Cellranger count

Align fastq files using Cell Ranger count.

Info

ID: cellranger_count
Namespace: mapping

Example commands

You can run the pipeline using nextflow run.

View help

You can use --help as a parameter to get an overview of the possible parameters.

nextflow run openpipelines-bio/openpipeline \
  -r 2.1.0 -latest \
  -main-script target/nextflow/mapping/cellranger_count/main.nf \
  --help

Run command

Example of params.yaml 
# Inputs
input: # please fill in - example: ["sample_S1_L001_R1_001.fastq.gz", "sample_S1_L001_R2_001.fastq.gz"]
reference: # please fill in - example: "reference.tar.gz"

# Outputs
# output: "$id.$key.output"

# Arguments
# expect_cells: 3000
# force_cells: 3000
chemistry: "auto"
secondary_analysis: false
generate_bam: true
include_introns: true
# r1_length: 123
# r2_length: 123
# lanes: [1, 2, 3]
library_compatibility_check: true
# min_crispr_umi: 123

# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"
nextflow run openpipelines-bio/openpipeline \
  -r 2.1.0 -latest \
  -profile docker \
  -main-script target/nextflow/mapping/cellranger_count/main.nf \
  -params-file params.yaml
Note

Replace -profile docker with -profile podman or -profile singularity depending on the desired backend.

Argument groups

Inputs

Name Description Attributes
--input The fastq.gz files to align. Can also be a single directory containing fastq.gz files. List of file, required, example: "sample_S1_L001_R1_001.fastq.gz", "sample_S1_L001_R2_001.fastq.gz", multiple_sep: ";"
--reference The path to Cell Ranger reference tar.gz file. Can also be a directory. file, required, example: "reference.tar.gz"

Outputs

Name Description Attributes
--output The folder to store the alignment results. file, required, example: "/path/to/output"

Arguments

Name Description Attributes
--expect_cells Expected number of recovered cells, used as input to cell calling algorithm. integer, example: 3000
--force_cells Force pipeline to use this number of cells, bypassing cell calling algorithm. integer, example: 3000
--chemistry Assay configuration. - auto: autodetect mode - threeprime: Single Cell 3’ - fiveprime: Single Cell 5’ - SC3Pv1: Single Cell 3’ v1 NOTE: this mode cannot be auto-detected. It must be set explicitly with this option. - SC3Pv2: Single Cell 3’ v2 - SC3Pv3: Single Cell 3’ v3 - SC3Pv4: Single Cell 3’ v4 - SC3Pv3LT: Single Cell 3’ v3 LT - SC3Pv3HT: Single Cell 3’ v3 HT - SC5P-PE-v3: Single Cell 5’ paired-end v3 (GEM-X) - SC5P-PE: Single Cell 5’ paired-end - SC5P-R2: Single Cell 5’ R2-only - SC-FB: Single Cell Antibody-only 3’ v2 or 5’ - ARC-v1: for analyzing the Gene Expression portion of Multiome data. NOTE: when the pipeline auto-detects ARC-v1 chemistry, an error is triggered. See https://kb.10xgenomics.com/hc/en-us/articles/115003764132-How-does-Cell-Ranger-auto-detect-chemistry- for more information. string, default: "auto"
--secondary_analysis Whether or not to run the secondary analysis e.g. clustering. boolean, default: FALSE
--generate_bam Whether to generate a BAM file. boolean, default: TRUE
--include_introns Include intronic reads in count. boolean, default: TRUE
--r1_length Hard trim the input Read 1 to this length before analysis integer
--r2_length Hard trim the input Read 2 to this length before analysis integer
--lanes Only use FASTQs from selected lanes. List of integer, example: 1, 2, 3, multiple_sep: ";"
--library_compatibility_check Whether to check for barcode compatibility between libraries. boolean, default: TRUE
--min_crispr_umi Set the minimum number of CRISPR guide RNA UMIs required for protospacer detection. If a lower or higher sensitivity is desired for detection, this value can be customized according to specific experimental needs. Applicable only to datasets that include a CRISPR Guide Capture library. integer

Authors

  • Angela Oliveira Pisco (author)

  • Samuel D’Souza (author)

  • Robrecht Cannoodt (author, maintainer)