Description Usage Arguments Value Author(s) Examples
View source: R/metaseqr.meta.R
This function performs permutation tests in order to
derive a meta pvalue by combining several of the
statistical algorithms of metaseqr. This is probably the
most accurate way of combining multiple statistical
algorithms for RNASeq data, as this issue is different
from the classic interpretation of the term
"metaanalysis" which implies the application of the same
statistical test on different datasets treating the same
subject/experiment. For other methods, see also the main
metaseqr
help page. You should keep in mind
that the permutation procedure can take a long time, even
when executed in parallel.
1 2 3 4 5 
contrast 
the contrasts to be tested by each
statistical algorithm. See the main

counts 
a normalized read counts table, one row for each gene, one column for each sample. 
sample.list 
the list containing condition names
and the samples under each condition. See the main

statistics 
the statistical algorithms used in
metaseqr. See the main 
stat.args 
the parameters for each statistical
algorithm. See the main 
libsize.list 
a list with library sizes. See the
main 
nperm 
the number of permutations (Monte Carlo simulations) to perform. 
weight 
a numeric vector of weights for each statistical algorithm. 
select 
how to select the initial vector of
pvalues. It can be 
replace 
same as the 
reprod 
create reproducible permutations. Ideally
one would want to create the same set of indices for a
given dataset so as to create reproducible pvalues. If

multic 
use multiple cores to execute the
premutations. This is an external parameter and implies
the existence of multicore package in the execution
environment. See the main 
A vector of meta pvalues
Panagiotis Moulos
1  # Not yet available

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