es_meta | R Documentation |
Performs effect-size meta-analyses across all studies and seperately for each tissue source.
es_meta(diff_exprs, cutoff = 0.3, by_source = FALSE)
diff_exprs |
Previous result of |
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 |
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
.
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 = |
fdr |
False discovery rates calculated from column |
pval |
p-values calculated from column |
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)
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