annotate/random_forest_annotation

Description

Automated cell type annotation tool for scRNA-seq datasets on the basis of random forest.

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" ]  
input_reference_gene_overlap: [ 100 ]  
output: "$id.$key.output.h5mu"  
output_obs_predictions: [ "random_forest_pred" ]  
output_obs_probability: [ "random_forest_probability" ]  
n_estimators: [ 100 ]  
criterion: [ "gini" ]  
class_weight: [ "balanced_subsample" ]  
max_features: [ "200" ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v3.0.0 \  
  -main-script target/nextflow/annotate/random_forest_annotation/main.nf \  
  -params-file params.yaml

Inputs

Name	Type & Properties	Description
--input	file required	The input (query) data to be labeled. Should be a .h5mu file.
--modality	string	Which modality to process.
--input_layer	string	The layer in the input data to be used for cell type annotation if .X is not to be used.
--input_var_gene_names	string	The name of the adata var column in the input data containing gene names; when no gene_name_layer is 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.

Name

Type & Properties

--input

file

required

--modality

string

--input_layer

string

--input_var_gene_names

string

--input_reference_gene_overlap

integer

Reference

Name	Type & Properties	Description
--reference	file	The reference data to train the CellTypist classifiers on. Only required if a pre-trained --model is not provided.
--reference_layer	string	The layer in the reference data to be used for cell type annotation if .X is not to be used. Data are expected to be processed in the same way as the --input query dataset.
--reference_obs_target	string required	Key in obs field of reference modality with cell-type information.
--reference_var_gene_names	string	The name of the adata var column in the reference data containing gene names; when no gene_name_layer is provided, the var index will be used.
--reference_var_input	string	.var column containing highly variable genes. By default, do not subset genes.

Name

Type & Properties

--reference

file

--reference_layer

string

--reference_obs_target

string

required

--reference_var_gene_names

string

--reference_var_input

string

Outputs

Name	Type & Properties	Description
--output	file output	Output h5mu file.
--output_obs_predictions	string	In which `.obs` slots to store the predicted information.
--output_obs_probability	string	In which `.obs` slots to store the probability of the predictions.
--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

output

--output_obs_predictions

string

--output_obs_probability

string

--output_compression

string

Model arguments

Name	Type & Properties	Description
--model	file	Pretrained model in pkl format. If not provided, the model will be trained on the reference data and --reference should be provided.
--n_estimators	integer	Number of trees in the random forest.
--max_depth	integer	Maximum depth of the trees in the random forest. If not provided, the nodes are expanded until all leaves only contain a single sample.
--criterion	string	The function to measure the quality of a split.
--class_weight	string	Weights associated with classes. The `balanced` mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data. The `balanced_subsample` mode is the same as `balanced` except that weights are computed based on the bootstrap sample for every tree grown. The `uniform` mode gives all classes a weight of one.
--max_features	string	The number of features to consider when looking for the best split. The value can either be a positive integer or one of `sqrt`, `log2` or `all`. If integer: consider max_features features at each split. If `sqrt`: max_features is the squareroot of all input features. If `log2`: max_features is the log2 of all input features. If `all`: max features equals all input features.

Name

Type & Properties

--model

file

--n_estimators

integer

--max_depth

integer

--criterion

string

--class_weight

string

--max_features

string