Popv

Performs popular major vote cell typing on single cell sequence data using multiple algorithms.

Info

ID: popv
Namespace: annotate

Note that this is a one-shot version of PopV.

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 0.12.0 -latest \
  -main-script target/nextflow/annotate/popv/main.nf \
  --help

Run command

Example of params.yaml
# Inputs
input: # please fill in - example: "input.h5mu"
modality: "rna"
# input_layer: "foo"
# input_obs_batch: "foo"
# input_var_subset: "foo"
# input_obs_label: "foo"
unknown_celltype_label: "unknown"

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

# Arguments
methods: # please fill in - example: ["knn_on_scvi", "scanvi"]

# Reference
reference: # please fill in - example: "TS_Bladder_filtered.h5ad"
# reference_layer: "foo"
reference_obs_label: "cell_ontology_class"
reference_obs_batch: "donor_assay"

# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"
nextflow run openpipelines-bio/openpipeline \
  -r 0.12.0 -latest \
  -profile docker \
  -main-script target/nextflow/annotate/popv/main.nf \
  -params-file params.yaml
Note

Replace -profile docker with -profile podman or -profile singularity depending on the desired backend.

Argument groups

Inputs

Arguments related to the input (aka query) dataset.

Name Description Attributes
--input Input h5mu file. file, required, example: "input.h5mu"
--modality Which modality to process. string, default: "rna"
--input_layer Which layer to use. If no value is provided, the counts are assumed to be in the .X slot. Otherwise, count data is expected to be in .layers[input_layer]. string
--input_obs_batch Key in obs field of input adata for batch information. If no value is provided, batch label is assumed to be unknown. string
--input_var_subset Subset the input object with this column. string
--input_obs_label Key in obs field of input adata for label information. This is only used for training scANVI. Unlabelled cells should be set to "unknown_celltype_label". string
--unknown_celltype_label If input_obs_label is specified, cells with this value will be treated as unknown and will be predicted by the model. string, default: "unknown"

Reference

Arguments related to the reference dataset.

Name Description Attributes
--reference User-provided reference tissue. The data that will be used as reference to call cell types. file, required, example: "TS_Bladder_filtered.h5ad"
--reference_layer Which layer to use. If no value is provided, the counts are assumed to be in the .X slot. Otherwise, count data is expected to be in .layers[reference_layer]. string
--reference_obs_label Key in obs field of reference AnnData with cell-type information. string, default: "cell_ontology_class"
--reference_obs_batch Key in obs field of input adata for batch information. string, default: "donor_assay"

Outputs

Output arguments.

Name Description Attributes
--output Output h5mu file. file, required, example: "output.h5mu"
--output_compression string, example: "gzip"

Arguments

Other arguments.

Name Description Attributes
--methods Methods to call cell types. By default, runs to knn_on_scvi and scanvi. List of string, required, example: "knn_on_scvi", "scanvi", multiple_sep: ":"

Authors

  • Matthias Beyens (author)

  • Robrecht Cannoodt (author)