Celltypist

Automated cell type annotation tool for scRNA-seq datasets on the basis of logistic regression classifiers optimised by the stochastic gradient descent algorithm.

Info

ID: celltypist
Namespace: annotate

Example commands

You can run the pipeline using nextflow run.

View help

You can use --help as a parameter to get an overview of the possible parameters.

nextflow run openpipelines-bio/openpipeline \
  -r 2.1.0 -latest \
  -main-script target/nextflow/annotate/celltypist/main.nf \
  --help

Run command

Example of params.yaml 
# Inputs
input: # please fill in - example: "input.h5mu"
modality: "rna"
# input_layer: "foo"
# input_var_gene_names: "foo"
input_reference_gene_overlap: 100

# Reference
# reference: "reference.h5mu"
# reference_layer: "foo"
reference_obs_target: "cell_ontology_class"
# reference_var_gene_names: "foo"
# reference_var_input: "foo"

# Model arguments
# model: "pretrained_model.pkl"
feature_selection: false
majority_voting: false
C: 1.0
max_iter: 1000
use_SGD: false
min_prop: 0.0

# Outputs
# output: "$id.$key.output.h5mu"
# output_compression: "gzip"
output_obs_predictions: "celltypist_pred"
output_obs_probability: "celltypist_probability"

# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"

# Arguments
nextflow run openpipelines-bio/openpipeline \
  -r 2.1.0 -latest \
  -profile docker \
  -main-script target/nextflow/annotate/celltypist/main.nf \
  -params-file params.yaml
Note

Replace -profile docker with -profile podman or -profile singularity depending on the desired backend.

Argument groups

Inputs

Input dataset (query) arguments

Name Description Attributes
--input The input (query) data to be labeled. Should be a .h5mu file. file, required, example: "input.h5mu"
--modality Which modality to process. string, default: "rna"
--input_layer The layer in the input data containing log normalized counts to be used for cell type annotation if .X is not to be used. string
--input_var_gene_names 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. string
--input_reference_gene_overlap The minimum number of genes present in both the reference and query datasets. integer, default: 100

Reference

Arguments related to the reference dataset.

Name Description Attributes
--reference The reference data to train the CellTypist classifiers on. Only required if a pre-trained –model is not provided. file, example: "reference.h5mu"
--reference_layer 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. string
--reference_obs_target The name of the adata obs column in the reference data containing cell type annotations. string, default: "cell_ontology_class"
--reference_var_gene_names 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. string
--reference_var_input .var column containing highly variable genes. By default, do not subset genes. string

Model arguments

Model arguments.

Name Description Attributes
--model Pretrained model in pkl format. If not provided, the model will be trained on the reference data and –reference should be provided. file, example: "pretrained_model.pkl"
--feature_selection Whether to perform feature selection. boolean, default: FALSE
--majority_voting Whether to refine the predicted labels by running the majority voting classifier after over-clustering. boolean, default: FALSE
--C Inverse of regularization strength in logistic regression. double, default: 1
--max_iter Maximum number of iterations before reaching the minimum of the cost function. integer, default: 1000
--use_SGD Whether to use the stochastic gradient descent algorithm. boolean_true
--min_prop “For the dominant cell type within a subcluster, the minimum proportion of cells required to support naming of the subcluster by this cell type. Ignored if majority_voting is set to False. Subcluster that fails to pass this proportion threshold will be assigned ‘Heterogeneous’.” double, default: 0

Outputs

Output arguments.

Name Description Attributes
--output Output h5mu file. file, example: "output.h5mu"
--output_compression string, example: "gzip"
--output_obs_predictions In which .obs slots to store the predicted information. string, default: "celltypist_pred"
--output_obs_probability In which .obs slots to store the probability of the predictions. string, default: "celltypist_probability"

Authors

  • Jakub Majercik (author)

  • Weiwei Schultz (contributor)