Author

Patrick Danaher

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:cell type annotation· 1 Research Focus:gene regulatory network inference· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Joseph Beechem· 2 shared Lucas Dennis· 2 shared Alice Wang· 1 shared Andrew McDavidiD· 1 shared Andrew White· 1 shared Erin Hanson· 1 shared Greg FinakiD· 1 shared Jack BallantyneiD· 1 shared

Activity by Year

2014-2016 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersBioinformatics

Joseph Beechem

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Author profile →

3 papers

Recent·Most cited

Gene expression markers of Tumor Infiltrating Leukocytes

Patrick Danaher, Sarah Warren, Lucas Dennis, Leonard D'Amico, Andrew White, Mary L Disis, Melissa A Geller, Kunle Odunsi, Joseph Beechem, Stephen P Fling

The paper identifies 60 gene markers for 14 immune cell types in tumors, validated through co-expression analysis in TCGA data and compared to flow cytometry and IHC.

bioRxiv · Aug 11, 2016Bioinformatics

Facile semi-automated forensic body fluid identification by multiplex solution hybridization of NanoString® barcode probes to specific mRNA targets

Patrick Danaher, Robin Lynn White, Erin Hanson, Jack Ballantyne

This paper develops a method for forensic body fluid identification using mRNA profiling and NanoString probes, achieving zero false positives in testing.

Forensic Science International: Genetics · Jan 1, 2015Peer-reviewedGenomics

Modeling bi-modality improves characterization of cell cycle on gene expression in single cells

Lucas Dennis, Andrew McDavid, Patrick Danaher, Greg Finak, Michael Krouse, Alice Wang, Philippa Webster, Joseph Beechem, Raphael Gottardo

The paper models bi-modality in single-cell gene expression to improve characterization of cell cycle effects and finds the cell cycle explains 5%-17% of expression variability.

PLoS Computational Biology · Jul 17, 2014Peer-reviewedBioinformatics