Scarches
Performs reference mapping with scArches
Info
ID: scarches
Namespace: integrate
Links
Example commands
You can run the pipeline using nextflow run
.
View help
You can use --help
as a parameter to get an overview of the possible parameters.
nextflow run openpipelines-bio/openpipeline \
-r 1.0.2 -latest \
-main-script target/nextflow/integrate/scarches/main.nf \
--help
Run command
Example of params.yaml
# Inputs
input: # please fill in - example: "path/to/file"
modality: "rna"
reference: # please fill in - example: "path/to/file"
dataset_name: "test_dataset"
# Outputs
# output: "$id.$key.output.output"
# output_compression: "gzip"
# model_output: "$id.$key.model_output.model_output"
obsm_output: "X_integrated_scanvi"
# Early stopping arguments
# early_stopping: true
early_stopping_monitor: "elbo_validation"
early_stopping_patience: 45
early_stopping_min_delta: 0.0
# Learning parameters
max_epochs: # please fill in - example: 123
reduce_lr_on_plateau: true
lr_factor: 0.6
lr_patience: 30
# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"
nextflow run openpipelines-bio/openpipeline \
-r 1.0.2 -latest \
-profile docker \
-main-script target/nextflow/integrate/scarches/main.nf \
-params-file params.yaml
Note
Replace -profile docker
with -profile podman
or -profile singularity
depending on the desired backend.
Argument groups
Inputs
Name | Description | Attributes |
---|---|---|
--input |
Input h5mu file to use as a query | file , required |
--modality |
string , default: "rna" |
|
--reference |
Path to the directory with reference model or a web link. For HLCA use https://zenodo.org/record/6337966/files/HLCA_reference_model.zip | file , required |
--dataset_name |
Name of query dataset to use as a batch name. If not set, name of the input file is used | string , default: "test_dataset" |
Outputs
Name | Description | Attributes |
---|---|---|
--output |
Output h5mu file. | file , required |
--output_compression |
The compression format to be used on the output h5mu object. | string , example: "gzip" |
--model_output |
Output directory for model | file , default: "model" |
--obsm_output |
In which .obsm slot to store the resulting integrated embedding. | string , default: "X_integrated_scanvi" |
Early stopping arguments
Name | Description | Attributes |
---|---|---|
--early_stopping |
Whether to perform early stopping with respect to the validation set. | boolean |
--early_stopping_monitor |
Metric logged during validation set epoch. | string , default: "elbo_validation" |
--early_stopping_patience |
Number of validation epochs with no improvement after which training will be stopped. | integer , default: 45 |
--early_stopping_min_delta |
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. | double , default: 0 |
Learning parameters
Name | Description | Attributes |
---|---|---|
--max_epochs |
Number of passes through the dataset, defaults to (20000 / number of cells) * 400 or 400; whichever is smallest. | integer , required |
--reduce_lr_on_plateau |
Whether to monitor validation loss and reduce learning rate when validation set lr_scheduler_metric plateaus. |
boolean , default: TRUE |
--lr_factor |
Factor to reduce learning rate. | double , default: 0.6 |
--lr_patience |
Number of epochs with no improvement after which learning rate will be reduced. | double , default: 30 |