single_cell/process_integrate_annotate

Description

A pipeline to process, integrate and annotate single cell (multi-)omics data.
Available integration methods:

Harmony
scVI
Available annotation methods:
CellTypist
scANVI (with scArches)

Type

nextflow_script

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 ]  
reference_layer_lognormalized_counts: [ "log_normalized" ]  
reference_obs_label_unlabeled_category: [ "Unkown" ]  
n_hvg: [ 2000 ]  
mitochondrial_gene_regex: [ "^[mM][tT]-" ]  
ribosomal_gene_regex: [ "^[Mm]?[Rr][Pp][LlSs]" ]  
harmony_theta: [ 2 ]  
harmony_obs_covariates: [ "sample_id" ]  
early_stopping_monitor: [ "elbo_validation" ]  
early_stopping_patience: [ 45 ]  
early_stopping_min_delta: [ 0 ]  
reduce_lr_on_plateau: [ true ]  
lr_factor: [ 0.6 ]  
lr_patience: [ 30 ]  
celltypist_feature_selection: [ false ]  
celltypist_majority_voting: [ false ]  
celltypist_C: [ 1 ]  
celltypist_max_iter: [ 1000 ]  
celltypist_min_prop: [ 0 ]  
leiden_resolution: [ 1 ]  
knn_weights: [ "uniform" ]  
knn_n_neighbors: [ 15 ]  
output: "$id.$key.output.h5mu"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline_composed.git \  
  -revision v0.1.1 \  
  -main-script target/nextflow/single_cell/process_integrate_annotate/main.nf \  
  -params-file params.yaml

Input (query) data arguments

Name	Type & Properties	Description
--id	string required	ID of the sample.
--input	file required	Input query dataset(s) to be annotated
--modality	string	Modality to be processed. Should match the modality in the --reference dataset, if provided.
--input_layer	string	The layer in the input data containing the raw counts, if .X is not to be used.
--input_var_gene_names	string	The name of the adata var column 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

--id

string

required

--input

file

required

--modality

string

--input_layer

string

--input_var_gene_names

string

--input_reference_gene_overlap

integer

Reference data arguments

Name	Type & Properties	Description
--reference	file	The reference dataset in .h5mu format to be used as a reference mapper and to train annotation algorithms on.
--reference_layer_raw_counts	string	The layer in the reference dataset containing the raw counts, if .X is not to be used.
--reference_layer_lognormalized_counts	string	The layer in the reference dataset containing the log-normalized counts, if .X is not to be used.
--reference_var_gene_names	string	The name of the adata .var column containing gene names if the .var index is not to be used.
--reference_obs_batch	string	The .obs column of the reference dataset containing the batch information.
--reference_obs_label	string	The `.obs` key of the target labels to tranfer.
--reference_obs_label_unlabeled_category	string	Value in the --reference_obs_label field that indicates unlabeled observations
--reference_var_input	string	.var column containing highly variable genes. By default, do not subset genes.

Name

Type & Properties

--reference

file

--reference_layer_raw_counts

string

--reference_layer_lognormalized_counts

string

--reference_var_gene_names

string

--reference_obs_batch

string

--reference_obs_label

string

--reference_obs_label_unlabeled_category

string

--reference_var_input

string

Methods

Name	Type & Properties	Description
--integration_methods	string multiple	Integration methods to be executed.
--annotation_methods	string multiple	Annotation methods to be executed.

Name

Type & Properties

--integration_methods

string

multiple

--annotation_methods

string

multiple

Pre-processing options: RNA filtering

Name	Type & Properties	Description
--rna_min_counts	integer	Minimum number of counts captured per cell.
--rna_max_counts	integer	Maximum number of counts captured per cell.
--rna_min_genes_per_cell	integer	Minimum of non-zero values per cell.
--rna_max_genes_per_cell	integer	Maximum of non-zero values per cell.
--rna_min_cells_per_gene	integer	Minimum of non-zero values per gene.
--rna_min_fraction_mito	double	Minimum fraction of UMIs that are mitochondrial.
--rna_max_fraction_mito	double	Maximum fraction of UMIs that are mitochondrial.

Name

Type & Properties

--rna_min_counts

integer

--rna_max_counts

integer

--rna_min_genes_per_cell

integer

--rna_max_genes_per_cell

integer

--rna_min_cells_per_gene

integer

--rna_min_fraction_mito

double

--rna_max_fraction_mito

double

Pre-processing options: Highly variable features detection

Name	Type & Properties	Description
--n_hvg	integer	Number of highly-variable features to keep. Only relevant if HVG need to be calculated across query and reference datasets (e.g. for --annotation_methods scvi_knn and harmony_knn). For reference mapping-based methods, the HVG's specified in --reference_var_input will be used.

Name

Type & Properties

--n_hvg

integer

Pre-processing options: Mitochondrial & Ribosomal Gene Detection

Name	Type & Properties	Description
--var_name_mitochondrial_genes	string	In which .var slot to store a boolean array corresponding the mitochondrial genes.
--var_name_ribosomal_genes	string	In which .var slot to store a boolean array corresponding the ribosomal genes.
--obs_name_mitochondrial_fraction	string	When specified, write the fraction of counts originating from mitochondrial genes (based on --mitochondrial_gene_regex) to an .obs column with the specified name. Requires --var_name_mitochondrial_genes.
--obs_name_ribosomal_fraction	string	When specified, write the fraction of counts originating from ribosomal genes (based on --ribosomal_gene_regex) to an .obs column with the specified name. Requires --var_name_ribosomal_genes.
--mitochondrial_gene_regex	string	Regex string that identifies mitochondrial genes from --var_gene_names. By default will detect human and mouse mitochondrial genes from a gene symbol.
--ribosomal_gene_regex	string	Regex string that identifies ribosomal genes from --var_gene_names. By default will detect human and mouse ribosomal genes from a gene symbol.

Name

Type & Properties

--var_name_mitochondrial_genes

string

--var_name_ribosomal_genes

string

--obs_name_mitochondrial_fraction

string

--obs_name_ribosomal_fraction

string

--mitochondrial_gene_regex

string

--ribosomal_gene_regex

string

Pre-processing options: QC metrics calculation options

Name	Type & Properties	Description
--var_qc_metrics	string multiple	Keys to select a boolean (containing only True or False) column from .var. For each cell, calculate the proportion of total values for genes which are labeled 'True', compared to the total sum of the values for all genes. Defaults to the combined values specified for --var_name_mitochondrial_genes and --highly_variable_features_var_output.

Name

Type & Properties

--var_qc_metrics

string

multiple

Harmony integration options

Name	Type & Properties	Description
--harmony_theta	double multiple	Diversity clustering penalty parameter. Specify for each variable in group.by.vars. theta=0 does not encourage any diversity. Larger values of theta result in more diverse clusters."
--harmony_obs_covariates	string required multiple	The .obs field(s) that define the covariate(s) to regress out.

Name

Type & Properties

--harmony_theta

double

multiple

--harmony_obs_covariates

string

required

multiple

scVI, scANVI and scArches training options

Name	Type & Properties	Description
--early_stopping	boolean	Whether to perform early stopping with respect to the validation set.
--early_stopping_monitor	string	Metric logged during validation set epoch.
--early_stopping_patience	integer	Number of validation epochs with no improvement after which training will be stopped.
--early_stopping_min_delta	double	Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.
--max_epochs	integer	Number of passes through the dataset, defaults to (20000 / number of cells) * 400 or 400; whichever is smallest.
--reduce_lr_on_plateau	boolean	Whether to monitor validation loss and reduce learning rate when validation set `lr_scheduler_metric` plateaus.
--lr_factor	double	Factor to reduce learning rate.
--lr_patience	double	Number of epochs with no improvement after which learning rate will be reduced.

Name

Type & Properties

--early_stopping

boolean

--early_stopping_monitor

string

--early_stopping_patience

integer

--early_stopping_min_delta

double

--max_epochs

integer

--reduce_lr_on_plateau

boolean

--lr_factor

double

--lr_patience

double

CellTypist reference model

Name	Type & Properties	Description
--celltypist_model	file	Pretrained model in pkl format. If not provided, the model will be trained on the reference data and --reference should be provided.

Name

Type & Properties

--celltypist_model

file

CellTypist annotation options

Name	Type & Properties	Description
--celltypist_feature_selection	boolean	Whether to perform feature selection.
--celltypist_majority_voting	boolean	Whether to refine the predicted labels by running the majority voting classifier after over-clustering.
--celltypist_C	double	Inverse of regularization strength in logistic regression.
--celltypist_max_iter	integer	Maximum number of iterations before reaching the minimum of the cost function.
--celltypist_use_SGD	boolean_true	Whether to use the stochastic gradient descent algorithm.
--celltypist_min_prop	double	"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'."

Name

Type & Properties

--celltypist_feature_selection

boolean

--celltypist_majority_voting

boolean

--celltypist_C

double

--celltypist_max_iter

integer

--celltypist_use_SGD

boolean_true

--celltypist_min_prop

double

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 output file.

Name

Type & Properties

--output

file

required

output