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Version 2. F1000Res. 2018 Aug 16 [revised 2019 May 29];7:1306. doi: 10.12688/f1000research.15830.2. eCollection 2018.

An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data.

Author information

1
Department of Medicine, University of California, San Diego, La Jolla, CA, 92093, USA.
2
Moores Cancer Center, University of California, San Diego, La Jolla, CA, 92093, USA.
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Contributed equally

Abstract

Single-cell RNA sequencing (scRNA-seq) has emerged as a popular method to profile gene expression at the resolution of individual cells. While there have been methods and software specifically developed to analyze scRNA-seq data, they are most accessible to users who program. We have created a scRNA-seq clustering analysis GenePattern Notebook that provides an interactive, easy-to-use interface for data analysis and exploration of scRNA-Seq data, without the need to write or view any code. The notebook provides a standard scRNA-seq analysis workflow for pre-processing data, identification of sub-populations of cells by clustering, and exploration of biomarkers to characterize heterogeneous cell populations and delineate cell types.

KEYWORDS:

GenePattern Notebook; Jupyter Notebook; clustering; interactive; open-source; pre-processing; scRNA-seq; single-cell expression; visualization

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