Processing 10x Genomics single cell data
Background
10xGenomics provides a range of platforms for various types of single cell samples including:
Single cell and single nuclei RNA-seq
Single cell ATAC-seq
Single cell multiome gene expression and ATAC data
Single cell immune profiling data
Cellplex (cell multiplexing)
Flex (fixed RNA profiling)
The auto_process pipeline has integrated support for Fastq generation
and QC (in the make_fastqs and run_qc commands) for these data
as outlined in the following sections.
Requirements
The appropriate 10x Genomics software pipeline must already be installed
and available to auto_process.py:
10x Genomics data |
Software required |
|---|---|
Single cell/single nuclei RNA-seq |
CellRanger |
Single cell ATAC-seq |
CellRanger-ATAC |
Single cell multiome |
CellRanger-ARC |
Single cell immune profiling |
Cellranger |
CellPlex (cell multiplexing) |
CellRanger |
Flex (fixed RNA profiling) |
CellRanger |
In addition:
Fastq generation for all data requires
bcl2fastq;QC for single cell multiome GEX also uses CellRanger;
QC for single cell multiome ATAC also uses Cellranger-ATAC.
Fastq generation
General
If a sample sheet with the appropriate 10x Genomics indexes is provided
then all 10x Genomics single cell data should be processed using the
make_fastqs command, with the appropriate
10x_* specified via the --protocol option:
10x Genomics data |
Protocol |
|---|---|
Single cell/single nuclei RNA-seq |
|
Single cell ATAC-seq |
|
Single cell multiome (unpooled GEX or ATAC) |
|
Single cell multiome GEX (pooled GEX and ATAC) |
|
Single cell multiome ATAC (pooled GEX and ATAC) |
|
Single cell immune profiling |
|
CellPlex (cell multiplexing) |
|
Flex (fixed RNA profiling) |
|
Note
By default adapter trimming is automatically disabled for all
10x_* protocols, by removing any adapter sequences specified
in the sample sheet.
Note
If the sample sheet contains Illumina index sequences then the
standard protocol should be used instead (note that in this case
the defaults used for masking and trimming compared to the defaults
may differ from those used by the 10x Genomics pipeline).
Choosing Fastq generation protocol for single cell multiome data
There are three Fastq generation protocols for single cell multiome data; which should be used will depend on the specific configuration of the sequencing run:
Run only has either the GEX or the ATAC component of the single cell multiome experiment: the
10x_multiomeprotocol is preferred as Cellranger-ARC should be able to automatically determine which component the data are.Run has both GEX and ATAC components of the single cell multiome experiment in different lanes: in this situation Cellranger-ARC cannot automatically determine which component is in which lane, so the
10x_multiome_gexprotocol should be explicitly specified for the lanes with the GEX data, and the10x_multiome_atacspecified for those with the ATAC data, via the--lanesoption.For example:
auto_process.py make_fastqs \ --lanes=1:10x_multiome_atac \ --lanes=2:10x_multiome_gexSee Details for handling pooled single cell multiome ATAC and GEX data for more information on how the multiome protocols are implemented and used.
Analysis project setup and QC
Once Fastqs have been successfully generated, the SC_platform
and Library metadata items should be set to the appropriate values
for the 10x Genomics single cell project(s) in the projects.info
control file.
The following values are valid options for 10x Genomics single cell data:
Single cell platform |
Library types |
|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
Running the setup_analysis_dirs command will automatically transfer these values into the single cell project metadata on creation.
Additionally for certain types of data, setup_analysis_dirs will
also create template control files for use in subsequent QC runs:
Single cell multiome: a template 10x_multiome_libraries.info file, which should be renamed and populated in order to link each ATAC (or GEX) sample to the complementary GEX (or ATAC) sample.
CellPlex and Flex: a template 10x_multi_config.csv file, which should be renamed and populated with information on the feature types, multiplexed samples etc.
Single Cell immune profiling: a template 10x_multi_config.csv, which should be copied for each sample in the project with the name 10x_multi_config.<SAMPLE>.csv. Each one should then be populated with information on the Fastqs, feature types etc for that sample.
The run_qc command will then determine the appropriate QC protocol to use based on the metadata values.
Note
Currently a full QC pipeline is not implemented for single cell immune profiling data: see Manual QC steps for single cell immune profiling data for additional manual steps that can be performed for these types of data.
Troubleshooting
Single-library analyses fail for low read counts
It has been observed that when the Fastq files produced by the mkfastq
command have very low read counts then the single-library analyses may
fail, with cellranger count reporting an error of the form e.g.:
Could not auto-detect Single Cell 3' chemistry. Fraction of barcodes
on whitelist was at best 0.23%, while we expected at least 10.00% for
one of the chemistries.
There is currently no workaround for this issue.
Single-library analyses fail to detect chemistry automatically
By default cellranger count attempts to determine the chemistry used
automatically, however this may fail if a low number of reads map to the
reference genome and give an error of the form:
The chemistry was unable to be automatically determined. This can
happen if not enough reads originate from the given reference. Please
verify your choice of reference or explicitly specify the chemistry
via the --chemistry argument.
If the reference data being used is correct then use the --chemistry
option to specify the appropriate assay configuration - see
https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/count
Appendices
Manual QC steps for single cell immune profiling data
Currently a full automated QC protocol is not available for Chromium
5’ single cell immune profiling: specifically, there is no provision
for running Cellranger’s multi pipeline for each sample, or for
automatically integrating the resulting outputs into the QC report.
It is possible to run the multi pipeline manually for each sample,
using the sample-specific 10x_multi_config.<SAMPLE>.csv files.
For example, a script of the form:
#!/usr/bin/bash
#$ -N cellranger_multi_PJB01
#$ -V
#$ -cwd
#$ -j y
#$ -pe smp.pe 16
#$ -l mem256
mkdir -p cellranger_multi && cd cellranger_multi
/PATH/TO/cellranger multi \
--id PJB01 --csv PATH/TO/10x_multi_config.PJB01.csv \
--jobmode=local \
--localcores=16 \
--localmem=128 \
--maxjobs=24 \
--jobinterval=100
could be used to submit a Cellranger multi job for the PJB01
sample, with the outputs being created in a subdirectory
cellranger_multi/PJB01 in the current directory.
To include the outputs in the QC report, copy the relevant files
(specifically the web_summary.html files for each sample) into
the QC directory and then create an extra_outputs.tsv which
references these (as described in
Including external (non-pipeline) outputs).
For example:
cellranger_multi/PJB01/web_summary.html CellRanger multi output for PJB01
Rerunning run_qc will force update of the QC report which should
then also link in these additional reports.
Details for handling pooled single cell multiome ATAC and GEX data
If 10x Genomics single cell multiome ATAC and multiome GEX libraries
are sequenced together in the same run then the standard 10x_multiome
protocol of the make_fastqs command is unable to correctly process
the data.
Pooling the ATAC and GEX components of a single cell multiome experiment
is not officially supported by 10x Genomics, and this limitation is due
to this configuration not being supported by the cellranger-arc
pipeline. However they do provide information on how to handle this
situation in this knowledge base article:
and the two sub-protocols outlined in that article have been implemented
within make_fastqs as the 10x_multiome_atac and 10_multiome_gex
protocols, which should be used as follows:
Ensure that ATAC and GEX data are assigned to separate projects in the input sample sheet
Use the
--lanesoption to explicitly specify the appropriate sub-protocol for the lanes with the ATAC and GEX samples
For example:
auto_process.py make_fastqs \
--lanes=1:10x_multiome_atac \
--lanes=2:10x_multiome_gex
assuming that the ATAC data are in lane 1 and the GEX data in lane 2.
Warning
These protocols should only be used when the single cell
multiome data has been pooled with other types of data;
when the single cell multiome data for a single component
(either GEX or ATAC) comprises the whole sequencing run
then the 10x_multiome protocol should be used instead.
The 10x_multiome_atac protocol then runs cellranger-arc mkfastq
with the following custom options:
--use-bases-maskwith a bases mask string that has been adjusted appropriately to match the templateY*,I8n*,Y24,Y*
--filter-single-indexis explicitly specified
The 10x_multiome_gex protocol runs cellranger-arc mkfastq with
the following custom options:
--use-bases-maskwith a bases mask string that has been adjusted appropriately to match the templateY28n*,I10,I10n*,Y*
--filter-dual-indexis explicitly specified