workflows/annotation/harmony_knn

Description

Cell type annotation workflow by performing harmony integration of reference and query dataset followed by KNN label transfer.

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" ]  
input_reference_gene_overlap: [ 100 ]  
n_hvg: [ 2000 ]  
harmony_theta: [ 2 ]  
leiden_resolution: [ 1 ]  
knn_weights: [ "uniform" ]  
knn_n_neighbors: [ 15 ]  
output: "$id.$key.output.h5mu"  
output_obsm_integrated: [ "X_integrated_harmony" ]  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v4.2.0 \  
  -main-script target/nextflow/workflows/annotation/harmony_knn/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.
--input_layer	string	The layer of the input dataset to process if .X is not to be used. Should contain log normalized counts.
--input_obs_batch_label	string required	The .obs field in the input (query) dataset containing the batch labels.
--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_reference_gene_overlap	integer	The minimum number of genes present in both the reference and query datasets.
--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_obs_batch_label

string

required

--input_var_gene_names

string

--input_reference_gene_overlap

integer

--overwrite_existing_key

boolean_true

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_layer	string	The layer of the reference dataset to process if .X is not to be used. Should contain log normalized counts.
--reference_obs_target	string required	The `.obs` key of the target cell type labels to transfer.
--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_layer

string

--reference_obs_target

string

required

--reference_var_gene_names

string

--reference_obs_batch_label

string

required

HVG subset arguments

Name	Type & Properties	Description
--n_hvg	integer	Number of highly variable genes to subset for.

Name

Type & Properties

--n_hvg

integer

PCA options

Name	Type & Properties	Description
--pca_num_components	integer	Number of principal components to compute. Defaults to 50, or 1 - minimum dimension size of selected representation.

Name

Type & Properties

--pca_num_components

integer

Harmony integration options

Name	Type & Properties	Description
--harmony_theta	double multiple	Diversity clustering penalty parameter. Can be set as a single value for all batch observations or as multiple values, one for each observation in the batches defined by --input_obs_batch_label. theta=0 does not encourage any diversity. Larger values of theta result in more diverse clusters."

Name

Type & Properties

--harmony_theta

double

multiple

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 annotated output .h5mu file.
--output_obs_predictions	string multiple	In which `.obs` slots to store the predicted cell labels. 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