integrate/scarches

Description

Performs reference mapping with scArches

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" ]  
sanitize_ensembl_ids: [ true ]  
output: "$id.$key.output"  
model_output: "$id.$key.model_output"  
obsm_output: [ "X_integrated_scanvi" ]  
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 v4.0.1 \  
  -main-script target/nextflow/integrate/scarches/main.nf \  
  -params-file params.yaml

Inputs

Name	Type & Properties	Description
--input -i	file required	Input h5mu file to use as a query
--layer	string	Layer to be used for scArches, if .X is not to be used.
--modality	string	Which modality from the input MuData file to process.
--input_obs_batch	string	Name of the .obs column with batch information.
--input_obs_label	string	Name of the .obs column with celltype information.
--input_var_gene_names	string	Name of the .var column with gene names, if the var .index is not to be used.
--input_obs_size_factor	string	Key in adata.obs for size factor information. Instead of using library size as a size factor, the provided size factor column will be used as offset in the mean of the likelihood. Assumed to be on linear scale.
--input_obs_categorical_covariate	string multiple	Keys in adata.obs that correspond to categorical data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for. Important: the order of the categorical covariates matters and should match the order of the covariates in the trained reference model.
--input_obs_continuous_covariate	string multiple	Keys in adata.obs that correspond to continuous data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for. Important: the order of the continuous covariates matters and should match the order of the covariates in the trained reference model.
--sanitize_ensembl_ids	boolean	Whether to sanitize ensembl ids by removing version numbers.

Name

Type & Properties

--input

-i

file

required

--layer

string

--modality

string

--input_obs_batch

string

--input_obs_label

string

--input_var_gene_names

string

--input_obs_size_factor

string

--input_obs_categorical_covariate

string

multiple

--input_obs_continuous_covariate

string

multiple

--sanitize_ensembl_ids

boolean

Reference

Name	Type & Properties	Description
--reference -r	file required	Path to the directory with reference model or a web link.
--reference_class	string	For legacy models; the type of model (where the type of model was not saved with it; e.g. when they were generated with scvi-tools versions < 0.15).

Name

Type & Properties

--reference

-r

file

required

--reference_class

string

Outputs

Name	Type & Properties	Description
--output -o	file required output	Output h5mu file.
--model_output	file output	Output directory for model
--obsm_output	string	In which .obsm slot to store the resulting integrated embedding.
--obs_output_predictions	string	In which .obs slot to store the resulting label predictions. Only relevant if a scANVI model was provided.
--obs_output_probabilities	string	In which .obs slot to store the probabilities of the label predictions. Only relevant if a scANVI model was provided.
--output_compression	string	Compression format to use for the output AnnData and/or Mudata objects. By default no compression is applied.

Name

Type & Properties

--output

-o

file

required

output

--model_output

file

output

--obsm_output

string

--obs_output_predictions

string

--obs_output_probabilities

string

--output_compression

string

Early stopping 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.

Name

Type & Properties

--early_stopping

boolean

--early_stopping_monitor

string

--early_stopping_patience

integer

--early_stopping_min_delta

double

Learning parameters

Name	Type & Properties	Description
--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

--max_epochs

integer

--reduce_lr_on_plateau

boolean

--lr_factor

double

--lr_patience

double