labels_transfer/knn

Description

This component performs label transfer from reference to query using a K-Neirest Neighbors classifier.

Type

python_script

License

MIT

Links

Repository

Contributors

Launch

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
modality: [ "rna" ]  
reference_obs_targets:  
  [  
    "ann_level_1",  
    "ann_level_2",  
    "ann_level_3",  
    "ann_level_4",  
    "ann_level_5",  
    "ann_finest_level"  
  ]  
output: "$id.$key.output"  
weights: [ "uniform" ]  
n_neighbors: [ 15 ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v4.0.0 \  
  -main-script target/nextflow/labels_transfer/knn/main.nf \  
  -params-file params.yaml

Input dataset (query) arguments

Name	Type & Properties	Description
--input	file required	The query data to transfer the labels to. Should be a .h5mu file.
--modality	string	Which modality to use.
--input_obsm_features	string	The `.obsm` key of the embedding to use for the classifier's inference. If not provided, the `.X` slot will be used instead. Make sure that embedding was obtained in the same way as the reference embedding (e.g. by the same model or preprocessing).

Name

Type & Properties

--input

file

required

--modality

string

--input_obsm_features

string

Reference dataset arguments

Name	Type & Properties	Description
--reference	file	The reference data to train classifiers on.
--reference_obsm_features	string	The `.obsm` key of the embedding to use for the classifier's training. If not provided, the `.X` slot will be used instead. Make sure that embedding was obtained in the same way as the query embedding (e.g. by the same model or preprocessing).
--reference_obs_targets	string multiple	The `.obs` key(s) of the target labels to tranfer.

Name

Type & Properties

--reference

file

--reference_obsm_features

string

--reference_obs_targets

string

multiple

Outputs

Name	Type & Properties	Description
--output	file required output	The query data in .h5mu format with predicted labels transfered from 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_compression	string	Compression format to use for the output AnnData and/or Mudata objects. By default no compression is applied.

Name

Type & Properties

--output

file

required

output

--output_obs_predictions

string

multiple

--output_obs_probability

string

multiple

--output_compression

string

Input dataset (query) arguments

Name	Type & Properties	Description
--input_obsm_distances	string	The `.obsm` key of the input (query) dataset containing pre-calculated distances. If not provided, the distances will be calculated using PyNNDescent. Make sure the distance matrix contains distances relative to the reference dataset and were obtained in the same way as the reference embedding.

Name

Type & Properties

--input_obsm_distances

string

Reference dataset arguments

Name	Type & Properties	Description
--reference_obsm_distances	string	The `.obsm` key of the reference dataset containing pre-calculated distances. If not provided, the distances will be calculated using PyNNDescent.

Name

Type & Properties

--reference_obsm_distances

string

KNN label transfer arguments

Name	Type & Properties	Description
--weights	string	Weight function used in prediction. Possible values are: - `uniform` - all points in each neighborhood are weighted equally - `distance` - weight points by the inverse of their distance - `gaussian` - weight points by the sum of their Gaussian kernel similarities to each sample
--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