Qc

A pipeline to add basic qc statistics to a MuData

Info

ID: qc
Namespace: qc

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 0.12.6 -latest \
  -main-script ./workflows/qc/qc/main.nf \
  --help

Run command

Example of params.yaml
# Inputs
id: # please fill in - example: "foo"
input: # please fill in - example: "input.h5mu"
modality: "rna"
# layer: "raw_counts"

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

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

# QC metrics calculation options
# var_qc_metrics: ["ercc", "highly_variable"]
top_n_vars: [50, 100, 200, 500]

# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"
nextflow run openpipelines-bio/openpipeline \
  -r 0.12.6 -latest \
  -profile docker \
  -main-script ./workflows/qc/qc/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
--id ID of the sample. string, required, example: "foo"
--input Path to the sample. file, required, example: "input.h5mu"
--modality Which modality to process. string, default: "rna"
--layer Layer to calculate qc metrics for. string, example: "raw_counts"

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 .Obs slot to store the fraction of reads found to be mitochondrial. Defaults to ‘fraction_’ suffixed by the value of –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]-"

QC metrics calculation options

Name Description Attributes
--var_qc_metrics Keys to select a boolean (containing only True or False) column from .var. For each cell, calculate the proportion of total values for genes which are labeled ‘True’, compared to the total sum of the values for all genes. Defaults to the value from –var_name_mitochondrial_genes. List of string, example: "ercc,highly_variable", multiple_sep: ","
--top_n_vars Number of top vars to be used to calculate cumulative proportions. If not specified, proportions are not calculated. --top_n_vars 20,50 finds cumulative proportion to the 20th and 50th most expressed vars. List of integer, default: 50, 100, 200, 500, multiple_sep: ","

Outputs

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

Authors

  • Dries Schaumont (author, maintainer)

Visualisation

flowchart LR
    v0(Input)
    v2(toSortedList)
    v4(flatMap)
    v7(filter)
    v13(grep_annotation_column)
    v15(join)
    v19(mix)
    v18(filter)
    v25(calculate_qc_metrics)
    v27(join)
    v35(publish)
    v37(join)
    v41(toSortedList)
    v43(Output)
    v18-->v19
    v0-->v2
    v2-->v4
    v4-->v7
    v4-->v18
    v7-->v15
    v7-->v13
    v13-->v15
    v15-->v19
    v19-->v27
    v19-->v25
    v25-->v27
    v27-->v37
    v27-->v35
    v35-->v37
    v37-->v41
    v41-->v43