Rna singlesample

Processing unimodal single-sample RNA transcriptomics data.

Info

ID: rna_singlesample
Namespace: multiomics

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 0.12.0 -latest \
  -main-script ./workflows/multiomics/rna_singlesample/main.nf \
  --help

Run command

Example of params.yaml 
# Input
id: # please fill in - example: "foo"
input: # please fill in - example: "dataset.h5mu"

# Output
# output: "$id.$key.output.h5mu"

# Filtering options
# 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

# Mitochondrial gene detection
# var_name_mitochondrial_genes: "foo"
# obs_name_mitochondrial_fraction: "foo"
# var_gene_names: "gene_symbol"
mitochondrial_gene_regex: "^[mM][tT]-"

# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"
nextflow run openpipelines-bio/openpipeline \
  -r 0.12.0 -latest \
  -profile docker \
  -main-script ./workflows/multiomics/rna_singlesample/main.nf \
  -params-file params.yaml
Note

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

Argument groups

Input

Name Description Attributes
--id ID of the sample. string, required, example: "foo"
--input Path to the sample. file, required, example: "dataset.h5mu"

Output

Name Description Attributes
--output Destination path to the output. file, required, example: "output.h5mu"

Filtering options

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. Requires –obs_name_mitochondrial_fraction. double, example: 0
--max_fraction_mito Maximum fraction of UMIs that are mitochondrial. Requires –obs_name_mitochondrial_fraction. double, example: 0.2

Mitochondrial gene detection

Name Description Attributes
--var_name_mitochondrial_genes In which .var slot to store a boolean array corresponding the mitochondrial genes. string
--obs_name_mitochondrial_fraction When specified, write the fraction of counts originating from mitochondrial genes (based on –mitochondrial_gene_regex) to an .obs column with the specified name. Requires –var_name_mitochondrial_genes. string
--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]-"

Authors

  • Dries De Maeyer (author)

  • Robrecht Cannoodt (author, maintainer)

  • Dries Schaumont (author)

Visualisation

flowchart LR
    v0(Input)
    v2(toSortedList)
    v4(flatMap)
    v10(filter)
    v16(grep_annotation_column)
    v18(join)
    v22(mix)
    v21(filter)
    v28(calculate_qc_metrics)
    v30(join)
    v38(publish)
    v40(join)
    v44(filter)
    v51(delimit_fraction)
    v53(join)
    v57(mix)
    v56(filter)
    v63(filter_with_counts)
    v65(join)
    v74(do_filter)
    v76(join)
    v84(filter_with_scrublet)
    v86(join)
    v93(Output)
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