Cell Ranger6.1, printed on 12/22/2024
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 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:
--libraries flag, 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 --fastqs argument.--feature-ref flag 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 and customizing the code in red, 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
for 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
downstream processing.
| library_type | Description |
|---|---|
Antibody Capture | 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. |
CRISPR Guide Capture | 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. |
Custom | 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.
| Column Name | Description |
|---|---|
fastqs | A fully qualified path to the directory containing the demultiplexed FASTQ files for this sample. Analogous to the --fastqs arg to cellranger count. This field does not accept comma-delimited paths. If you have multiple sets of fastqs for this library, add an additional row, and use the use same library_type value. |
sample | Same as the --sample arg to cellranger count. Sample name assigned in the bcl2fastq sample sheet. |
library_type | The FASTQ data will be interpreted using the rows from the feature reference file that have a ‘feature_type’ that matches this library_type. This field is case-sensitive, and must match a valid library type as described in the Library / Feature Types section. Must be Gene Expression for the gene expression libraries. (This includes Targeted Gene Expression.) Must be one of Custom, Antibody Capture, or CRISPR Guide Capture for Feature Barcode libraries.
|
| 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 GEX_sample1 and CRISPR_sample1
on the bcl2fastq / mkfastq sample sheet. This generated two FASTQ files named
GEX_sample1_S0_L001_001.fastq.gz and CRISPR_sample1_S0_L001_001.fastq.gz
in the path /opt/foo (be sure to use the correct full path to your FASTQ files).
We pass the FASTQ sample names and paths to Cell
Ranger with the appropriate library types:
| fastqs | sample | library_type |
|---|---|---|
| /opt/foo/ | GEX_sample1 | Gene Expression |
| /opt/foo/ | CRISPR_sample1 | CRISPR Guide Capture |
Gene expression + Antibody libraries. In this example we've demultiplexed
the sequencing data from two libraries named GEX_sample2 and Ab_sample2 on
the bcl2fastq / mkfastq sample sheet. This generated two FASTQ files named
GEX_sample2_S0_L001_001.fastq.gz and Ab_sample2_S0_L001_001.fastq.gz in
the path /opt/foo (be sure to use the correct full path to your FASTQ files).
We pass the FASTQ sample names to Cell Ranger with the
appropriate library types:
| fastqs | sample | library_type |
|---|---|---|
| /opt/foo/ | GEX_sample2 | Gene Expression |
| /opt/foo/ | Ab_sample2 | Antibody Capture |
If your assay scheme creates a library containing multiple Feature Barcode
library_types (e.g., CRISPR Guide Capture and Antibody Capture), please contact support@10xgenomics.com for assistance in configuring your Libraries CSV.
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
(in the 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 gene_id column).
This table describes the columns in the Feature Reference CSV file. Example files can be found below.
| Column Name | Description |
|---|---|
id |
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. |
name |
Human-readable name for this feature. Must not contain whitespace. This name will be displayed in Loupe Browser. |
read |
Specifies which RNA sequencing read contains the Feature Barcode sequence. Must be R1 or R2. Note: in most cases R2 is the correct read. |
pattern |
Specifies how to extract the Feature Barcode sequence from the read. See the Barcode Extraction Pattern section below for details. |
sequence |
Nucleotide barcode sequence associated with this feature. E.g., antibody barcode or sgRNA protospacer sequence. |
feature_type |
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 library_type that matches the feature_type will be scanned for occurrences of this feature. Each feature type in the feature reference must match a library_type entry in the Libraries CSV file. This field is case sensitive.
|
target_gene_id |
(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 target_gene_id and target_gene_name will enable the pipeline to perform differential expression analysis, assuming that control ("Non-Targeting") guides are also specified. Non-targeting guides must contain the value "Non-Targeting" in the target_gene_id and target_gene_name fields. See the CRISPR Overview section for more details.
|
target_gene_name |
(Optional) Gene name of target gene of a CRISPR guide RNA. The gene name corresponding to the gene referenced in the target_gene_id field must match the gene name given here. See the CRISPR Overview section for more details.
|
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 pattern column
can be made up of a combination of these elements:
sequence column 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 a non-N anchor, we recommend 12bp-20bp of constant sequence.
The extracted Feature Barcode sequence is aligned to the feature reference and up to one base mismatch is allowed. The extracted Feature Barcode sequences are corrected up to a Hamming distance of one base with the 10x Genomics barcode correction algorithm for correcting 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.
| read | pattern |
|---|---|
| R2 | 5PNNNNNNNNNN(BC) |
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.
| read | pattern |
|---|---|
| R2 | 5PNNNNNNNNNN(BC) |
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
the feature_type column for dextramer or multimer reagents. Therefore, the
same
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.
Although TotalSeq™-A can be used with the CITE-Seq assay, CITE-Seq is not a 10x Genomics supported assay. Please contact New York Genome Center or BioLegend for assistance with the assay or software.
| read | pattern |
|---|---|
| R2 | 5P(BC) |
In CRISPR Guide Capture assays, the Feature Barcode sequence is the CRISPR 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_id and 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 target_gene_id as Non-Targeting. Cells containing
Non-Targeting guides will be used as controls for CRISPR perturbation
analysis. The four remaining guides target two genes.
| Read | Pattern | Assay | Example |
|---|---|---|---|
| R2 | (BC)GACCAGGATGGGCACCACCC | 3’ Gene Expression with Feature Barcode | Download 3' CSV |
| R2 | GACCAGGATGGGCACCACCC(BC) | 5’ Gene Expression with Feature Barcode | Download 5' CSV |