Workshop in Biostatistics
|DATE:||February 4, 2016|
|TIME:||1:30 - 3:00 pm|
|LOCATION:||Medical School Office Building, Rm x303|
|TITLE:||Statistical methods for RNA-seq data
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute
Quantification of gene expression from RNA sequencing data is a fundamental task in computational biology, critical for projects across biological and biomedical sciences. Statistical analysis of RNA-seq data, such as identification of differentially expressed genes across samples or estimation of isoform abundances, presents new challenges: non-normality of count data, dependence of the variance on the mean, as well as technical artifacts in measurements. In this talk, I will discuss statistical methods I have developed for RNA-seq data, including robust estimators for inference of differential expression and an approach to remove systematic errors in isoform abundance estimates arising from variations in sample preparation.
Love MI, Hubr W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15(12):550.
Michael I Love, John B Hogenesch, Rafael A Irizarry.
Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation. BioRxiv, Cold Spring Harbor Laboratory.