GenePattern Notebook for analysis and exploration of single-cell transcriptomic data

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. Researchers from UCSD School of Medicine 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.

t-SNE plot visualizing cluster assignments of cells


The clustering parameters can be changed using the sliders and re-plotted with the “Plot” button. Cells are projected into t-SNE space, with the first two t-SNE components as the axes of the plot. Cluster assignments of cells are defined by Louvain clustering and denoted as distinct colors.

Availability – GenePattern Notebook Web site GenePattern Notebook repository and workspace: GenePattern Notebook source code is available from: GenePattern Notebook and all its dependencies are available as a Docker image:

Mah CK, Wenzel AT, Juarez EF, Tabor T, Reich MM, Mesirov JP. (2019) An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data. Version 2. F1000Res 7:1306. [article]

Leave a Reply

Your email address will not be published. Required fields are marked *


Time limit is exhausted. Please reload CAPTCHA.