Description Usage Arguments Value control Return Value See Also Examples
Modules defined in sets
are tested for average differences in expression from the "average" gene.
By using bootstraps, the betweengene covariance of terms in the hurdle model is found, and is used to adjust for coexpression between genes.
We drop genes if the coefficient we are testing was not estimible in original model fit in zFit
or in any of the bootstrap replicates (evidenced an NA
in the bootstrap array). This might yield overly conservative inference.
Since bootstrapping is a randomized procedure, the degrees of freedom of a module (and its variance parameters) might differ from runtorun.
You might try setting var_estimate='modelbased'
to relax this requirement by assuming independence between genes and then using the asymptotic covariance estimates, which are deterministic, but may result in overlygenerous inference.
1 2  gseaAfterBoot(zFit, boots, sets, hypothesis, control = list(n_randomize = Inf,
var_estimate = "bootall"))

zFit 
object of class ZlmFit 
boots 
bootstraps of zFit 
sets 
list of indices of genes 
hypothesis 
a 
control 
list of control parameters. See details. 
Object of class GSEATests
, containing slots tests
, 4D array and bootR
, the number of boostrap replicates.
control
control
is a list with elements:
n_randomize
, giving the number of genes to sample to approximate the nonmodule average expression. Set to Inf
to turn off the approximation (the default).
var_estimate
, giving the method used to estimate the variance of the modules. bootall
uses the bootstrapped covariance matrices. bootdiag
uses only the diagonal of the bootstrapped covariance matrix (so assuming independence across genes). modelbased
assumes independence across genes and uses the variance estimated from the model.
A 4D array is returned, with dimensions "set" (each module), "comp" ('disc'rete or 'cont'inuous), "metric" ('stat' gives the average of the coefficient, 'var' gives the variance of that average, 'dof' gives the number of genes that were actually tested in the set), "group" ('test' for the genes in testset, "null" for all genes outside the testset).
calcZ
summary,GSEATestsmethod
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