10x Genomics
Chromium Single Cell Gene Expression

Cell Ranger1.2, printed on 11/21/2024

What is Cell Ranger?

Cell Ranger is a set of analysis pipelines that processes Chromium single cell 3’ RNA-seq output to align reads, generate gene-cell matrices and perform clustering and gene expression analysis.

Cell Ranger 1.2 supports libraries generated by the Chromium Single Cell 3' v1 and v2 reagent kits, whereas Cell Ranger 1.1 and earlier do not support v2 libraries.

Pipelines

Cell Ranger includes four main pipelines:

• cellranger mkfastq wraps Illumina's bcl2fastq to correctly demultiplex Chromium-prepared sequencing samples and to convert barcode and read data to FASTQ files.

• cellranger count takes FASTQ files from cellranger mkfastq and performs alignment, filtering, and UMI counting. It uses the Chromium cellular barcodes to generate gene-barcode matrices and perform clustering and gene expression analysis. count can take input from multiple sequencing runs on the same library.

• cellranger aggr aggregates outputs from multiple runs of cellranger count, normalizing those runs to the same sequencing depth and then recomputing the gene-barcode matrices and analysis on the combined data. aggr can be used to combine data from multiple samples into an experiment-wide gene-barcode matrix and analysis.

• cellranger reanalyze takes gene-barcode matrices produced by cellranger count or cellranger aggr and reruns the dimensionality reduction, clustering, and gene expression algorithms using tunable parameter settings.

These pipelines combine Chromium-specific algorithms with the widely used RNA-seq aligner STAR. Output is delivered in standard BAM, MEX, CSV, HDF5 and HTML formats that are augmented with cellular information.

Terminology

Throughout the documentation, you will see references to samples, libraries and sequencing runs. We define these as follows:

• Sample: A cell suspension extracted from a single biological source (blood, tissue, etc).
• Library: A 10x-barcoded sequencing library prepared from a single sample, corresponding to a single chip channel of a 10x Chromium Controller run.
• Sequencing Run / Flowcell: A flowcell containing data from one sequencing instrument run. The sequencing data can be further addressed by lane and by one or more sample indices.

The relationship between these terms can be complex:

• A single sample may be prepared into multiple libraries; this is commonly done to achieve higher cell counts without overloading a single 10x Chromium Controller run.
• A single library may be sequenced across multiple flowcells, and then combined as if all the reads came from one sequencing run.
• A single flowcell may contain multiple libraries, separated using different lanes and / or sample indices.
• The definition of "library" makes no distinction between two channels of the same physical 10x chip, versus the same channel of two different chips.

Workflows

The Cell Ranger workflow always starts with running cellranger mkfastq on each flowcell directory, as described in Generating FASTQs. The subsequent steps vary depending on how many samples, libraries and flowcells you have. We will describe them in order of increasing complexity:

Single Sample, Library, and Flowcell

This is the most basic case. You have a single biological sample, which was prepared into a single library, and then sequenced on a single flowcell. Assuming the FASTQs have been generated with cellranger mkfastq, you just need to run cellranger count as described in Single-Library Analysis.

One Library, Multiple Flowcells

If you have a library which was sequenced across multiple flowcells, you can pool the reads from both sequencing runs. Follow the steps in Multi-Flowcell Samples to combine them in a single cellranger count run.

One Sample, Multiple Libraries

If you prepared multiple libraries from the same sample (technical replicates, for example), then each one should be run through a separate instance of cellranger count. Once those are completed, you can perform a combined analysis using cellranger aggr, as described in Multi-Library Aggregation.

Multiple Biological Samples

For a full experiment involving multiple biological samples, you must run cellranger count separately for each individual library deriving from each of those samples. For instance, if your experiment involves four samples, each having two libraries / replicates, then you will have to run cellranger count eight times. Then you can combine them all in a single call to cellranger aggr.