flowchart TB v97(branch) v113(concat) v102(delimit_fraction) v122(rna_filter_with_counts) v142(rna_do_filter) v162(filter_with_scrublet) v189(Output) subgraph group_qc [qc] v0(Channel.fromList) v11(filter) v29(grep_annotation_column) v42(mix) v51(calculate_qc_metrics) v71(publish) end v97-->v113 v97-->v102 v0-->v11 v11-->v29 v29-->v42 v11-->v42 v42-->v51 v51-->v71 v71-->v97 v102-->v113 v113-->v122 v122-->v142 v142-->v162 v162-->v189 style group_qc fill:#F0F0F0,stroke:#969696; style v0 fill:#e3dcea,stroke:#7a4baa; style v11 fill:#e3dcea,stroke:#7a4baa; style v29 fill:#e3dcea,stroke:#7a4baa; style v42 fill:#e3dcea,stroke:#7a4baa; style v51 fill:#e3dcea,stroke:#7a4baa; style v71 fill:#e3dcea,stroke:#7a4baa; style v97 fill:#e3dcea,stroke:#7a4baa; style v113 fill:#e3dcea,stroke:#7a4baa; style v102 fill:#e3dcea,stroke:#7a4baa; style v122 fill:#e3dcea,stroke:#7a4baa; style v142 fill:#e3dcea,stroke:#7a4baa; style v162 fill:#e3dcea,stroke:#7a4baa; style v189 fill:#e3dcea,stroke:#7a4baa;
Rna singlesample
Processing unimodal single-sample RNA transcriptomics data.
Info
ID: rna_singlesample
Namespace: workflows/rna
Links
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 1.0.1 -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
# 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 1.0.1 -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 |
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]-" |