Congratulations! You’ve successfully completed your Cell Ranger pipeline run. You might be wondering what to do next. How should you evaluate the quality of the data? How should you compare experimental and control samples? How should you make beautiful plots for publication? Here, we will provide some resources that could help you navigate through your results and move your project forward. The general pattern is to check the data quality, prepare for further analysis by combining multiple data sets if needed, and then explore your data using Loupe Browser or community-developed tools.

The web_summary.html file output from Cell Ranger is a great place to start assessing the quality of your data. Several metrics in the web summary file can be used to assess the overall success of an experiment, including sequencing, mapping, and cell metrics. This Technical Note presents an overview of web summary file interpretation, including expected values for the metrics and characteristic plots.

Many experiments contain more than one sample. You may have treated vs. untreated samples, or diseased vs. healthy samples that you would like to compare. The process of combining multiple Cell Ranger runs for further analysis is called data integration. To integrate data you can use the cellranger aggr pipeline described here, or a variety of community-developed tools discussed later in this article.

The Cell Ranger pipelines generate cloupe files. This file can be opened in Loupe Browser, a 10x-developed desktop application for visualization and analysis of 10x data. You can run differential expression analysis, filter and recluster data, identify cell types, and much more. Loupe Browser tutorials can be found here.

Hundreds of tools have been developed for analyzing 10x Genomics data by the community. The use of most of these tools requires some programming skills (R and Python). These tools are often used to generate figures for publications and for analyses that are not enabled by Cell Ranger or Loupe tools. Here is a short list of popular 10x-compatible third-party tools that cover common use cases:

• Seurat is an R package with several methods to analyze single cell and other data types. Use cases include quality assessment, clustering, and data integration. A set of Seurat tutorials can be found on this page.
• Bioconductor is a collection of R packages that includes tools for analyzing and visualizing single cell gene expression data. There is an online book here that uses a variety of bioconductor packages for common single cell analysis workflows.
• Monocle is an R package for clustering, trajectory analysis, and differential expression. Tutorials can be found here.
• Scanpy is a Python tool for clustering, trajectory inference, and differential expression. Tutorials are here.

Visit the 10x Genomics publication page to find articles related to your area of interest.

If you would like to start with review articles, here are two popular examples:

• Current best practices in single‐cell RNA‐seq analysis: a tutorial. Molecular systems biology is a comprehensive review article that outlines the typical scRNA‐seq analysis workflow. This article could be a great place to start if you are interested in understanding basic analysis workflows.
• Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data provides a hands-on guide for experimentalists interested in analyzing their data as well as an overview for bioinformaticians seeking to develop new computational methods.

Several institutes and organizations have created courses and workshops for single cell data analysis. Users of all levels may find some of these courses useful:

• Orchestrating Single-Cell Analysis with Bioconductor, an e-book that teaches users some common workflows for the analysis of scRNA-seq.
• Broad Institute single cell workshop.
• Single cell RNA sequencing, an e-book generated by the Bioinformatics team at NYU for mastering NGS analysis, including scRNA-seq analysis.

The above is not a comprehensive list. The landscape of single cell data analysis is evolving to incorporate new features. New and exciting tools, articles, courses, and other resources continue to be released. We selected these tools based on a combination of factors including citations, quality of documentation, functionality/ease of use, and active support. We hope this article can help you start your exciting journey of single cell data analysis!