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 2.1.0 -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 2.1.0 -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
    v54-->v81
    v81-->v82
    v138-->v165
    v169-->v196
    v196-->v197
    v0-->v2
    v2-->v11
    v23-->v28
    v28-->v35
    v23-->v35
    v23-->v45
    v45-->v50
    v54-->v59
    v59-->v66
    v54-->v66
    v54-->v76
    v76-->v81
    v82-->v90
    v90-->v97
    v82-->v97
    v82-->v107
    v107-->v112
    v112-->v119
    v11-->v119
    v11-->v129
    v138-->v143
    v143-->v150
    v138-->v150
    v138-->v160
    v160-->v165
    v169-->v174
    v174-->v181
    v169-->v181
    v169-->v191
    v191-->v196
    v197-->v205
    v205-->v212
    v197-->v212
    v197-->v222
    v228-->v236
    v236-->v243
    v228-->v243
    v228-->v253
    v259-->v267
    v267-->v274
    v259-->v274
    v259-->v284
    v284-->v289
    v289-->v296
    v2-->v296
    v296-->v303
    v2-->v303
    v2-->v315
    v315-->v322
    v2-->v322
    v322-->v326
    v11-->v23
    v28-->v45
    v50-->v54
    v59-->v76
    v90-->v107
    v112-->v129
    v129-->v138
    v143-->v160
    v165-->v169
    v174-->v191
    v222-->v228
    v205-->v222
    v253-->v259
    v236-->v253
    v267-->v284
    v289-->v315
    style group_qc fill:#F0F0F0,stroke:#969696;
    style v0 fill:#e3dcea,stroke:#7a4baa;
    style v2 fill:#e3dcea,stroke:#7a4baa;
    style v11 fill:#e3dcea,stroke:#7a4baa;
    style v23 fill:#e3dcea,stroke:#7a4baa;
    style v50 fill:#e3dcea,stroke:#7a4baa;
    style v28 fill:#e3dcea,stroke:#7a4baa;
    style v35 fill:#e3dcea,stroke:#7a4baa;
    style v45 fill:#e3dcea,stroke:#7a4baa;
    style v54 fill:#e3dcea,stroke:#7a4baa;
    style v81 fill:#e3dcea,stroke:#7a4baa;
    style v59 fill:#e3dcea,stroke:#7a4baa;
    style v66 fill:#e3dcea,stroke:#7a4baa;
    style v76 fill:#e3dcea,stroke:#7a4baa;
    style v82 fill:#e3dcea,stroke:#7a4baa;
    style v112 fill:#e3dcea,stroke:#7a4baa;
    style v90 fill:#e3dcea,stroke:#7a4baa;
    style v97 fill:#e3dcea,stroke:#7a4baa;
    style v107 fill:#e3dcea,stroke:#7a4baa;
    style v119 fill:#e3dcea,stroke:#7a4baa;
    style v129 fill:#e3dcea,stroke:#7a4baa;
    style v138 fill:#e3dcea,stroke:#7a4baa;
    style v165 fill:#e3dcea,stroke:#7a4baa;
    style v143 fill:#e3dcea,stroke:#7a4baa;
    style v150 fill:#e3dcea,stroke:#7a4baa;
    style v160 fill:#e3dcea,stroke:#7a4baa;
    style v169 fill:#e3dcea,stroke:#7a4baa;
    style v196 fill:#e3dcea,stroke:#7a4baa;
    style v174 fill:#e3dcea,stroke:#7a4baa;
    style v181 fill:#e3dcea,stroke:#7a4baa;
    style v191 fill:#e3dcea,stroke:#7a4baa;
    style v197 fill:#e3dcea,stroke:#7a4baa;
    style v205 fill:#e3dcea,stroke:#7a4baa;
    style v212 fill:#e3dcea,stroke:#7a4baa;
    style v222 fill:#e3dcea,stroke:#7a4baa;
    style v228 fill:#e3dcea,stroke:#7a4baa;
    style v236 fill:#e3dcea,stroke:#7a4baa;
    style v243 fill:#e3dcea,stroke:#7a4baa;
    style v253 fill:#e3dcea,stroke:#7a4baa;
    style v259 fill:#e3dcea,stroke:#7a4baa;
    style v289 fill:#e3dcea,stroke:#7a4baa;
    style v267 fill:#e3dcea,stroke:#7a4baa;
    style v274 fill:#e3dcea,stroke:#7a4baa;
    style v284 fill:#e3dcea,stroke:#7a4baa;
    style v296 fill:#e3dcea,stroke:#7a4baa;
    style v303 fill:#e3dcea,stroke:#7a4baa;
    style v315 fill:#e3dcea,stroke:#7a4baa;
    style v322 fill:#e3dcea,stroke:#7a4baa;
    style v326 fill:#e3dcea,stroke:#7a4baa;