michaelisaiahlove at gmail dot com *
* for software questions, do not email, instead use the Bioconductor support site
- Michael Love will be presenting at:
- September 24, 2018 - Brown University: Statistics Seminar Series
- October 22, 2018 - Duke University: Computational Biology & Bioinformatics Series
- March 24-27, 2019 - ENAR, speaker in the Teaching Data Science through Case Studies invited session organized by Stephanie Hicks and Leah Jager
- July 2018 - Sean McCabe sent off his first preprint: MOVIE: Multi-Omics VIsualization of Estimated contributions, which describes the movie software.
- June 2018 - A new differential transcript usage workflow from the Love Lab is published in F1000Research: Swimming downstream
- April 2018 - Anqi Zhu sent off the first preprint from the lab: Heavy-tailed prior distributions for sequence count data, which describes the apeglm software.
- May 2018 - Michael Love gave a talk at NC State’s Bioinformatics Research Center Seminar Series.
- February 2018 - Michael Love gave a talk at UVA’s Center for Public Health Genomics: Fragment-level bias modeling for RNA-seq Data Analysis (link to Google Slides).
- December 2017 - Anqi Zhu and Michael Love featured in a Nature article talking about the Bioconductor Project for genomic data analysis.
- December 2016 - Michael Love gave a talk at UMD’s Computational Biology, Bioinformatics & Genomics seminar series.
- October 2016 - Michael Love gave a talk at the Triangle Statistical Genetics Meeting, SAS Campus, Cary.
The Love Lab uses statistical models to infer biologically meaningful patterns from high-throughput sequencing data, and develops open-source statistical software for the Bioconductor Project. At UNC-Chapel Hill, we collaborate with groups in the Genetics Department and the Lineberger Comprehensive Cancer Center, studying how genetic variants relevant to diseases are associated with changes in gene expression levels and other molecular phenotypes. A major effort of the lab is supporting and maintaining the DESeq2 package for statistical analysis of RNA-seq and other sequencing experiments, and associated packages and workflows for analyzing genomics data.
PI - Background
From 2013-2016, I was a Postdoctoral Fellow in the group of Rafael Irizarry in the Biostatistics and Computational Biology Department at the Dana-Farber Cancer Institute and the Harvard TH Chan School of Public Health. I completed a PhD in Computational Biology and Scientific Computing (2013) in the Vingron Department at the Max Planck Institute for Molecular Genetics in Berlin and the Mathematics and Informatics Department of the Freie Universität, Berlin. I completed a Statistics M.S. (2010) and Mathematics B.S. (2005) at Stanford University.