Transcriptome-based Phenotyping Project

This project is developing a general approach for characterizing and classifying cells according to transcriptome profiles from RNAseq data. This approach will be applicable to classifying samples according to cell type, tissue of origin, developmental stage, disease state, and active cellular processes.

Related CPCP Publications

IPI59: An actionable biomarker to improve treatment response in serous ovarian carcinoma patients. Choi J, Ye S, Eng K, Korthauer K, Bradley W, Rader J, Kendziorski C. Statistics in Biosciences 9(1):1-12, 2017

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Analysis of embryonic development in the unsequenced axolotl: waves of transcriptional upheaval and stability. Jiang P, Nelson J, Leng N, Collins M, Swanson S, Dewey C, Thomson J, Stewart R. Developmental Biology 426(2):143-154, 2017

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Statistical methods for latent class quantitative trait loci mapping. Ye S, Bacher R, Keller MP, Attie AD, Kendziorski C. Genetics 206(3):1309-1317, 2017

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MetaSRA: normalized human sample-specific metadata for the Sequence Read Archive. Bernstein M, Doan A, Dewey C. Bioinformatics 33(18):2914–2923, 2017

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SCnorm: robust normalization of single-cell RNA-seq data. Bacher R, Chu LF, Leng N, Gasch A, Thomson J, Stewart R, Newton M, Kendziorski C. Nature Methods 14:584–586, 2017

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Lead

Colin Dewey

Investigators

Christina Kendziorski

Nate Fillmore

Rhonda Bacher

Resources

CPCP 2017 Retreat: MetaSRA - Normalized Sample-Specific Metadata for the Sequence Read Archive Symposium Video

scDD software

scPattern software

MetaSRA pipeline software

MetaSRA: normalized metadata for the Sequence Read Archive data

CPCP Retreat 2016: Entity Matching for EHR- and Transcriptome-based Phenotyping Symposium Video

OEFinder software

Oscope software

EBSeqHMM software