integrate/totalvi

Description

Performs TotalVI integration of CITE-seq and scRNA-seq data.

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'  
rna_modality: [ "rna" ]  
prot_modality: [ "prot" ]  
obs_batch: [ "sample_id" ]  
n_obs_min_count: [ 0 ]  
n_var_gene_min_count: [ 0 ]  
n_var_protein_min_count: [ 0 ]  
n_dimensions_latent_space: [ 20 ]  
gene_dispersion: [ "gene" ]  
protein_dispersion: [ "protein" ]  
gene_likelihood: [ "nb" ]  
latent_distribution: [ "normal" ]  
early_stopping: [ true ]  
output: "$id.$key.output"  
output_model: "$id.$key.output_model"  
obsm_output: [ "X_integrated_totalvi" ]  
obsm_normalized_rna_output: [ "X_totalvi_normalized_rna" ]  
obsm_normalized_protein_output: [ "X_totalvi_normalized_protein" ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v4.0.2 \  
  -main-script target/nextflow/integrate/totalvi/main.nf \  
  -params-file params.yaml

Inputs

Name	Type & Properties	Description
--input -i	file required	Input h5mu file to be integrated.
--rna_modality	string required
--prot_modality	string required	Name of the modality in the input (query) h5mu file containing protein data
--input_layer_rna	string	Input layer to use from the rna modality for gene counts. If None, X is used
--input_layer_protein	string	Input layer to use from the protein modality for protein counts. If None, X is used
--obs_batch	string	Column name discriminating between your batches.
--obs_size_factor	string	Key in adata.obs for size factor information. Instead of using library size as a size factor, the provided size factor column will be used as offset in the mean of the likelihood. Assumed to be on linear scale.
--obs_categorical_covariate	string multiple	Keys in adata.obs that correspond to categorical data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.
--obs_continuous_covariate	string multiple	Keys in adata.obs that correspond to continuous data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.
--var_gene_names	string	.var column in the rna modality containing gene names. By default, use mod['rna'].var_names.
--var_protein_names	string	.var column in the protein modality containing protein names. By default, use mod['prot'].var_names.
--var_input	string	.var column containing highly variable genes. By default, do not subset genes.

Name

Type & Properties

--input

-i

file

required

--rna_modality

string

required

--prot_modality

string

required

--input_layer_rna

string

--input_layer_protein

string

--obs_batch

string

--obs_size_factor

string

--obs_categorical_covariate

string

multiple

--obs_continuous_covariate

string

multiple

--var_gene_names

string

--var_protein_names

string

--var_input

string

Data validity checks

Name	Type & Properties	Description
--n_obs_min_count	integer	Minimum number of cells threshold ensuring that every obs_batch category has sufficient observations (cells) for model training.
--n_var_gene_min_count	integer	Minimum number of genes threshold ensuring that sufficient observations (genes) are present for model training.
--n_var_protein_min_count	integer	Minimum number of proteins threshold ensuring that sufficient observations (genes) are present for model training.

Name

Type & Properties

--n_obs_min_count

integer

--n_var_gene_min_count

integer

--n_var_protein_min_count

integer

Model arguments

Name	Type & Properties	Description
--n_dimensions_latent_space	integer	Dimensionality of the latent space.
--gene_dispersion	string	Set the behavior for the dispersion for negative binomial distributions: - gene: dispersion parameter of negative binomial is constant per gene across cells - gene-batch: dispersion can differ between different batches - gene-label: dispersion can differ between different labels - gene-cell: dispersion can differ for every gene in every cell
--protein_dispersion	string	Set the behavior for the dispersion for negative binomial distributions: - protein: dispersion parameter of negative binomial is constant per protein across cells - protein-batch: dispersion can differ between different batches - protein-label: dispersion can differ between different labels
--gene_likelihood	string	Model used to generate the expression data from a count-based likelihood distribution. - nb: Negative binomial distribution - zinb: Zero-inflated negative binomial distribution
--latent_distribution	string	Set the latent distribution to use: - normal: Normal distribution - ln: Log-normal distribution (Normal( 0, 1) transformed by softmax)
--empirical_protein_background_prior	boolean	Set the initialization of protein background prior empirically. This option fits a GMM for each of 100 cells per batch and averages the distributions. Note that even with this option set to True, this only initializes a parameter that is learned during inference. If False, randomly initializes. By default, sets this to True if greater than 10 proteins are used.
--override_missing_proteins	boolean_true	If True, will not treat proteins with all 0 expression in a particular batch as missing.

Name

Type & Properties

--n_dimensions_latent_space

integer

--gene_dispersion

string

--protein_dispersion

string

--gene_likelihood

string

--latent_distribution

string

--empirical_protein_background_prior

boolean

--override_missing_proteins

boolean_true

Training arguments

Name	Type & Properties	Description
--max_epochs	integer	Number of passes through the dataset, defaults to (20000 / number of cells) * 400 or 400; whichever is smallest.
--early_stopping	boolean	Whether to perform early stopping with respect to the validation set.

Name

Type & Properties

--max_epochs

integer

--early_stopping

boolean

Outputs

Name	Type & Properties	Description
--output -o	file required output	Output h5mu file.
--output_model	file output	Directory with the reference model. If not exists, trained model will be saved there
--obsm_output	string	In which .obsm slot to store the resulting integrated embedding.
--obsm_normalized_rna_output	string	In which .obsm slot to store the normalized RNA data from TOTALVI.
--obsm_normalized_protein_output	string	In which .obsm slot to store the normalized protein data from TOTALVI.
--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

-o

file

required

output

--output_model

file

output

--obsm_output

string

--obsm_normalized_rna_output

string

--obsm_normalized_protein_output

string

--output_compression

string