annotate/celltypist

Description

Automated cell type annotation tool for scRNA-seq datasets on the basis of logistic regression classifiers optimised by the stochastic gradient descent algorithm.

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_layer: [ "log_normalized" ]  
input_reference_gene_overlap: [ 100 ]  
sanitize_ensembl_ids: [ true ]  
reference_obs_target: [ "cell_ontology_class" ]  
reference_var_input: [ "filter_with_hvg" ]  
feature_selection: [ false ]  
majority_voting: [ false ]  
C: [ 1 ]  
max_iter: [ 1000 ]  
min_prop: [ 0 ]  
output: "$id.$key.output.h5mu"  
output_obs_predictions: [ "celltypist_pred" ]  
output_obs_probability: [ "celltypist_probability" ]  
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/celltypist/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 containing counts that are lognormalized to 10000, .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

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 containing counts that are lognormalized to 10000, if .X is not to be used.
--reference_obs_target	string	The name of the adata obs column in the reference data containing cell type annotations.
--reference_var_input	string	.var column containing highly variable genes. If not provided, genes will not be subset.
--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.

Name

Type & Properties

--reference

file

--reference_layer

string

--reference_obs_target

string

--reference_var_input

string

--reference_var_gene_names

string

Model arguments

Name	Type & Properties	Description
--model	file	Pretrained model in pkl format. If not provided, the model will be trained on the reference data and --reference should be provided.
--feature_selection	boolean	Whether to perform feature selection.
--majority_voting	boolean	Whether to refine the predicted labels by running the majority voting classifier after over-clustering.
--C	double	Inverse of regularization strength in logistic regression.
--max_iter	integer	Maximum number of iterations before reaching the minimum of the cost function.
--use_SGD	boolean_true	Whether to use the stochastic gradient descent algorithm.
--min_prop	double	"For the dominant cell type within a subcluster, the minimum proportion of cells required to support naming of the subcluster by this cell type. Ignored if majority_voting is set to False. Subcluster that fails to pass this proportion threshold will be assigned 'Heterogeneous'."

Name

Type & Properties

--model

file

--feature_selection

boolean

--majority_voting

boolean

--C

double

--max_iter

integer

--use_SGD

boolean_true

--min_prop

double

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