workflows/annotation/scgpt_integration_knn

Description

Cell type annotation workflow that performs scGPT integration of reference and query dataset followed by KNN label transfer.

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.
--input_layer	string	Mudata layer (key from layers) to use as input data for scGPT integration; if not specified, X is used. Should match the layer name of the reference dataset.
--input_var_gene_names	string	The .var field in the input (query) dataset containing gene names; if not provided, the .var index will be used.
--input_obs_batch_label	string required	The .obs field in the input (query) dataset containing the batch labels.
--overwrite_existing_key	boolean_true	If provided, will overwrite existing fields in the input dataset when data are copied during the reference alignment process.

Name

Type & Properties

--id

string

required

--input

file

required

--modality

string

--input_layer

string

--input_var_gene_names

string

--input_obs_batch_label

string

required

--overwrite_existing_key

boolean_true

Reference input

Name	Type & Properties	Description
--reference	file required	Reference dataset consisting of observations with cell type labels present in the .obs --reference_obs_batch_label column to train the classifier on. The dataset is expected to be pre-processed in the same way as the --input query dataset(s).
--reference_obs_targets	string required multiple	The `.obs` key(s) of the target labels to tranfer.
--reference_var_gene_names	string	The .var field in the reference dataset containing gene names; if not provided, the .var index will be used.
--reference_obs_batch_label	string required	The .obs field in the reference dataset containing the batch labels.

Name

Type & Properties

--reference

file

required

--reference_obs_targets

string

required

multiple

--reference_var_gene_names

string

--reference_obs_batch_label

string

required

scGPT model

Name	Type & Properties	Description
--model	file required	The scGPT model file. Can either be a foundation model or a fine-tuned model. If the model file is a fine-tuned model, it must contain a key for the checkpoints (--finetuned_checkpoints_key).
--model_vocab	file required	The scGPT model vocabulary file.
--model_config	file required	The scGPT model configuration file.
--finetuned_checkpoints_key	string	Key in the model file containing the pretrained checkpoints. Must be provided when `--model` is a fine-tuned model.

Name

Type & Properties

--model

file

required

--model_vocab

file

required

--model_config

file

required

--finetuned_checkpoints_key

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.

Name

Type & Properties

--n_hvg

integer

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

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
--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)
--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

--weights

string

--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 multiple	In which `.obs` slots to store the predicted information. If provided, must have the same length as `--reference_obs_targets`. If empty, will default to the `reference_obs_targets` combined with the `"_pred"` suffix.
--output_obs_probability	string multiple	In which `.obs` slots to store the probability of the predictions. If provided, must have the same length as `--reference_obs_targets`. If empty, will default to the `reference_obs_targets` combined with the `"_probability"` suffix.
--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.

Name

Type & Properties

--output

file

required

output

--output_obs_predictions

string

multiple

--output_obs_probability

string

multiple

--output_obsm_integrated

string

--output_compression

string

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
id: "run"  
modality: [ "rna" ]  
pad_token: [ "<pad>" ]  
pad_value: [ -2 ]  
n_hvg: [ 1200 ]  
dsbn: [ true ]  
batch_size: [ 64 ]  
n_input_bins: [ 51 ]  
leiden_resolution: [ 1 ]  
weights: [ "uniform" ]  
n_neighbors: [ 15 ]  
output: "$id.$key.output.h5mu"  
output_obsm_integrated: [ "X_integrated_scgpt" ]  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision 2.0.0 \  
  -main-script target/nextflow/workflows/annotation/scgpt_integration_knn/main.nf \  
  -params-file params.yaml

workflows/annotation/scgpt_integration_knn

Query Input

Reference input

scGPT model

Padding arguments

HVG subset arguments

Tokenization arguments

Embedding arguments

Binning arguments

Leiden clustering options

Neighbor classifier arguments

Outputs

Run this component

Relationships

Used by

Uses