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" ]  
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 v3.0.0 \  
  -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_var_gene_names	string	.var column containing gene names. By default, use the index.

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_var_gene_names

string

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.
--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

--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