Filter with counts

Filter scRNA-seq data based on the primary QC metrics.

Info

ID: filter_with_counts
Namespace: filter

This is based on both the UMI counts, the gene counts and the mitochondrial genes (genes starting with mt/MT)

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 0.10.0 -latest \
  -main-script target/nextflow/filter/filter_with_counts/main.nf \
  --help

Run command

Example of params.yaml 
# Inputs
input: # please fill in - example: "input.h5mu"
modality: "rna"
# layer: "raw_counts"
# var_gene_names: "gene_symbol"
mitochondrial_gene_regex: "^[mM][tT]-"

# Outputs
# output: "$id.$key.output.h5mu"
# output_compression: "gzip"
do_subset: false
obs_name_filter: "filter_with_counts"
var_name_filter: "filter_with_counts"
# var_name_mitochondrial_genes: "foo"

# Arguments
# min_counts: 200
# max_counts: 5000000
# min_genes_per_cell: 200
# max_genes_per_cell: 1500000
# min_cells_per_gene: 3
# min_fraction_mito: 0
# max_fraction_mito: 0.2

# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"
nextflow run openpipelines-bio/openpipeline \
  -r 0.10.0 -latest \
  -profile docker \
  -main-script target/nextflow/filter/filter_with_counts/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 Input h5mu file file, required, example: "input.h5mu"
--modality string, default: "rna"
--layer string, example: "raw_counts"
--var_gene_names .var column name to be used to detect mitochondrial genes instead of .var_names (default if not set). Gene names matching with the regex value from –mitochondrial_gene_regex will be identified as a mitochondrial gene. string, example: "gene_symbol"
--mitochondrial_gene_regex Regex string that identifies mitochondrial genes from –var_gene_names. By default will detect human and mouse mitochondrial genes from a gene symbol. string, default: "^[mM][tT]-"

Outputs

Name Description Attributes
--output Output h5mu file. file, example: "output.h5mu"
--output_compression The compression format to be used on the output h5mu object. string, example: "gzip"
--do_subset Whether to subset before storing the output. boolean_true
--obs_name_filter In which .obs slot to store a boolean array corresponding to which observations should be removed. string, default: "filter_with_counts"
--var_name_filter In which .var slot to store a boolean array corresponding to which variables should be removed. string, default: "filter_with_counts"
--var_name_mitochondrial_genes In which .var slot to store a boolean array corresponding the mitochondrial genes. Will only be used if –min_fraction_mito or –max_fraction_mito are specified. string

Arguments

Name Description Attributes
--min_counts Minimum number of counts captured per cell. integer, example: 200
--max_counts Maximum number of counts captured per cell. integer, example: 5000000
--min_genes_per_cell Minimum of non-zero values per cell. integer, example: 200
--max_genes_per_cell Maximum of non-zero values per cell. integer, example: 1500000
--min_cells_per_gene Minimum of non-zero values per gene. integer, example: 3
--min_fraction_mito Minimum fraction of UMIs that are mitochondrial. double, example: 0
--max_fraction_mito Maximum fraction of UMIs that are mitochondrial. double, example: 0.2

Authors

  • Dries De Maeyer (author)

  • Robrecht Cannoodt (maintainer, author)