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
SPEAKER: Michael Love
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.

Suggested readings:

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.