Software

Here are some of the software products to come out of my research:

Monkeybread

Explore cellular niches in single-cell resolution spatial transcriptomics data.

• GitHub: https://github.com/immunitastx/monkeybread
• Documentation: https://monkeybread.readthedocs.io/en/latest/

SpatialCorr

Identify gene sets with spatially varying correlation structure.

• GitHub: https://github.com/mbernste/SpatialCorr
• Documentation: https://spatialcorr.readthedocs.io/en/latest/index.html
• Paper: https://doi.org/10.1016/j.crmeth.2022.100369

CellO

Cell type classification of single-cell RNA-seq data against the Cell Ontology.

• GitHub: https://github.com/deweylab/CellO
• Paper: https://doi.org/10.1016/j.isci.2020.101913

CellO Viewer

Explore cell type-specific gene expression signatures across the Cell Ontology.

• Website: https://uwgraphics.github.io/CellOViewer/
• GitHub: https://github.com/uwgraphics/CellOViewer
• Paper: https://doi.org/10.1016/j.isci.2020.101913

MetaSRA Website

Search for public RNA-seq data within the Sequence Read Archive using biomedical ontologies.

• Website: http://metasra.biostat.wisc.edu
• GitHub:
  • https://github.com/deweylab/MetaSRA-website-frontend
  • https://github.com/deweylab/MetaSRA-API-backend
• Paper: https://doi.org/10.1093/bioinformatics/btx334

MetaSRA Pipeline

Standardize biomedical metadata that are encoded as key-value pairs.

• GitHub: https://github.com/deweylab/MetaSRA-pipeline
• Paper: https://doi.org/10.1093/bioinformatics/btx334

CHARTS

Characterize and compare tumor subpopulations across public single-cell RNA-seq data.

• Website: https://charts.morgridge.org
• GitHub: https://github.com/stewart-lab/CHARTS
• Paper: https://doi.org/10.1186/s12859-021-04021-x

Series Finder & Case-Control Finder

Build structured datasets of RNA-seq data from the Sequence Read Archive.

• GitHub: https://github.com/mbernste/hypothesis-driven-SRA-queries
• Paper: https://doi.org/10.12688/f1000research.23180.2