workflows/integration/scgpt_leiden

Description

Run scGPT integration (cell embedding generation) followed by neighbour calculations, leiden clustering and run umap on the result.

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" ]  
output: "$id.$key.output.h5mu"  
obsm_integrated: [ "X_scgpt" ]  
pad_token: [ "<pad>" ]  
pad_value: [ -2 ]  
n_hvg: [ 1200 ]  
hvg_flavor: [ "cell_ranger" ]  
dsbn: [ true ]  
batch_size: [ 64 ]  
n_input_bins: [ 51 ]  
leiden_resolution: [ 1 ]  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v3.0.2 \  
  -main-script target/nextflow/workflows/integration/scgpt_leiden/main.nf \  
  -params-file params.yaml

Inputs

Name	Type & Properties	Description
--id	string required	ID of the sample.
--input	file required	Path to the input file.
--modality	string	Which modality from the input MuData file to process.
--input_layer	string	The layer of the input dataset to process if .X is not to be used. Should contain log normalized counts.
--var_gene_names	string	The name of the adata var column containing gene names; when no gene_name_layer is provided, the var index will be used.
--obs_batch_label	string	The name of the adata obs column containing the batch labels.

Name

Type & Properties

--id

string

required

--input

file

required

--modality

string

--input_layer

string

--var_gene_names

string

--obs_batch_label

string

Model

Name	Type & Properties	Description
--model	file required	Path to scGPT model file.
--model_vocab	file required	Path to scGPT model vocabulary file.
--model_config	file required	Path to scGPT model config file.
--finetuned_checkpoints_key	string	Key in the model file containing the pretrained checkpoints. Only relevant for fine-tuned models.

Name

Type & Properties

--model

file

required

--model_vocab

file

required

--model_config

file

required

--finetuned_checkpoints_key

string

Outputs

Name	Type & Properties	Description
--output	file required output	Output file path
--obsm_integrated	string	In which .obsm slot to store the resulting integrated embedding.

Name

Type & Properties

--output

file

required

output

--obsm_integrated

string

Padding arguments

Name	Type & Properties	Description
--pad_token	string	Token used for padding.
--pad_value	integer	The value of the padding token.

Name

Type & Properties

--pad_token

string

--pad_value

integer

HVG subset arguments

Name	Type & Properties	Description
--n_hvg	integer	Number of highly variable genes to subset for.
--hvg_flavor	string	Method to be used for identifying highly variable genes. Note that the default for this workflow (`cell_ranger`) is not the default method for scanpy hvg detection (`seurat`).

Name

Type & Properties

--n_hvg

integer

--hvg_flavor

string

Tokenization arguments

Name	Type & Properties	Description
--max_seq_len	integer	The maximum sequence length of the tokenized data. Defaults to the number of features if not provided.

Name

Type & Properties

--max_seq_len

integer

Embedding arguments

Name	Type & Properties	Description
--dsbn	boolean	Apply domain-specific batch normalization
--batch_size	integer	The batch size to be used for embedding inference.

Name

Type & Properties

--dsbn

boolean

--batch_size

integer

Binning arguments

Name	Type & Properties	Description
--n_input_bins	integer	The number of bins to discretize the data into; When no value is provided, data won't be binned.
--seed	integer	Seed for random number generation used for binning. If not set, no seed is used.

Name

Type & Properties

--n_input_bins

integer

--seed

integer

Clustering arguments

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