Rna singlesample

Processing unimodal single-sample RNA transcriptomics data.

Info

ID: rna_singlesample
Namespace: workflows/rna

Links

Source

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 2.1.1 -latest \
  -main-script target/nextflow/workflows/rna/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"
# layer: "foo"

# 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: 0.0
# max_fraction_mito: 0.2
# min_fraction_ribo: 0.0
# max_fraction_ribo: 0.2

# 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]"

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

# Arguments

nextflow run openpipelines-bio/openpipeline \
  -r 2.1.1 -latest \
  -profile docker \
  -main-script target/nextflow/workflows/rna/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"`
`--layer`	Input layer to start from. By default, .X will be used.	`string`

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`
`--min_fraction_ribo`	Minimum fraction of UMIs that are ribosomal. Requires –obs_name_ribosomal_fraction.	`double`, example: `0`
`--max_fraction_ribo`	Maximum fraction of UMIs that are ribosomal. Requires –obs_name_ribosomal_fraction.	`double`, example: `0.2`

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`	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`
`--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]"`

Authors

Dries De Maeyer (author)
Robrecht Cannoodt (author, maintainer)
Dries Schaumont (author)

Visualisation

flowchart TB
    v0(Channel.fromList)
    v2(filter)
    v11(filter)
    v23(branch)
    v50(concat)
    v35(cross)
    v45(cross)
    v54(branch)
    v81(concat)
    v66(cross)
    v76(cross)
    v82(filter)
    v112(concat)
    v97(cross)
    v107(cross)
    v119(cross)
    v129(cross)
    v138(branch)
    v165(concat)
    v143(delimit_fraction)
    v150(cross)
    v160(cross)
    v169(branch)
    v196(concat)
    v174(delimit_fraction)
    v181(cross)
    v191(cross)
    v197(filter)
    v205(rna_filter_with_counts)
    v212(cross)
    v222(cross)
    v228(filter)
    v236(rna_do_filter)
    v243(cross)
    v253(cross)
    v259(filter)
    v289(concat)
    v267(filter_with_scrublet)
    v274(cross)
    v284(cross)
    v296(cross)
    v303(cross)
    v315(cross)
    v322(cross)
    v326(Output)
    subgraph group_qc [qc]
        v28(grep_mitochondrial_genes)
        v59(grep_ribosomal_genes)
        v90(calculate_qc_metrics)
    end
    v23-->v50
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