michaelisaiahlove at gmail dot com *
* for software questions, do not email, instead use the Bioconductor support site
- Scott Van Buren’s paper is preprinted:
“Compression of quantification uncertainty for scRNA-seq counts”
- Sarah Reifeis’s first paper is published in Genetic Epidemiology: “Assessing exposure effects on gene expression” doi: 10.1002/gepi.22324
- Arjun Bhattacharya’s second and third papers are preprinted:
“An approach for normalization and quality control for NanoString RNA expression data”
doi: 10.1101/2020.04.08.032490, and
“Multi-omic strategies for transcriptome-wide prediction and association studies”
- Tximeta is published in PLOS Computational Biology doi: 10.1371/journal.pcbi.1007664
- Arjun Bhattacharya’s breast cancer TWAS is published in Genome Biology doi: 10.1186/s13059-020-1942-6
- New paper on F1000Research: From first author Stuart Lee (Monash U.), “Fluent genomics with plyranges and tximeta” doi: 10.12688/f1000research.22259.1
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
- November 2019 - 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
- November 2019 - 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.20916.1
- August 2019 - Anqi Zhu’s second paper published in Nucleic Acids Research: doi: 10.1093/nar/gkz622, which describes the Swish method in the fishpond package.
- July 2019 - Sean McCabe’s first paper published in Briefings in Bioinformatics: doi: 10.1093/bib/bbz070, 2019 Author Manuscript