annotate/onclass

Description

OnClass is a python package for single-cell cell type annotation. It uses the Cell Ontology to capture the cell type similarity.
These similarities enable OnClass to annotate cell types that are never seen in the training data.

Type

python_script

License

MIT

Links

Repository

Contributors

Launch

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
modality: [ "rna" ]  
input_reference_gene_overlap: [ 100 ]  
sanitize_ensembl_ids: [ true ]  
unknown_celltype: [ "Unknown" ]  
output: "$id.$key.output.h5mu"  
output_obs_predictions: [ "onclass_pred" ]  
output_obs_probability: [ "onclass_prob" ]  
max_iter: [ 30 ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v4.2.0 \  
  -main-script target/nextflow/annotate/onclass/main.nf \  
  -params-file params.yaml

Inputs

Name	Type & Properties	Description
--input -i	file required	The input (query) data to be labeled. Should be a .h5mu file.
--modality	string	Which modality to process.
--input_layer	string	The layer in the input data to be used for cell type annotation if .X is not to be used.
--input_var_gene_names	string	The name of the adata var column in the input data containing gene names; when no gene_name_layer is provided, the var index will be used.
--input_reference_gene_overlap	integer	The minimum number of genes present in both the reference and query datasets.
--sanitize_ensembl_ids	boolean	Whether to sanitize ensembl ids by removing version numbers.

Name

Type & Properties

--input

-i

file

required

--modality

string

--input_layer

string

--input_var_gene_names

string

--input_reference_gene_overlap

integer

--sanitize_ensembl_ids

boolean

Ontology

Name	Type & Properties	Description
--cl_nlp_emb_file	file required	The .nlp.emb file with the cell type embeddings.
--cl_ontology_file	file required	The .ontology file with the cell type ontology.
--cl_obo_file	file required	The .obo file with the cell type ontology.

Name

Type & Properties

--cl_nlp_emb_file

file

required

--cl_ontology_file

file

required

--cl_obo_file

file

required

Reference

Name	Type & Properties	Description
--reference	file	The reference data to train the CellTypist classifiers on. Only required if a pre-trained --model is not provided.
--reference_layer	string	The layer in the reference data to be used for cell type annotation if .X is not to be used.
--reference_obs_target	string required	The name of the adata obs column in the reference data containing cell type annotations.
--reference_var_gene_names	string	The name of the adata var column in the reference data containing gene names; when no gene_name_layer is provided, the var index will be used.
--reference_var_input	string	.var column containing highly variable genes. By default, do not subset genes.
--unknown_celltype	string	Label for unknown cell types.

Name

Type & Properties

--reference

file

--reference_layer

string

--reference_obs_target

string

required

--reference_var_gene_names

string

--reference_var_input

string

--unknown_celltype

string

Outputs

Name	Type & Properties	Description
--output	file output	Output h5mu file.
--output_obs_predictions	string	In which `.obs` slots to store the predicted information.
--output_obs_probability	string	In which `.obs` slots to store the probability of the predictions.
--output_compression	string	Compression format to use for the output AnnData and/or Mudata H5 files. By default no compression is applied.

Name

Type & Properties

--output

file

output

--output_obs_predictions

string

--output_obs_probability

string

--output_compression

string

Model arguments

Name	Type & Properties	Description
--model	string	"Pretrained model path without a file extension. If not provided, the model will be trained on the reference data and --reference should be provided. The path namespace should contain: - a .npz or .pkl file - a .data file - a .meta file - a .index file e.g. /path/to/model/pretrained_model_target1 as saved by OnClass."
--max_iter	integer	Maximum number of iterations for training the model.

Name

Type & Properties

--model

string

--max_iter

integer