Description Usage Arguments Details Value Author(s) Examples
This function automatically estimates weights
for the "weight"
and "dperm_weight"
options of metaseqR2 for combining pvalues from
multiple statistical tests. It creates simulated
dataset based on real data and then performs
statistical analysis with metaseqR2 several times
in order to derive False Discovery Curves. Then,
the average areas under the false discovery curves
are used to construct weights for each algorithm,
according to its performance when using simulated
data.
1 2 3 4 5 6 
counts 
the real raw counts table from which the simulation parameters will be estimated. It must not be normalized and must contain only integer counts, without any other annotation elements and unique gene identifiers as the rownames attribute. 
normalization 
same as 
statistics 
same as 
nsim 
the number of simulations to perform to estimate the weights. It default to 10. 
N 
the number of genes to produce.
See 
samples 
a vector with 2 integers, which are the number of samples for each condition (two conditions currently supported). 
ndeg 
a vector with 2 integers, which are the number of differentially expressed genes to be produced. The first element is the number of upregulated genes while the second is the number of downregulated genes. 
fcBasis 
the minimum foldchange for deregulation. 
top 
the top 
modelOrg 
the organism from which the
data are derived. It must be one of

drawFpc 
draw the averaged false
discovery curves? Default to 
rc 
the fraction of the available cores to use in a multicore system. 
... 
Further arguments to be passed to

The weight estimation process involves a lot of
random sampling. For guaranteed reproducibility,
be sure to use set.seed
prior to any
calculations. By default, when the metaseqR2 package
is loaded, the seed is set to 42
.
A vector of weights to be used in
metaseqr2
with the
weights
option.
Panagiotis Moulos
1 2 3 4 5 6 7 8 9  require(zoo)
data("mm9GeneData",package="metaseqR2")
weights < estimateAufcWeights(
counts=as.matrix(mm9GeneCounts[sample(nrow(mm9GeneCounts),1000),9:12]),
normalization="edaseq",
statistics=c("edger","limma"),
nsim=1,N=100,ndeg=c(10,10),top=10,modelOrg=NULL,
rc=0.1,libsizeGt=1e+5
)

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