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Poisson scRNA Integration of Mixed Unknown Signals

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PRIMUS

Poisson scRNA Integration of Mixed Unknown Signals

Overview

PRIMUS is a holistic clustering approach that identifies phenotypic cell groups from the scRNA-seq data while accounting for date source (e.g. patient, sample, dataset) -specific components as well as technical noises.

PRIMUS uses a bilinear Poisson regression model to simultaneously factorize the expression data into the defined nuisance factors, undefined cellular phenotypes, and their corresponding transcriptomic profiles.

Installation

To run PRIMUS, open R and install PRIMUS from github:

devtools::install_github("KaiyangZ/PRIMUS")

Usage

As input PRIMUS takes raw counts from scRNA-seq experiments, a design matrix encoding the different nuisance factors, such as patient labels and technical factors, and a vector of size factors. Check out this vignette for a quick start tutorial.

Citation

Zhang K, Erkan EP, Jamalzadeh S, Dai J, Andersson N, Kaipio K, Lamminen T, Mansuri N, Huhtinen K, Carpén O, Hietanen S, Oikkonen J, Hynninen J, Virtanen A, Häkkinen A, Hautaniemi S, Vähärautio A. Longitudinal single-cell RNA-seq analysis reveals stress-promoted chemoresistance in metastatic ovarian cancer. Sci Adv. 2022 Feb 25;8(8):eabm1831. doi: 10.1126/sciadv.abm1831. Epub 2022 Feb 23. PMID: 35196078.

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Poisson scRNA Integration of Mixed Unknown Signals

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