# sva v3.20.0

## Surrogate Variable Analysis

The sva package contains functions for removing batch
effects and other unwanted variation in high-throughput
experiment. Specifically, the sva package contains functions
for the identifying and building surrogate variables for
high-dimensional data sets. Surrogate variables are covariates
constructed directly from high-dimensional data (like gene
expression/RNA sequencing/methylation/brain imaging data) that
can be used in subsequent analyses to adjust for unknown,
unmodeled, or latent sources of noise. The sva package can be
used to remove artifacts in three ways: (1) identifying and
estimating surrogate variables for unknown sources of variation
in high-throughput experiments (Leek and Storey 2007 PLoS
Genetics,2008 PNAS), (2) directly removing known batch
effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing
batch effects with known control probes (Leek 2014 biorXiv).
Removing batch effects and using surrogate variables in
differential expression analysis have been shown to reduce
dependence, stabilize error rate estimates, and improve
reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008
PNAS or Leek et al. 2011 Nat. Reviews Genetics).

## Functions in sva

Name | Description | |

fsva | A function for performing frozen surrogate variable analysis as proposed in Parker, Corrada Bravo and Leek 2013 | |

twostepsva.build | A function for estimating surrogate variables with the two step approach of Leek and Storey 2007 | |

empirical.controls | A function for estimating the probability that each gene is an empirical control | |

f.pvalue | A function for quickly calculating f statistic p-values for use in sva | |

svaseq | A function for estimating surrogate variables for count based RNA-seq data. | |

psva | A function for estimating surrogate variables with the two step approach of Leek and Storey 2007 | |

sva | sva: a package for removing artifacts from microarray and sequencing data | |

ssva | A function for estimating surrogate variables using a supervised approach | |

ComBat | Adjust for batch effects using an empirical Bayes framework | |

fstats | A function for quickly calculating f statistics for use in sva | |

num.sv | A function for calculating the number of surrogate variables to estimate in a model | |

sva.check | A function for post-hoc checking of an sva object to check for degenerate cases. | |

irwsva.build | A function for estimating surrogate variables by estimating empirical control probes | |

No Results! |

## Last year downloads

## Details

License | Artistic-2.0 |

biocViews | Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization |

depends | base (>= 2.8) , genefilter , mgcv , R (>= 2.8) |

suggests | BiocStyle , bladderbatch , limma , pamr , testthat , zebrafishRNASeq |

Contributors | John D Storey, Elana J Fertig, Jeffrey T Leek, W Evan Johnson, Hilary S Parker, Andrew E Jaffe |

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