annotate/scanvi

Description

scANVI () is a semi-supervised model for single-cell transcriptomics data. scANVI is an scVI extension that can leverage the cell type knowledge for a subset of the cells present in the data sets to infer the states of the rest of the cells.
This component will instantiate a scANVI model from a pre-trained scVI model, integrate the data and perform label prediction.

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" ]  
unlabeled_category: [ "Unknown" ]  
sanitize_ensembl_ids: [ true ]  
output: "$id.$key.output.h5mu"  
output_model: "$id.$key.output_model.path/to/output_model"  
obsm_output: [ "X_scanvi_integrated" ]  
obs_output_predictions: [ "scanvi_pred" ]  
obs_output_probabilities: [ "scanvi_proba" ]  
early_stopping_monitor: [ "elbo_validation" ]  
early_stopping_patience: [ 45 ]  
early_stopping_min_delta: [ 0 ]  
reduce_lr_on_plateau: [ true ]  
lr_factor: [ 0.6 ]  
lr_patience: [ 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/scanvi/main.nf \  
  -params-file params.yaml

Inputs

Name	Type & Properties	Description
--input -i	file required	Input h5mu file. Note that this needs to be the exact same dataset as the --scvi_model was trained on.
--modality	string	Which modality from the input MuData file to process.
--input_layer	string	Input layer to use. If None, X is used
--var_input	string	.var column containing highly variable genes that were used to train the scVi model. By default, do not subset genes.
--var_gene_names	string	.var column containing gene names. By default, use the index.
--obs_labels	string required	.obs field containing the labels
--unlabeled_category	string	Value in the --obs_labels field that indicates unlabeled observations
--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

--var_input

string

--var_gene_names

string

--obs_labels

string

required

--unlabeled_category

string

--sanitize_ensembl_ids

boolean

scVI Model

Name	Type & Properties	Description
--scvi_model	file required	Pretrained SCVI reference model to initialize the SCANVI model with.

Name

Type & Properties

--scvi_model

file

required

Outputs

Name	Type & Properties	Description
--output -o	file required output	Output h5mu file.
--output_model	file output	Folder where the state of the trained model will be saved to.
--obsm_output	string	In which .obsm slot to store the resulting integrated embedding.
--obs_output_predictions	string	In which .obs slot to store the predicted labels.
--obs_output_probabilities	string	In which. obs slot to store the probabilities of the predicted labels.
--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

-o

file

required

output

--output_model

file

output

--obsm_output

string

--obs_output_predictions

string

--obs_output_probabilities

string

--output_compression

string

scANVI training arguments

Name	Type & Properties	Description
--early_stopping	boolean	Whether to perform early stopping with respect to the validation set.
--early_stopping_monitor	string	Metric logged during validation set epoch.
--early_stopping_patience	integer	Number of validation epochs with no improvement after which training will be stopped.
--early_stopping_min_delta	double	Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.
--max_epochs	integer	Number of passes through the dataset, defaults to (20000 / number of cells) * 400 or 400; whichever is smallest.
--reduce_lr_on_plateau	boolean	Whether to monitor validation loss and reduce learning rate when validation set `lr_scheduler_metric` plateaus.
--lr_factor	double	Factor to reduce learning rate.
--lr_patience	double	Number of epochs with no improvement after which learning rate will be reduced.

Name

Type & Properties

--early_stopping

boolean

--early_stopping_monitor

string

--early_stopping_patience

integer

--early_stopping_min_delta

double

--max_epochs

integer

--reduce_lr_on_plateau

boolean

--lr_factor

double

--lr_patience

double