ComBat
(Package: sva) :
Adjust for batch effects using an empirical Bayes framework
ComBat allows users to adjust for batch effects in datasets where the batch covariate is known, using methodology described in Johnson et al. 2007. It uses either parametric or non-parametric empirical Bayes frameworks for adjusting data for batch effects. Users are returned an expression matrix that has been corrected for batch effects. The input data are assumed to be cleaned and normalized before batch effect removal.
● Data Source:
BioConductor
● Keywords:
● Alias: ComBat
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empirical.controls
(Package: sva) :
A function for estimating the probability that each gene is an empirical control
This function uses the iteratively reweighted surrogate variable analysis approach to estimate the probability that each gene is an empirical control.
● Data Source:
BioConductor
● Keywords:
● Alias: empirical.controls
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f.pvalue
(Package: sva) :
A function for quickly calculating f statistic p-values for use in sva
This function does simple linear algebra to calculate f-statistics for each row of a data matrix comparing the nested models defined by the design matrices for the alternative (mod) and and null (mod0) cases. The columns of mod0 must be a subset of the columns of mod.
● Data Source:
BioConductor
● Keywords:
● Alias: f.pvalue
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fstats
(Package: sva) :
A function for quickly calculating f statistics for use in sva
This function does simple linear algebra to calculate f-statistics for each row of a data matrix comparing the nested models defined by the design matrices for the alternative (mod) and and null (mod0) cases. The columns of mod0 must be a subset of the columns of mod.
● Data Source:
BioConductor
● Keywords:
● Alias: fstats
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fsva
(Package: sva) :
A function for performing frozen surrogate variable analysis as proposed in Parker, Corrada Bravo and Leek 2013
This function performs frozen surrogate variable analysis as described in Parker, Corrada Bravo and Leek 2013. The approach uses a training database to create surrogate variables which are then used to remove batch effects both from the training database and a new data set for prediction purposes. For inferential analysis see sva, svaseq, with low level functionality available through irwsva.build and ssva.
● Data Source:
BioConductor
● Keywords:
● Alias: fsva
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irwsva.build
(Package: sva) :
A function for estimating surrogate variables by estimating empirical control probes
This function is the implementation of the iteratively re-weighted least squares approach for estimating surrogate variables. As a buy product, this function produces estimates of the probability of being an empirical control. See the function empirical.controls for a direct estimate of the empirical controls.
● Data Source:
BioConductor
● Keywords:
● Alias: irwsva.build
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num.sv
(Package: sva) :
A function for calculating the number of surrogate variables to estimate in a model
This function estimates the number of surrogate variables that should be included in a differential expression model. The default approach is based on a permutation procedure originally prooposed by Buja and Eyuboglu 1992. The function also provides an interface to the asymptotic approach proposed by Leek 2011 Biometrics.
● Data Source:
BioConductor
● Keywords:
● Alias: num.sv
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psva
(Package: sva) :
A function for estimating surrogate variables with the two step approach of Leek and Storey 2007
This function is the implementation of the two step approach for estimating surrogate variables proposed by Leek and Storey 2007 PLoS Genetics. This function is primarily included for backwards compatibility. Newer versions of the sva algorithm are available through sva, svaseq, with low level functionality available through irwsva.build and ssva.
● Data Source:
BioConductor
● Keywords:
● Alias: psva
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ssva
(Package: sva) :
A function for estimating surrogate variables using a supervised approach
This function implements a supervised surrogate variable analysis approach where genes/probes known to be affected by artifacts but not by the biological variables of interest are assumed to be known in advance. This supervised sva approach can be called through the sva and svaseq functions by specifying controls.
● Data Source:
BioConductor
● Keywords:
● Alias: ssva
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sva
(Package: sva) :
sva: a package for removing artifacts from microarray and sequencing data
sva has functionality to estimate and remove artifacts from high dimensional data the sva function can be used to estimate artifacts from microarray data the svaseq function can be used to estimate artifacts from count-based RNA-sequencing (and other sequencing) data. The ComBat function can be used to remove known batch effecs from microarray data. The fsva function can be used to remove batch effects for prediction problems.
● Data Source:
BioConductor
● Keywords:
● Alias: sva, sva-package
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