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

MetaSRA: normalized human sample-specific metadata for the Sequence Read Archive. Bernstein M, Doan A, Dewey C. Bioinformatics, 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, 2017

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A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Korthauer K, Chu LF, Newton M, Li Y, Thomson J, Stewart R, Kendziorski K. Genome Biology 17:222, 2016

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Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm. Chu LF, Leng N, Zhang J, Hou Z, Mamott D, Vereide D, Choi J, Kendziorski C, Stewart R, Thomson J. Genome Biology 17:173

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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, 2016

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Colin Dewey


Christina Kendziorski

Nate Fillmore

Rhonda Bacher


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