Love Lab

PI: Michael I. Love
Assistant Professor
Department of Biostatistics
Department of Genetics
Member of Lineberger Comprehensive Cancer Center
University of North Carolina-Chapel Hill

@mikelove — Google Scholar — Biostatistics Dept profile

michaelisaiahlove at gmail dot com *

* for software questions, do not email, instead use the Bioconductor support site

News and upcoming talks

  • Sean McCabe’s second paper is preprinted: “ACTOR: a latent Dirichlet model to compare expressed isoform proportions to a reference panel” doi: 10.1101/856401
  • Joshua Zitovsky’s first paper is up on F1000Research: “Fast effect size shrinkage software for beta-binomial models of allelic imbalance” doi: 10.12688/f1000research.20916
  • Sarah Reifeis’s first paper is preprinted: “Assessing exposure effects on gene expression” doi: 10.1101/806554
  • Arjun Bhattacharya’s first paper is preprinted: “A framework for transcriptome-wide association studies in breast cancer in diverse study populations” doi: 10.1101/769570

Research

The Love Lab uses statistical models to infer biologically meaningful patterns in high-dimensional datasets, and develops open-source statistical software for the Bioconductor Project. At UNC-Chapel Hill, we often 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 molecular and cellular phenotypes.

  • We collaborate with the Patro lab in the development of computational and statistical methods for quantification of RNA transcript abundance, including the software packages Salmon and tximeta.
  • We collaborate with the Stein lab in the study of genetic risk for neuropsychiatric disorders, leveraging data from molecular assays of chromatin accessibility and the transcriptome in neural progenitor cell lines to macroscale phenotypes such as gross human brain structure measured with MRI.
  • We collaborate with the Troester lab and the Carolina Breast Cancer Study, investigating the intersection between gene expression profiles of breast tumors and genetic risk for breast cancer, with a focus on racial disparities in patient outcomes.
  • We develop, support, and maintain the DESeq2 package for statistical analysis of RNA-seq and other sequencing experiments, and associated packages and workflows for analyzing genomics data. See the Software tab for more details.

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.

Past news items