Define where the pipeline should find input data and save output data.

Path to comma-separated file containing information about the samples in the experiment.

type: string
pattern: ^\S+\.csv$

You will need to create a design file with information about the samples in your experiment before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row. See usage docs.

The output directory where the results will be saved. You have to use absolute paths to storage on Cloud infrastructure.

type: string

Save intermediate files to the output directory

type: boolean

Email address for completion summary.

type: string
pattern: ^([a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$

Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (~/.nextflow/config) then you don't need to specify this on the command line for every run.

MultiQC report title. Printed as page header, used for filename if not otherwise specified.

type: string

Options for converting the input data to the unified format.

Unify gene symbols to the latest version of the Ensembl database

type: boolean

Method to aggregate gene expression values for non-unique genes

type: string

Method to aggregate gene expression values for non-unique genes. Available methods are: mean, sum, max

Force keeping certain columns in the merged AnnData object, even if they are not present in all samples

type: string
pattern: ^([a-zA-Z0-9_]*(,[a-zA-Z0-9_]*)*)?$

If you want to keep certain columns in the merged AnnData object, even if they are not present in all samples, specify them here. Separate them with a comma.

Options for quality control of the input data.

Specify the tool to use for ambient RNA removal

type: string

Specify the tools to use for doublet detection. Setting to 'none' will skip this step

type: string
default: scrublet
pattern: ^(none|((solo|scrublet|doubletdetection|scds)?,?)*[^,]+$)

If you want to use multiple tools, separate them with a comma. Available methods are: solo, scrublet, doubletdetection, scds

Number of tools that need to agree on a doublet for it to be called as such

type: integer
default: 1

Number of epochs to train the CellBender model

type: integer
default: 150

Options for integration of the input data. For configuration of the scVI/scANVI models, see the scVI_options section.

Specify the tool to use for integration

type: string
default: scvi
pattern: ^((scvi|scanvi|harmony|bbknn|combat|seurat|scimilarity)(,(scvi|scanvi|harmony|bbknn|combat|seurat|scimilarity))*)?$

If you want to use multiple tools, separate them with a comma. Available methods are: scvi, scanvi, harmony, bbknn, combat, seurat

Number of highly variable genes to use for integration. If set to 0, the number of highly variable genes will be automatically determined. If set to a negative number, all genes will be used.

type: integer

If a reference model is provided, this does not have any effect. This is because the reference model defines the set of genes that will be used for integration.

Path to a pre-trained scVI model, only relevant if scVI is selected in integration_methods. If provided, the model will be used for integration. Otherwise, a new model will be trained.

type: string
pattern: ^\S+\.pt$

The file should be in the .pt format.

Path to a pre-trained scANVI model, only relevant if scANVI is selected in integration_methods. If provided, the model will be used for integration. Otherwise, a new model will be trained.

type: string
pattern: ^\S+\.pt$

The file should be in the .pt format.

Path to a pre-trained scimilarity model, only relevant if scimilarity is selected in integration_methods. If provided, the model will be used for integration. Otherwise, a new model will be trained.

type: string
default: https://zenodo.org/records/10685499/files/model_v1.1.tar.gz

The file can be a .tar.gz file or the corresponding unzipped directory. The official models are shared via Zenodo.

If you already produced an integrated AnnData object with this pipeline and want to add new data to it, you can specify the path to the base AnnData object and some information about it here. This will allow you to project the new data onto the existing integrated object.

If you want to project new data onto an already integrated object, specify the path to the base AnnData object here

type: string
pattern: ^\S+\.h5ad$

The file should be in the .h5ad format.

The column in the base AnnData object that contains the label (e.g. cell type) information.

type: string
default: label

The keys in the obsm of the base AnnData object that contain the embeddings (without leading X_). Required if input is not provided - otherwise it is ignored.

type: string
pattern: ^((scvi|scanvi|harmony|bbknn|combat|seurat)(,(scvi|scanvi|harmony|bbknn|combat|seurat))*)?$

If the input parameter is not provided (no new data to add), integration will not be performed. In order to be able to utilize existing integration results, you need to provide the keys in the obsm of the base AnnData object that contain the embeddings (without leading X_).

Options for clustering the integrated data.

Specify the resolutions for clustering

type: string
default: 0.5,1.0
pattern: ^\d+(\.\d+)?(,\d+(\.\d+)?)*$

Specify the resolutions for clustering. If you want to use multiple resolutions, separate them with a comma.

Create a UMAP and a clustering for each unique value in the label column (and for each integration method)

type: boolean

Create a global UMAP and clustering (for each integration method)

type: boolean
default: true

Options for various tools used in the pipeline.

Specify the models to use for the celltypist cell type annotation

type: string
pattern: ^([a-zA-Z0-9_]*(,[a-zA-Z0-9_]*)*)?$

If you want to use multiple models, separate them with a comma. Available models can be found here.

Options for resource allocation and CPU usage.

Scale the memory requirements for each process by this factor. Should be increased if you have a large number of cells.

type: integer
default: 1

Use GPU acceleration for tasks that support it

hidden
type: boolean

Options for selecting which tools should be used for certain tasks

Only run the preprocessing steps, skip the integration and clustering steps

type: boolean

Skip the LIANA step. For large datasets, the pipeline might fail due to high memory usage in this step. Use this option to skip it.

type: boolean

Skip the rank_genes_groups step. For large datasets, the pipeline might fail due to high memory usage in this step. Use this option to skip it.

type: boolean

Prepare the output for visualisation in cellxgene

type: boolean

Options for the scVI and scANVI integration methods

Number of latent dimensions for scVI/scANVI

type: integer
default: 30

Number of hidden units in the neural network for scVI/scANVI

type: integer
default: 128

Number of layers in the neural network for scVI/scANVI

type: integer
default: 2

Dispersion parameter for scVI/scANVI

type: string

Dispersion parameter for scVI/scANVI. Can be 'gene', 'gene-batch', 'gene-label', or 'gene-cell'.

Gene likelihood for scVI/scANVI

type: string

Gene likelihood for scVI/scANVI. Can be 'zinb', 'nb', 'poisson', or 'normal'.

Maximum number of epochs for training scVI/scANVI. If not set, a heuristic provided by scVI/scANVI will be used.

type: integer

Categorical covariates for scVI/scANVI

type: string

If you want to use multiple covariates, separate them with a comma.

Continuous covariates for scVI/scANVI

type: string

If you want to use multiple covariates, separate them with a comma.

Parameters used to describe centralised config profiles. These should not be edited.

Git commit id for Institutional configs.

hidden
type: string
default: master

Base directory for Institutional configs.

hidden
type: string
default: https://raw.githubusercontent.com/nf-core/configs/master

If you're running offline, Nextflow will not be able to fetch the institutional config files from the internet. If you don't need them, then this is not a problem. If you do need them, you should download the files from the repo and tell Nextflow where to find them with this parameter.

Institutional config name.

hidden
type: string

Institutional config description.

hidden
type: string

Institutional config contact information.

hidden
type: string

Institutional config URL link.

hidden
type: string

Less common options for the pipeline, typically set in a config file.

Display version and exit.

hidden
type: boolean

Method used to save pipeline results to output directory.

hidden
type: string

The Nextflow publishDir option specifies which intermediate files should be saved to the output directory. This option tells the pipeline what method should be used to move these files. See Nextflow docs for details.

Email address for completion summary, only when pipeline fails.

hidden
type: string
pattern: ^([a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$

An email address to send a summary email to when the pipeline is completed - ONLY sent if the pipeline does not exit successfully.

Send plain-text email instead of HTML.

hidden
type: boolean

File size limit when attaching MultiQC reports to summary emails.

hidden
type: string
default: 25.MB
pattern: ^\d+(\.\d+)?\.?\s*(K|M|G|T)?B$

Do not use coloured log outputs.

hidden
type: boolean

Incoming hook URL for messaging service

hidden
type: string

Incoming hook URL for messaging service. Currently, MS Teams and Slack are supported.

Custom config file to supply to MultiQC.

hidden
type: string

Custom logo file to supply to MultiQC. File name must also be set in the MultiQC config file

hidden
type: string

Custom MultiQC yaml file containing HTML including a methods description.

type: string

Boolean whether to validate parameters against the schema at runtime

hidden
type: boolean
default: true

Base URL or local path to location of pipeline test dataset files

hidden
type: string
default: https://raw.githubusercontent.com/nf-core/test-datasets/

Suffix to add to the trace report filename. Default is the date and time in the format yyyy-MM-dd_HH-mm-ss.

hidden
type: string