Analysis Guides

Analysis of 10x Genomics data is full of possibilities. Here is a collection of introductions, tutorials, and blogs for data analysis beyond software developed by 10x Genomics. We hope these guides help you navigate your exciting analysis journey.

Map of the 10x analysis journey—experimental design, lab workflow, raw data processing, QC, visualization & interpretation

Introductions

Broad introductions to common data analysis topics.

Integrating Single Cell and Visium Spatial Gene Expression data

Integrating Single Cell and Visium Spatial Gene Expression data

Single cell and Visium gene expression data can be combined to elucidate spatiality in single cell data and improve resolution in Visium data.

Single cell and Visium gene expression data can be combined to elucidate spatiality in single cell data and improve resolution in Visium data.

Batch Effect Correction

Batch Effect Correction

Batch effects come from technical variation across samples. This can often be prevented with good experimental design. When it cannot, there are computational approaches that can help.

Batch effects come from technical variation across samples. This can often be prevented with good experimental design. When it cannot, there are computational approaches that can help.

Web Resources for Cell Type Annotation

Web Resources for Cell Type Annotation

The advantage of visualizing cell populations at single-cell resolutions has introduced us to the challenges of annotating cells. A growing number of annotation databases and tools are available to aid us in the process. In this article, we provide general guidance for a selected few to assist you with your research.

The advantage of visualizing cell populations at single-cell resolutions has introduced us to the challenges of annotating cells. A growing number of annotation databases and tools are available to aid us in the process. In this article, we provide general guidance for a selected few to assist you with your research.

Bioinformatics Tools for Sample Demultiplexing

Bioinformatics Tools for Sample Demultiplexing

Sample multiplexing can minimize batch effects, facilitate multiplet identification, lower experiment costs, and make large-scale sample operations practical. In this article, we introduce main methods along with the bioinformatics tools for sample demultiplexing.

Sample multiplexing can minimize batch effects, facilitate multiplet identification, lower experiment costs, and make large-scale sample operations practical. In this article, we introduce main methods along with the bioinformatics tools for sample demultiplexing.

Differential Gene Expression Analysis in scRNA-seq Data between Conditions with Biological Replicates

Differential Gene Expression Analysis in scRNA-seq Data between Conditions with Biological Replicates

This article introduces various bioinformatics methods for performing differential gene expression analysis between conditions using single cell data.

This article introduces various bioinformatics methods for performing differential gene expression analysis between conditions using single cell data.

Common Considerations for Quality Control Filters for Single Cell RNA-seq Data

Common Considerations for Quality Control Filters for Single Cell RNA-seq Data

Quality control (QC) is an important step in the analysis to help assess the sample quality and to remove noises. In this article, we discuss the most common metrics and methods for QC and filtering of cell barcodes in scRNA-seq data.

Quality control (QC) is an important step in the analysis to help assess the sample quality and to remove noises. In this article, we discuss the most common metrics and methods for QC and filtering of cell barcodes in scRNA-seq data.

Tutorials

Step-by-step example analysis covering a variety of topics.

Correcting Batch Effects in Visium Data

Correcting Batch Effects in Visium Data

Guided, step-by-step tutorial in R for correcting batch effects between two public 10x Genomics Visium datasets (mouse brain) using the Harmony algorithm, then importing corrected projections and clusters into the Loupe Browser.

Guided, step-by-step tutorial in R for correcting batch effects between two public 10x Genomics Visium datasets (mouse brain) using the Harmony algorithm, then importing corrected projections and clusters into the Loupe Browser.

Navigating 10x Genomics Barcoded BAM Files

Navigating 10x Genomics Barcoded BAM Files

The BAM file produced by the Ranger pipelines include standard SAM/BAM flags and several custom flags that can be used to mine information about alignment, annotation, counting, etc. This tutorial walks users through the process of identifying records in the BAM file that contribute to UMI counting.

The BAM file produced by the Ranger pipelines include standard SAM/BAM flags and several custom flags that can be used to mine information about alignment, annotation, counting, etc. This tutorial walks users through the process of identifying records in the BAM file that contribute to UMI counting.

Testing Microscope Image Compatibility with Space Ranger

Testing Microscope Image Compatibility with Space Ranger

Guides users through testing Loupe Browser and Space Ranger compatibility with images of various formats. Illustrates conversion to compatible TIFF in Fiji using multichannel CZI and multi-hue SVS images.

Guides users through testing Loupe Browser and Space Ranger compatibility with images of various formats. Illustrates conversion to compatible TIFF in Fiji using multichannel CZI and multi-hue SVS images.

Trajectory Analysis using 10x Genomics Single Cell Gene Expression Data

Trajectory Analysis using 10x Genomics Single Cell Gene Expression Data

This tutorial provides users with the instructions to import results obtained with Cell Ranger and Loupe Browser into community-developed tools for RNA velocity analysis. This analysis can be used to reconstruct the dynamic processes that cells undergo as part of their true biological nature.

This tutorial provides users with the instructions to import results obtained with Cell Ranger and Loupe Browser into community-developed tools for RNA velocity analysis. This analysis can be used to reconstruct the dynamic processes that cells undergo as part of their true biological nature.

Batch Effect Correction in Chromium Single Cell ATAC Data

Batch Effect Correction in Chromium Single Cell ATAC Data

A step-by-step tutorial for batch effect correction in two 10x Genomics Single Cell ATAC datasets using the Harmony algorithm in R. The corrected projection was then imported into the Loupe Browser.

A step-by-step tutorial for batch effect correction in two 10x Genomics Single Cell ATAC datasets using the Harmony algorithm in R. The corrected projection was then imported into the Loupe Browser.

Tag Assignment of 10x Genomics CellPlex Data Using Seurat’s HTODemux Function

Tag Assignment of 10x Genomics CellPlex Data Using Seurat’s HTODemux Function

A step-by-step tutorial for using Seurat’s HTODemux function to perform custom tag assignment of 10x Genomics CellPlex data. The output tag assignments can be loaded back into Cell Ranger to rerun the primary analysis.

A step-by-step tutorial for using Seurat’s HTODemux function to perform custom tag assignment of 10x Genomics CellPlex data. The output tag assignments can be loaded back into Cell Ranger to rerun the primary analysis.

Background Removal Guidance for Single Cell Gene Expression Datasets Using CellBender

Background Removal Guidance for Single Cell Gene Expression Datasets Using CellBender

A guided tutorial for removing background in Single Cell Gene Expression data using the community developed tool CellBender. The ‘cleaned’ dataset is further validated by cell type annotation and visualization.

A guided tutorial for removing background in Single Cell Gene Expression data using the community developed tool CellBender. The ‘cleaned’ dataset is further validated by cell type annotation and visualization.

Integrating 10x Visium and Chromium data with R

Integrating 10x Visium and Chromium data with R

A step-by-step guide for using spacexr to deconvolve Visium spatial transcriptomic spot cell types using a Chromium single cell reference.

A step-by-step guide for using spacexr to deconvolve Visium spatial transcriptomic spot cell types using a Chromium single cell reference.

Demultiplexing and Analyzing 5’ Immune Profiling Libraries Pooled with Hashtags

Demultiplexing and Analyzing 5’ Immune Profiling Libraries Pooled with Hashtags

TotalSeq™-C antibodies from BioLegend can be used for multiplexing samples (cell hashing) with the 10x Genomics 5' Immune Profiling workflow. This is a step-by-step tutorial on using Cell Ranger to demultiplex and analyze 5' Gene Expression, TCR, BCR, and cell surface protein (Antibody) libraries.

TotalSeq™-C antibodies from BioLegend can be used for multiplexing samples (cell hashing) with the 10x Genomics 5' Immune Profiling workflow. This is a step-by-step tutorial on using Cell Ranger to demultiplex and analyze 5' Gene Expression, TCR, BCR, and cell surface protein (Antibody) libraries.

Informatics Blogs

Tips, commentary, and highlights related to 10x Genomics data analysis.

Continuing Your Journey After Running Cell Ranger

Continuing Your Journey After Running Cell Ranger

Guidance on next steps after running Cell Ranger, including 10x Genomics tools and third-party tool analysis.

Guidance on next steps after running Cell Ranger, including 10x Genomics tools and third-party tool analysis.

Publication Highlight: Benchmarking scRNA-seq Batch Correction Methods

Publication Highlight: Benchmarking scRNA-seq Batch Correction Methods

Tran et al. performed comprehensive comparisons of 14 batch correction methods. Different methods performed best for 10x Genomics datasets with different characteristics.

Tran et al. performed comprehensive comparisons of 14 batch correction methods. Different methods performed best for 10x Genomics datasets with different characteristics.

10 Tips for Biologists Learning Bioinformatics

10 Tips for Biologists Learning Bioinformatics

Getting started in bioinformatics can feel overwhelming. In this article, you will find some tips and tricks that have helped us on this journey.

Getting started in bioinformatics can feel overwhelming. In this article, you will find some tips and tricks that have helped us on this journey.

Continuing Your Journey After Running Space Ranger

Continuing Your Journey After Running Space Ranger

In this article we provide an overview of common next steps after running Space Ranger. We cover 10x and community developed tools.

In this article we provide an overview of common next steps after running Space Ranger. We cover 10x and community developed tools.

Publication Highlight: Benchmarking Methods to Integrate Spatial and Single-Cell Transcriptomics Data

Publication Highlight: Benchmarking Methods to Integrate Spatial and Single-Cell Transcriptomics Data

Li et al. benchmarked 16 methods to integrate spatial and single cell gene expression data. They identified the top-performing methods to predict RNA spatial distribution and deconvolute spot cell type composition.

Li et al. benchmarked 16 methods to integrate spatial and single cell gene expression data. They identified the top-performing methods to predict RNA spatial distribution and deconvolute spot cell type composition.