workflows/annotation/scanvi_scarches

Description

Cell type annotation workflow using ScanVI with scArches for reference mapping.

Type

nextflow_script

License

MIT

Links

Repository

Contributors

Launch

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
id: "run"  
modality: [ "rna" ]  
sanitize_ensembl_ids: [ true ]  
unlabeled_category: [ "Unknown" ]  
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 ]  
leiden_resolution: [ 1 ]  
knn_weights: [ "uniform" ]  
knn_n_neighbors: [ 15 ]  
output: "$id.$key.output.h5mu"  
output_obs_predictions: [ "scanvi_pred" ]  
output_obs_probability: [ "scanvi_probabilities" ]  
output_obsm_integrated: [ "X_integrated_scanvi" ]  
output_model: "$id.$key.output_model"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v4.0.1 \  
  -main-script target/nextflow/workflows/annotation/scanvi_scarches/main.nf \  
  -params-file params.yaml

Query Input

Name	Type & Properties	Description
--id	string required	ID of the sample.
--input	file required	Input dataset consisting of the (unlabeled) query observations. The dataset is expected to be pre-processed in the same way as --reference.
--modality	string	Which modality to process. Should match the modality of the --reference dataset.
--layer	string	Which layer to use for integration if .X is not to be used. Should match the layer of the --reference dataset.
--input_obs_batch_label	string required	The .obs field in the input (query) dataset containing the batch labels.
--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 reference data.
--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 reference data.
--input_var_gene_names	string	.var column containing gene names. By default, use the index.
--sanitize_ensembl_ids	boolean	Whether to sanitize ensembl ids by removing version numbers.

Name

Type & Properties

--id

string

required

--input

file

required

--modality

string

--layer

string

--input_obs_batch_label

string

required

--input_obs_size_factor

string

--input_obs_categorical_covariate

string

multiple

--input_obs_continuous_covariate

string

multiple

--input_var_gene_names

string

--sanitize_ensembl_ids

boolean

Reference input

Name	Type & Properties	Description
--reference	file required	Reference dataset consisting of the labeled observations to train the KNN classifier on. The dataset is expected to be pre-processed in the same way as the --input query dataset.
--reference_obs_target	string required	The `.obs` key containing the target labels.
--reference_obs_batch_label	string required	The .obs field in the reference dataset containing the batch labels.
--reference_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.
--reference_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 query data.
--reference_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 query data.
--unlabeled_category	string	Value in the --reference_obs_batch_label field that indicates unlabeled observations
--reference_var_hvg	string	.var column containing highly variable genes. If not provided, genes will not be subset.
--reference_var_gene_names	string	.var column containing gene names. By default, use the index.

Name

Type & Properties

--reference

file

required

--reference_obs_target

string

required

--reference_obs_batch_label

string

required

--reference_obs_size_factor

string

--reference_obs_categorical_covariate

string

multiple

--reference_obs_continuous_covariate

string

multiple

--unlabeled_category

string

--reference_var_hvg

string

--reference_var_gene_names

string

scVI, scANVI and scArches training options

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

Leiden clustering options

Name	Type & Properties	Description
--leiden_resolution	double multiple	Control the coarseness of the clustering. Higher values lead to more clusters.

Name

Type & Properties

--leiden_resolution

double

multiple

Neighbor classifier arguments

Name	Type & Properties	Description
--knn_weights	string	Weight function used in prediction. Possible values are: `uniform` (all points in each neighborhood are weighted equally) or `distance` (weight points by the inverse of their distance)
--knn_n_neighbors	integer	The number of neighbors to use in k-neighbor graph structure used for fast approximate nearest neighbor search with PyNNDescent. Larger values will result in more accurate search results at the cost of computation time.

Name

Type & Properties

--knn_weights

string

--knn_n_neighbors

integer

Outputs

Name	Type & Properties	Description
--output	file required output	The query data in .h5mu format with predicted labels predicted from the classifier trained on the reference.
--output_obs_predictions	string	In which .obs slot to store the predicted labels.
--output_obs_probability	string	In which. obs slot to store the probabilities of the predicted labels.
--output_obsm_integrated	string	In which .obsm slot to store the integrated embedding.
--output_compression	string	The compression format to be used on the output h5mu object.
--output_model	file output	Path to the resulting scANVI model that was updated with query data.

Name

Type & Properties

--output

file

required

output

--output_obs_predictions

string

--output_obs_probability

string

--output_obsm_integrated

string

--output_compression

string

--output_model

file

output