Cell Ranger4.0, printed on 08/14/2022
Cell Ranger processes all Feature Barcode data through a counting pipeline that quantifies each feature in each cell. This analysis is done by the cellranger count pipeline. The pipeline outputs a unified feature-barcode matrix that contains gene expression counts alongside Feature Barcode counts for each cell barcode. The feature-barcode matrix replaces the gene-barcode matrix emitted by older versions of Cell Ranger.
The pipeline first extracts and corrects the cell barcode and UMI from the feature library using the same methods as gene expression read processing. It then then matches the Feature Barcode read against the list of features declared in the Feature Barcode Reference. The counts for each feature are available in the feature-barcode matrix output files and in the Loupe Browser output file.
To enable Feature Barcode analysis, cellranger count needs two new inputs:
--librariesflag, and declares the FASTQ files and library type for each input dataset. In a typical Feature Barcode analysis there will be two input libraries: one for the normal single-cell gene expression reads, and one for the Feature Barcode reads. This argument replaces the
--feature-refflag and declares the set of Feature Barcode reagents in use in the experiment. For each unique Feature Barcode used, this file declares a feature name and identifier, the unique Feature Barcode sequence associated with this reagent, and a pattern indicating how to extract the Feature Barcode sequence from the read sequence. See Feature Barcode Reference for details on how to construct the feature reference.
After creating the CSV files, run cellranger count:
$ cd /home/jdoe/runs $ cellranger count --id=sample345 \ --libraries=library.csv \ --transcriptome=/opt/refdata-gex-GRCh38-2020-A \ --feature-ref=feature_ref.csv \ --expect-cells=1000
The complete set of arguments to cellranger count are covered in Single-Sample Analysis.
When inputting Feature Barcode data to Cell Ranger via the Libraries CSV File,
you must declare the
library_type of each library. Specific values
library_type will enable additional downstream processing,
specifically for CRISPR Guide Capture and Antibody Capture. The following table
outlines the types of libraries that can be specified and what they mean for the
|For use with experiments measuring cell surface protein expression levels via an antibody and/or antigen-multimer staining assay. Enables a t-SNE projection of the cells using only the Antibody Capture / Cell Surface Protein feature counts. This projection is available in an output file and in Loupe Browser. See the Antibody Algorithms page for more details.|
|Enables an analysis of gene expression changes caused by the presence of CRISPR perturbations, in a Perturb-Seq style assay. See the CRISPR Overview page for more details. This mode also creates a t-SNE projection using only the CRISPR guide counts. This projection is available in an output file and in Loupe Browser.|
|Provides processing of the Feature Barcode reads and a basic summary of the sequencing quality and library quality, but performs no special processing of the Feature Barcode counts.|
The Libraries CSV File declares the input FASTQ data for the libraries that make
up a Feature Barcode experiment. This will include one library containing Single
Cell Gene Expression reads, and one or more libraries containing Feature Barcode
reads. To use cellranger count in Feature Barcode mode, you must
create a Libraries CSV File and pass it with the
--libraries flag. The
following table describes what the content should be in the Libraries CSV File.
|A fully qualified path to the directory containing the demultiplexed FASTQ files for this sample. Analogous to the |
|Same as the |
|The FASTQ data will be interpreted using the rows from the feature reference file that have a ‘feature_type’ that matches this |
|Note: Each unique sample id requires a separate line in the library CSV file|
Gene expression + CRISPR libraries. In this example we've demultiplexed
the sequencing data from two libraries named
on the bcl2fastq / mkfastq sample sheet. This generated FASTQ files named
into the path
/opt/foo. We pass the FASTQ sample names and paths to Cell
Ranger with the appropriate library types:
|/opt/foo/||CRISPR_sample1||CRISPR Guide Capture|
Gene expression + Antibody libraries. In this example we've demultiplexed
the sequencing data from two libraries named
the bcl2fastq / mkfastq sample sheet. This generated FASTQ files named
/opt/foo. We pass the FASTQ sample names to Cell Ranger with the
appropriate library types:
If your assay scheme creates a library containing multiple
library_types, for example if you're using CRISPR Guide Capture and
Antibody Capture features, you will need to select a single
library_type for the library when inputting it into the Libraries
CSV File. This will provide only one kind of specialized library analysis. To
get multiple specialized analyses, you will need to run Cell Ranger multiple
times, passing different
library_type values in the Libraries CSV
File. This is a limitation of Cell Ranger that will be ameliorated in a future
release. Regardless of the
library_type specified, the
feature-barcode matrix outputs will contain counts for all specified features.
A Feature Reference CSV File is required when processing Feature Barcode data.
It declares the molecule structure and unique Feature Barcode sequence of each
feature present in your experiment. Each line of the CSV declares one unique
Feature Barcode. The Feature Reference CSV File is passed to cellranger
count with the
--feature-ref flag. Please note that the CSV
may not contain characters outside of the ASCII range.
Targeted Gene Expression data is compatible with Feature Barcode analysis.
However, if Targeted Gene Expression data is analyzed in conjunction with
CRISPR-based Feature Barcode data, there are additional requirements imposed for
the Feature Reference CSV file. Specifically, any CRISPR guide RNA target genes
target_gene_id column of the Feature Reference CSV file)
must correspond to genes which are also included in the targeted gene expression
Target Panel CSV file (in the
This table describes the columns in the Feature Reference CSV File. Example files can be found below.
||Unique ID for this feature. Must not contain whitespace, quote or comma characters. Each ID must be unique and must not collide with a gene identifier from the transcriptome.|
||Human-readable name for this feature. Must not contain whitespace. This name will be displayed in Loupe Browser.|
||Specifies which RNA sequencing read contains the Feature Barcode sequence. Must be R1 or R2. Note: in most cases R2 is the correct read.|
||Specifies how to extract the Feature Barcode sequence from the read. See the Barcode Extraction Pattern section below for details.|
||Nucleotide barcode sequence associated with this feature. E.g., antibody barcode or sgRNA protospacer sequence.|
||Type of the feature. See the Library/Feature Types section for details on allowed values of this field. FASTQ data specified in the Library CSV File with a
||(Optional) Reference gene identifier of the target gene of a CRISPR guide RNA. A gene with this id must exist in the reference transcriptome. Providing
||(Optional) Gene name of target gene of a CRISPR guide RNA. The gene name corresponding to the gene referenced in the
pattern field of the feature reference defines how to locate
the Feature Barcode within a read. The Feature Barcode may appear at a known
offset with respect to the start or end of the read or may appear at a fixed
position relative to a known anchor sequence. The
can be made up of a combination of these elements:
sequencecolumn of the feature reference. Must appear exactly once in the pattern.
Any constant sequences made up of A, C, G and T in the pattern must match exactly in the read sequence. Any N in the pattern is allowed to match a single arbitrary base. A modest number of fixed bases should be used to minimize the chance of a sequencing error disrupting the match. The fixed sequence should also be long enough to uniquely identify the position of the Feature Barcode. For feature types that require an non-N anchor, we recommend 12bp-20bp of constant sequence. The extracted Feature Barcode sequences are corrected up to a Hamming distance of 1 using the 10x barcode correction algorithm that is used to correct cell barcodes.
TotalSeq™-B is a line of antibody-oligonucleotide conjugates supplied by BioLegend that are compatible with the Single Cell 3' v3 assay. The Feature Barcode sequence appears at a fixed position (base 10) in the R2 read.
Example TotalSeq™-B Feature Reference CSV Please note, this is a pre-release set of TotalSeq-B antibodies. The Feature Barcode sequences have since changed. Please refer to https://www.biolegend.com/totalseq for the latest conjugated Feature Barcode information.
TotalSeq™-C is a line of antibody-oligonucleotide conjugates supplied by BioLegend that are compatible with the Single Cell 5' assay. The Feature Barcode sequence appears at a fixed position (base 10) in the R2 read.
The feature reference for
Immudex's dMHC Dextramer® libraries with dCODE Dextramers
has the same feature barcode pattern as TotalSeq™-C. Use "Antibody Capture" in
feature_type column for dextramer or multimer reagents. Therefore, the
feature reference example for TotalSeq™-C
can also be used for MHC Dextramer® libraries.
TotalSeq™-A is a line of antibody-oligonucleotide conjugates supplied by BioLegend that are compatible with the Single Cell 3' v2 and Single Cell 3' v3 kits. The Feature Barcode sequence appears at the start of the R2 read.
In CRISPR Guide Capture assays, the Feature Barcode sequence is the
protospacer sequence. The protospacer is followed by a downstream constant
sequence in the guide RNA which is used as an anchor to identify the location of
the protospacer. We recommend using a 12bp-20bp constant sequence that can be
uniquely identified, but is short enough that it is unlikely to be disrupted by
a sequencing error. In the example Feature Reference CSV file we declare six
guide RNA features with six distinct barcode / protospacer sequences. We use the
target_gene_name columns to declare the target gene of
each guide RNA, for use in downstream CRISPR perturbation analysis. Two guides
are declared with
Non-Targeting. Cells containing
Non-Targeting guides will be used as controls for CRISPR perturbation
analysis. The four remaining guides target two genes.