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 https://genepattern-notebook.org. GenePattern Notebook repository and workspace: https://notebook.genepattern.org/. GenePattern Notebook source code is available from: https://github.com/genepattern/seurat_python_notebook. GenePattern Notebook and all its dependencies are available as a Docker image: https://hub.docker.com/r/genepattern/genepattern-notebook