`by_source` | Should seperate meta-analyses be performed for each tissue source added with `add_sources`?

### Details

Builds on `zScores` function from GeneMeta by allowing for genes that were not measured in all studies. This implementation also uses moderated unbiased effect sizes calculated by `effectsize` from metaMA and determines false discovery rates using `fdrtool`.

### Value

A list of named lists, one for each tissue source. Each list contains two named data.frames. The first, `filt`, has all the columns below for genes present in cutoff or more fraction of contrasts. The second, `raw`, has only `dprime` and `vardprime` columns, but for all genes (NAs for genes not measured by a given contrast).

`dprime` | Unbiased effect sizes (one column per contrast).
`vardprime` | Variances of unbiased effect sizes (one column per contrast).
`mu` | Overall mean effect sizes.
`var` | Variances of overall mean effect sizes.
`z` | Overall z score = `mu / sqrt(var)`.
`fdr` | False discovery rates calculated by `fdrtool`.

### Examples

    library(lydata)

    # location of data
    data_dir <- system.file("extdata", package = "lydata")

    # gather GSE names
    gse_names  <- c("GSE9601", "GSE15069", "GSE50841", "GSE34817", "GSE29689")

    # load previous analysis
    anals <- load_diff(gse_names, data_dir)

    # add tissue sources to perform seperate meta-analyses for each source (optional)
    # anals <- add_sources(anals, data_dir)

    # perform meta-analysis
    es <- es_meta(anals, by_source = TRUE)

[Package crossmeta version 1.6.0 Index]