Qc

A pipeline to add basic qc statistics to a MuData

Info

ID: qc
Namespace: workflows/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 2.1.0 -latest \
  -main-script target/nextflow/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"

# Mitochondrial & Ribosomal Gene Detection
# var_gene_names: "gene_symbol"
# var_name_mitochondrial_genes: "foo"
# obs_name_mitochondrial_fraction: "foo"
mitochondrial_gene_regex: "^[mM][tT]-"
# var_name_ribosomal_genes: "foo"
# obs_name_ribosomal_fraction: "foo"
ribosomal_gene_regex: "^[Mm]?[Rr][Pp][LlSs]"

# QC metrics calculation options
# var_qc_metrics: ["ercc,highly_variable"]
top_n_vars: [50, 100, 200, 500]
output_obs_num_nonzero_vars: "num_nonzero_vars"
output_obs_total_counts_vars: "total_counts"
output_var_num_nonzero_obs: "num_nonzero_obs"
output_var_total_counts_obs: "total_counts"
output_var_obs_mean: "obs_mean"
output_var_pct_dropout: "pct_dropout"

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

# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"

# Arguments
nextflow run openpipelines-bio/openpipeline \
  -r 2.1.0 -latest \
  -profile docker \
  -main-script target/nextflow/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 & Ribosomal Gene Detection

Name Description Attributes
--var_gene_names .var column name to be used to detect mitochondrial/ribosomal genes instead of .var_names (default if not set). Gene names matching with the regex value from –mitochondrial_gene_regex or –ribosomal_gene_regex will be identified as mitochondrial or ribosomal genes, respectively. string, example: "gene_symbol"
--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
--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]-"
--var_name_ribosomal_genes In which .var slot to store a boolean array corresponding the ribosomal genes. string
--obs_name_ribosomal_fraction When specified, write the fraction of counts originating from ribosomal genes (based on –ribosomal_gene_regex) to an .obs column with the specified name. Requires –var_name_ribosomal_genes. string
--ribosomal_gene_regex Regex string that identifies ribosomal genes from –var_gene_names. By default will detect human and mouse ribosomal genes from a gene symbol. string, default: "^[Mm]?[Rr][Pp][LlSs]"

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: ","
--output_obs_num_nonzero_vars Name of column in .obs describing, for each observation, the number of stored values (including explicit zeroes). In other words, the name of the column that counts for each row the number of columns that contain data. string, default: "num_nonzero_vars"
--output_obs_total_counts_vars Name of the column for .obs describing, for each observation (row), the sum of the stored values in the columns. string, default: "total_counts"
--output_var_num_nonzero_obs Name of column describing, for each feature, the number of stored values (including explicit zeroes). In other words, the name of the column that counts for each column the number of rows that contain data. string, default: "num_nonzero_obs"
--output_var_total_counts_obs Name of the column in .var describing, for each feature (column), the sum of the stored values in the rows. string, default: "total_counts"
--output_var_obs_mean Name of the column in .obs providing the mean of the values in each row. string, default: "obs_mean"
--output_var_pct_dropout Name of the column in .obs providing for each feature the percentage of observations the feature does not appear on (i.e. is missing). Same as --output_var_num_nonzero_obs but percentage based. string, default: "pct_dropout"

Outputs

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

Authors

  • Dries Schaumont (author, maintainer)

Visualisation

flowchart TB
    v0(Channel.fromList)
    v2(filter)
    v14(branch)
    v41(concat)
    v19(grep_mitochondrial_genes)
    v26(cross)
    v36(cross)
    v45(branch)
    v72(concat)
    v50(grep_ribosomal_genes)
    v57(cross)
    v67(cross)
    v73(filter)
    v103(concat)
    v81(calculate_qc_metrics)
    v88(cross)
    v98(cross)
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    v140(Output)
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