Lianapy
    Performs LIANA integration based as described in https://github.com/saezlab/liana-py
  
Info
ID: lianapy
Namespace: interpret
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 2.1.1 -latest \
  -main-script target/nextflow/interpret/lianapy/main.nf \
  --helpRun command
Example of params.yaml
# Arguments
input: # please fill in - example: "path/to/file"
# output: "$id.$key.output"
output_compression: "gzip"
modality: "rna"
# layer: "foo"
groupby: # please fill in - example: "foo"
resource_name: "consensus"
gene_symbol: "gene_symbol"
expr_prop: 0.1
min_cells: 5
aggregate_method: "rra"
return_all_lrs: false
n_perms: 100
# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"nextflow run openpipelines-bio/openpipeline \
  -r 2.1.1 -latest \
  -profile docker \
  -main-script target/nextflow/interpret/lianapy/main.nf \
  -params-file params.yaml
Note
Replace -profile docker with -profile podman or -profile singularity depending on the desired backend.
Argument group
Arguments
| Name | Description | Attributes | 
|---|---|---|
| --input | Input h5mu file | file, required | 
| --output | Output h5mu file. | file, required | 
| --output_compression | string, default:"gzip" | |
| --modality | string, default:"rna" | |
| --layer | Layer in anndata.AnnData.layers to use. If None, use mudata.mod[modality].X. | string | 
| --groupby | The key of the observations grouping to consider. | string, required | 
| --resource_name | Name of the resource to be loaded and use for ligand-receptor inference. | string, default:"consensus" | 
| --gene_symbol | Column name in var DataFrame in which gene symbol are stored. | string, default:"gene_symbol" | 
| --expr_prop | Minimum expression proportion for the ligands/receptors (and their subunits) in the corresponding cell identities. Set to ‘0’, to return unfiltered results. | double, default:0.1 | 
| --min_cells | Minimum cells per cell identity (‘groupby’) to be considered for downstream analysis. | integer, default:5 | 
| --aggregate_method | Method aggregation approach, one of [‘mean’, ‘rra’], where ‘mean’ represents the mean rank, while ‘rra’ is the RobustRankAggregate (Kolde et al., 2014) of the interactions. | string, default:"rra" | 
| --return_all_lrs | Bool whether to return all LRs, or only those that surpass the ‘expr_prop’ threshold. Those interactions that do not pass the ‘expr_prop’ threshold will be assigned to the worst score of the ones that do. ‘False’ by default. | boolean, default:FALSE | 
| --n_perms | Number of permutations for the permutation test. Note that this is relevant only for permutation-based methods - e.g. ’CellPhoneDB | integer, default:100 |