PT  - JOURNAL ARTICLE
AU  - Jeffrey T. Leek
TI  - svaseq: removing batch effects and other unwanted noise from sequencing data
AID  - 10.1101/006585
DP  - 2014 Jan 01
TA  - bioRxiv
PG  - 006585
4099  - http://biorxiv.org/content/early/2014/06/26/006585.short
4100  - http://biorxiv.org/content/early/2014/06/26/006585.full
AB  - It is now well known that unwanted noise and unmodeled artifacts such as batch effects can dramatically reduce the accuracy of statistical inference in genomic experiments. We introduced surrogate variable analysis for estimating these artifacts by (1) identifying the part of the genomic data only affected by artifacts and (2) estimating the artifacts with principal components or singular vectors of the subset of the data matrix. The resulting estimates of artifacts can be used in subsequent analyses as adjustment factors. Here I describe an update to the sva approach that can be applied to analyze count data or FPKMs from sequencing experiments. I also describe the addition of supervised sva (ssva) for using control probes to identify the part of the genomic data only affected by artifacts. These updates are available through the surrogate variable analysis (sva) Bioconductor package.