MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that para...

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Detalles Bibliográficos
Autores principales: Finak, Greg, McDavid, Andrew, Yajima, Masanao, Deng, Jingyuan, Gersuk, Vivian, Shalek, Alex K., Slichter, Chloe K., Miller, Hannah W., McElrath, M. Juliana, Prlic, Martin, Linsley, Peter S., Gottardo, Raphael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4676162/
https://www.ncbi.nlm.nih.gov/pubmed/26653891
http://dx.doi.org/10.1186/s13059-015-0844-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4676162/
https://www.ncbi.nlm.nih.gov/pubmed/26653891
http://dx.doi.org/10.1186/s13059-015-0844-5

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