nextflow_schema.json

{
    "$schema": "http://json-schema.org/draft-07/schema",
    "$id": "https://raw.githubusercontent.com/nf-core/scflow/master/nextflow_schema.json",
    "title": "nf-core/scflow pipeline parameters",
    "description": "Complete analysis workflow for single-cell/nuclei RNA-sequencing data.",
    "type": "object",
    "definitions": {
        "input_output_options": {
            "title": "Input/output options",
            "type": "object",
            "fa_icon": "fas fa-terminal",
            "description": "Define where the pipeline should find input data and save output data.",
            "properties": {
                "manifest": {
                    "type": "string",
                    "default": "./refs/Manifest.txt",
                    "fa_icon": "fas fa-table",
                    "description": "The .tsv file specifying sample matrix filepaths."
                },
                "input": {
                    "type": "string",
                    "default": "./refs/SampleSheet.tsv",
                    "fa_icon": "fas fa-table",
                    "description": "The .tsv file specifying sample metadata."
                },
                "ensembl_mappings": {
                    "type": "string",
                    "default": "https://raw.githubusercontent.com/nf-core/test-datasets/scflow/assets/ensembl_mappings.tsv",
                    "fa_icon": "fas fa-table",
                    "description": "Optional tsv file containing mappings between ensembl_gene_id's and gene_names's"
                },
                "ctd_path": {
                    "type": "string",
                    "default": "https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/28033407/ctd_v1.zip",
                    "description": "Cell-type annotations reference file path",
                    "help_text": "This is a zip file containing cell-type annotation reference files for the EWCE package.",
                    "fa_icon": "fas fa-file-archive"
                },
                "celltype_mappings": {
                    "type": "string",
                    "default": "./conf/celltype_mappings.tsv",
                    "fa_icon": "fas fa-table",
                    "description": "Optional tsv file specifying manual revisions of cell-type annotations."
                },
                "reddim_genes_yml": {
                    "type": "string",
                    "default": "./conf/reddim_genes.yml",
                    "fa_icon": "fas fa-list",
                    "description": "Optional list of genes of interest in YML format for plotting of gene expression."
                },
                "species": {
                    "type": "string",
                    "default": "human",
                    "fa_icon": "fas fa-male",
                    "description": "Input sample species.",
                    "help_text": "Currently, \"human\" and \"mouse\" are supported."
                },
                "outdir": {
                    "type": "string",
                    "default": "./results",
                    "description": "Outputs directory.",
                    "fa_icon": "fas fa-folder-open"
                }
            },
            "required": [
                "manifest",
                "input",
                "ensembl_mappings",
                "ctd_path",
                "species"
            ],
            "help_text": ""
        },
        "quality_control": {
            "title": "Quality-Control",
            "type": "object",
            "description": "Parameters for quality-control and thresholding.",
            "default": "",
            "properties": {
                "qc_key_colname": {
                    "type": "string",
                    "default": "manifest",
                    "description": "The sample sheet column name with unique sample identifiers.",
                    "fa_icon": "fas fa-user-tag"
                },
                "qc_factor_vars": {
                    "type": "string",
                    "default": "seqdate",
                    "description": "The sample sheet variables to treat as factors.",
                    "help_text": "All sample sheet columns with numbers which should be treated as factors should be specified here separated by commas.   Examples include columns with dates, numeric sample identifiers, etc.",
                    "fa_icon": "fas fa-layer-group"
                },
                "qc_min_library_size": {
                    "type": "integer",
                    "default": 250,
                    "description": "Minimum library size (counts) per cell.",
                    "fa_icon": "fas fa-greater-than-equal"
                },
                "qc_max_library_size": {
                    "type": "string",
                    "default": "adaptive",
                    "description": "Maximum library size (counts) per cell.",
                    "fa_icon": "fas fa-less-than-equal"
                },
                "qc_min_features": {
                    "type": "integer",
                    "default": 100,
                    "description": "Minimum features (expressive genes) per cell.",
                    "fa_icon": "fas fa-greater-than-equal"
                },
                "qc_max_features": {
                    "type": "string",
                    "default": "adaptive",
                    "description": "Maximum features (expressive genes) per cell.",
                    "fa_icon": "fas fa-less-than-equal"
                },
                "qc_min_ribo": {
                    "type": "number",
                    "description": "Minimum proportion of counts mapping to ribosomal genes.",
                    "fa_icon": "fas fa-greater-than-equal",
                    "minimum": 0,
                    "maximum": 1
                },
                "qc_max_ribo": {
                    "type": "number",
                    "default": 1,
                    "description": "Maximum proportion of counts mapping to ribosomal genes.",
                    "fa_icon": "fas fa-less-than-equal",
                    "minimum": 0,
                    "maximum": 1
                },
                "qc_max_mito": {
                    "type": "string",
                    "default": "adaptive",
                    "description": "Maximum proportion of counts mapping to mitochondrial genes.",
                    "fa_icon": "fas fa-less-than-equal"
                },
                "qc_min_counts": {
                    "type": "integer",
                    "default": 2,
                    "description": "Minimum counts for gene expressivity.",
                    "help_text": "Expressive genes must have >=min_counts in >=min_cells",
                    "fa_icon": "fas fa-greater-than-equal"
                },
                "qc_min_cells": {
                    "type": "integer",
                    "default": 2,
                    "description": "Minimum cells for gene expressivity.",
                    "help_text": "Expressive genes must have >=min_counts in >=min_cells",
                    "fa_icon": "fas fa-greater-than-equal"
                },
                "qc_drop_unmapped": {
                    "type": "string",
                    "default": "True",
                    "description": "Option to drop unmapped genes.",
                    "fa_icon": "fas fa-cut"
                },
                "qc_drop_mito": {
                    "type": "string",
                    "default": "True",
                    "description": "Option to drop mitochondrial genes.",
                    "fa_icon": "fas fa-cut"
                },
                "qc_drop_ribo": {
                    "type": "string",
                    "description": "Option to drop ribosomal genes.",
                    "fa_icon": "fas fa-cut",
                    "default": "false"
                },
                "qc_nmads": {
                    "type": "number",
                    "default": 4,
                    "description": "The number of MADs for outlier detection.",
                    "help_text": "The number of median absolute deviations (MADs) used to define outliers for adaptive thresholding.",
                    "fa_icon": "fas fa-mountain"
                }
            },
            "fa_icon": "far fa-check-circle",
            "required": [
                "qc_key_colname",
                "qc_factor_vars",
                "qc_min_library_size",
                "qc_max_library_size",
                "qc_min_features",
                "qc_max_features",
                "qc_max_ribo",
                "qc_max_mito",
                "qc_min_counts",
                "qc_min_cells",
                "qc_drop_unmapped",
                "qc_drop_mito",
                "qc_drop_ribo",
                "qc_nmads"
            ]
        },
        "ambient_rna_profiling": {
            "title": "Ambient RNA Profiling",
            "type": "object",
            "description": "Options for profiling ambient RNA/empty droplets.",
            "default": "",
            "properties": {
                "amb_find_cells": {
                    "type": "string",
                    "default": "true",
                    "description": "Enable ambient RNA / empty droplet profiling.",
                    "fa_icon": "fas fa-cut"
                },
                "amb_retain": {
                    "type": "string",
                    "default": "auto",
                    "help_text": "A numeric scalar specifying the threshold for the total UMI count above which all barcodes are assumed to contain cells, or \"auto\" for automated estimation based on the data.",
                    "description": "Upper UMI counts threshold for true cell annotation.",
                    "pattern": "^(\\d+|auto)$",
                    "fa_icon": "fas fa-less-than-equal"
                },
                "amb_lower": {
                    "type": "integer",
                    "default": 100,
                    "help_text": "A numeric scalar specifying the lower bound on the total UMI count, at or below which all barcodes are assumed to correspond to empty droplets.",
                    "description": "Lower UMI counts threshold for empty droplet annotation.",
                    "fa_icon": "fas fa-greater-than-equal"
                },
                "amb_alpha_cutoff": {
                    "type": "number",
                    "default": 0.001,
                    "description": "The maximum FDR for the emptyDrops algorithm.",
                    "fa_icon": "fas fa-less-than-equal"
                },
                "amb_niters": {
                    "type": "integer",
                    "default": 10000,
                    "help_text": "An integer scalar specifying the number of iterations to use for the Monte Carlo p-value calculations for the emptyDrops algorithm.",
                    "description": "Number of Monte Carlo p-value iterations.",
                    "fa_icon": "fas fa-recycle"
                },
                "amb_expect_cells": {
                    "type": "integer",
                    "default": 3000,
                    "description": "Expected number of cells per sample.",
                    "help_text": "If the \"retain\" parameter is set to \"auto\" (recommended), then this parameter is used to identify the optimal value for \"retain\" for the emptyDrops algorithm.",
                    "fa_icon": "fas fa-greater-than-equal"
                }
            },
            "fa_icon": "far fa-chart-bar",
            "required": [
                "amb_find_cells",
                "amb_retain",
                "amb_lower",
                "amb_alpha_cutoff",
                "amb_niters",
                "amb_expect_cells"
            ]
        },
        "multiplet_identification": {
            "title": "Multiplet Identification",
            "type": "object",
            "description": "Parameters for identifying singlets/doublets/multiplets.",
            "default": "",
            "properties": {
                "mult_find_singlets": {
                    "type": "string",
                    "default": "true",
                    "description": "Enable doublet/multiplet identification.",
                    "fa_icon": "fas fa-cut"
                },
                "mult_singlets_method": {
                    "type": "string",
                    "default": "doubletfinder",
                    "description": "Algorithm to use for doublet/multiplet identification.",
                    "fa_icon": "fas fa-toolbox"
                },
                "mult_vars_to_regress_out": {
                    "type": "string",
                    "default": "nCount_RNA,pc_mito",
                    "description": "Variables to regress out for dimensionality reduction.",
                    "fa_icon": "fas fa-layer-group"
                },
                "mult_pca_dims": {
                    "type": "integer",
                    "default": 10,
                    "description": "Number of PCA dimensions to use.",
                    "fa_icon": "fas fa-calculator"
                },
                "mult_var_features": {
                    "type": "integer",
                    "default": 2000,
                    "description": "The top n most variable features to use.",
                    "fa_icon": "fas fa-calculator"
                },
                "mult_doublet_rate": {
                    "type": "number",
                    "description": "A fixed doublet rate.",
                    "help_text": "Use a fixed default rate (e.g. 0.075 to specify that 7.5% of all cells should be marked as doublets), or set to 0 to use the \"dpk\" method (recommended).",
                    "fa_icon": "fas fa-calculator"
                },
                "mult_dpk": {
                    "type": "integer",
                    "default": 8,
                    "description": "Doublets per thousand cells increment.",
                    "help_text": "The doublets per thousand cell increment specifies the expected doublet rate based on the number of cells, i.e. with a dpk of 8 (recommended by 10X), a dataset with 1000 cells is expected to contain 8 doublets per thousand cells, a dataset with 2000 cells is expected to contain 16 doublets per thousand cells, and a dataset with 10000 cells is expected to contain 80 cells per thousand cells (or 800 doublets in total).  If the \"doublet_rate\" parameter is manually specified this recommended incremental behaviour is overridden.",
                    "minimum": 0,
                    "maximum": 1000,
                    "fa_icon": "fas fa-calculator"
                },
                "mult_pK": {
                    "type": "number",
                    "default": 0.02,
                    "description": "Specify a pK value instead of parameter sweep.",
                    "help_text": "The optimal pK value used by the doubletFinder algorithm is determined following a compute-intensive parameter sweep.  The parameter sweep can be overridden by manually specifying a pK value.",
                    "fa_icon": "fas fa-calculator"
                }
            },
            "fa_icon": "fas fa-adjust",
            "required": [
                "mult_find_singlets",
                "mult_singlets_method",
                "mult_vars_to_regress_out",
                "mult_pca_dims",
                "mult_var_features",
                "mult_dpk",
                "mult_pK"
            ]
        },
        "merge": {
            "title": "Merge",
            "type": "object",
            "description": "Parameters used in the merged quality-control report.",
            "default": "",
            "properties": {
                "merge_plot_vars": {
                    "type": "string",
                    "default": "total_features_by_counts,total_counts,pc_mito,pc_ribo",
                    "description": "Numeric variables for inter-sample metrics.",
                    "help_text": "A comma-separated list of numeric variables which differ between individual cells of each sample.  The merged sample report will include plots facilitating between-sample comparisons for each of these numeric variables.",
                    "fa_icon": "fas fa-layer-group"
                },
                "merge_facet_vars": {
                    "type": "string",
                    "default": "NULL",
                    "description": "Categorical variables for further sub-setting of plots",
                    "help_text": "A comma-separated list of categorical variables.  The merged sample report will include additional plots of sample metrics subset by each of these variables (e.g. sex, diagnosis).",
                    "fa_icon": "fas fa-layer-group"
                },
                "merge_outlier_vars": {
                    "type": "string",
                    "default": "total_features_by_counts,total_counts",
                    "description": "Numeric variables for outlier identification.",
                    "help_text": "The merged report will include tables highlighting samples that are putative outliers for each of these numeric variables.",
                    "fa_icon": "fas fa-layer-group"
                }
            },
            "fa_icon": "fas fa-object-ungroup",
            "required": [
                "merge_plot_vars",
                "merge_facet_vars",
                "merge_outlier_vars"
            ]
        },
        "integration": {
            "title": "Integration",
            "type": "object",
            "description": "Parameters for integrating datasets and batch correction.",
            "default": "",
            "properties": {
                "integ_method": {
                    "type": "string",
                    "default": "Liger",
                    "description": "Choice of integration method.",
                    "fa_icon": "fas fa-toolbox"
                },
                "integ_unique_id_var": {
                    "type": "string",
                    "default": "manifest",
                    "description": "Unique sample identifier variable.",
                    "fa_icon": "fas fa-key"
                },
                "integ_take_gene_union": {
                    "type": "string",
                    "default": "false",
                    "description": "Fill out matrices with union of genes.",
                    "help_text": "See rliger::createLiger().  Whether to fill out raw.data matrices with union of genes across all datasets (filling in 0 for missing data) (requires make.sparse = TRUE) (default FALSE).",
                    "fa_icon": "fas fa-cut"
                },
                "integ_remove_missing": {
                    "type": "string",
                    "default": "true",
                    "description": "Remove non-expressing cells/genes.",
                    "help_text": "See rliger::createLiger().  Whether to remove cells not expressing any measured genes, and genes not expressed in any cells (if take.gene.union = TRUE, removes only genes not expressed in any dataset) (default TRUE).",
                    "fa_icon": "fas fa-cut"
                },
                "integ_num_genes": {
                    "type": "integer",
                    "default": 3000,
                    "description": "Number of genes to find for each dataset.",
                    "help_text": "See rliger::selectGenes(). Number of genes to find for each dataset. Optimises the value of var.thresh for each dataset to get this number of genes.",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_combine": {
                    "type": "string",
                    "default": "union",
                    "description": "How to combine variable genes across experiments.",
                    "help_text": "See rliger::selectGenes().  Either \"union\" or \"intersection\".",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_keep_unique": {
                    "type": "string",
                    "default": "false",
                    "description": "Keep unique genes.",
                    "help_text": "See rliger::selectGenes().",
                    "fa_icon": "fas fa-cut"
                },
                "integ_capitalize": {
                    "type": "string",
                    "default": "false",
                    "description": "Capitalize gene names to match homologous genes.",
                    "help_text": "See rliger::selectGenes().",
                    "fa_icon": "fab fa-adn"
                },
                "integ_use_cols": {
                    "type": "string",
                    "default": "true",
                    "description": "Treat each column as a cell.",
                    "help_text": "See rliger::removeMissingObs().",
                    "fa_icon": "fas fa-columns"
                },
                "integ_k": {
                    "type": "integer",
                    "default": 30,
                    "description": "Inner dimension of factorization (n factors).",
                    "help_text": "See rliger::optimizeALS().  Inner dimension of factorization (number of factors). Run suggestK to determine appropriate value; a general rule of thumb is that a higher k will be needed for datasets with more sub-structure.",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_lambda": {
                    "type": "number",
                    "default": 5,
                    "description": "Regularization parameter.",
                    "help_text": "See rliger::optimizeALS(). Regularization parameter. Larger values penalize dataset-specific effects more strongly (ie. alignment should increase as lambda increases). Run suggestLambda to determine most appropriate value for balancing dataset alignment and agreement (default 5.0).",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_thresh": {
                    "type": "number",
                    "default": 0.0001,
                    "description": "Convergence threshold.",
                    "help_text": "See rliger::optimizeALS().",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_max_iters": {
                    "type": "integer",
                    "default": 100,
                    "description": "Maximum number of block coordinate descent iterations.",
                    "help_text": "See rliger::optimizeALS().",
                    "fa_icon": "fas fa-less-than-equal"
                },
                "integ_nrep": {
                    "type": "integer",
                    "default": 1,
                    "description": "Number of restarts to perform.",
                    "help_text": "See rliger::optimizeALS().",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_rand_seed": {
                    "type": "integer",
                    "default": 1,
                    "description": "Random seed for reproducible results.",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_knn_k": {
                    "type": "integer",
                    "default": 20,
                    "description": "Number of neearest neighbours for within-dataset knn graph.",
                    "help_text": "See rliger::quantile_norm().",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_k2": {
                    "type": "integer",
                    "default": 500,
                    "description": "Horizon parameter for shared nearest factor graph.",
                    "help_text": "See rliger::quantileAlignSNF().  Distances to all but the k2 nearest neighbors are set to 0 (cuts down on memory usage for very large graphs).",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_prune_thresh": {
                    "type": "number",
                    "default": 0.2,
                    "description": "Minimum allowed edge weight.",
                    "help_text": "See rliger::quantileAlignSNF().",
                    "fa_icon": "fas fa-greater-than-equal"
                },
                "integ_ref_dataset": {
                    "type": "string",
                    "default": "NULL",
                    "description": "Name of dataset to use as a reference.",
                    "help_text": "See rliger::quantile_norm().  Name of dataset to use as a \"reference\" for normalization. By default, the dataset with the largest number of cells is used.",
                    "fa_icon": "fas fa-quote-left"
                },
                "integ_min_cells": {
                    "type": "integer",
                    "default": 2,
                    "description": "Minimum number of cells to consider a cluster shared across datasets.",
                    "help_text": "See rliger::quantile_norm().",
                    "fa_icon": "fas fa-greater-than-equal"
                },
                "integ_quantiles": {
                    "type": "integer",
                    "default": 50,
                    "description": "Number of quantiles to use for normalization.",
                    "help_text": "See rliger::quantile_norm().",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_nstart": {
                    "type": "integer",
                    "default": 10,
                    "description": "Number of times to perform Louvain community detection.",
                    "help_text": "See rliger::quantileAlignSNF().  Number of times to perform Louvain community detection with different random starts (default 10).",
                    "fa_icon": "fas fa-recycle"
                },
                "integ_resolution": {
                    "type": "integer",
                    "default": 1,
                    "description": "Controls the number of communities detected.",
                    "help_text": "See rliger::quantileAlignSNF().",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_dims_use": {
                    "type": "string",
                    "default": "NULL",
                    "description": "Indices of factors to use for shared nearest factor determination.",
                    "help_text": "See rliger::quantile_norm().",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_dist_use": {
                    "type": "string",
                    "default": "CR",
                    "description": "Distance metric to use in calculating nearest neighbour.",
                    "help_text": "See rliger::quantileAlignSNF().  Default \"CR\".",
                    "fa_icon": "fas fa-digital-tachograph"
                },
                "integ_center": {
                    "type": "string",
                    "default": "false",
                    "description": "Center the data when scaling factors.",
                    "help_text": "See rliger::quantile_norm().",
                    "fa_icon": "fas fa-compress-arrows-alt"
                },
                "integ_small_clust_thresh": {
                    "type": "integer",
                    "help_text": "See rliger::quantileAlignSNF().  Extracts small clusters loading highly on single factor with fewer cells than this before regular alignment (default 0 \u2013 no small cluster extraction).",
                    "description": "Small cluster extraction cells threshold.",
                    "fa_icon": "fas fa-calculator"
                },
                "integ_categorical_covariates": {
                    "type": "string",
                    "default": "individual,diagnosis,region,sex",
                    "description": "Categorical variables for integration report metrics.",
                    "help_text": "The integration report will provide plots and integration metrics for these categorical variables.",
                    "fa_icon": "fas fa-layer-group"
                },
                "integ_input_reduced_dim": {
                    "type": "string",
                    "default": "UMAP",
                    "description": "Reduced dimension embedding for the integration report.",
                    "help_text": "The integration report will provide with and without integration plots using this embedding.",
                    "fa_icon": "fas fa-chess-board"
                }
            },
            "fa_icon": "far fa-object-group",
            "required": [
                "integ_method",
                "integ_unique_id_var",
                "integ_take_gene_union",
                "integ_remove_missing",
                "integ_num_genes",
                "integ_combine",
                "integ_keep_unique",
                "integ_capitalize",
                "integ_use_cols",
                "integ_k",
                "integ_lambda",
                "integ_thresh",
                "integ_max_iters",
                "integ_nrep",
                "integ_rand_seed",
                "integ_knn_k",
                "integ_k2",
                "integ_prune_thresh",
                "integ_ref_dataset",
                "integ_min_cells",
                "integ_quantiles",
                "integ_nstart",
                "integ_resolution",
                "integ_dims_use",
                "integ_dist_use",
                "integ_center",
                "integ_categorical_covariates",
                "integ_input_reduced_dim"
            ]
        },
        "dimensionality_reduction": {
            "title": "Dimensionality Reduction",
            "type": "object",
            "description": "Settings for dimensionality reduction algorithms.",
            "default": "",
            "properties": {
                "reddim_input_reduced_dim": {
                    "type": "string",
                    "default": "PCA,Liger",
                    "description": "Input matrix for dimension reduction.",
                    "fa_icon": "fas fa-chess-board"
                },
                "reddim_reduction_methods": {
                    "type": "string",
                    "default": "tSNE,UMAP,UMAP3D",
                    "description": "Dimension reduction outputs to generate.",
                    "help_text": "Typically 'UMAP,UMAP3D' or 'tSNE'.",
                    "fa_icon": "fas fa-toolbox"
                },
                "reddim_vars_to_regress_out": {
                    "type": "string",
                    "default": "nCount_RNA,pc_mito",
                    "description": "Variables to regress out before dimension reduction.",
                    "fa_icon": "fas fa-layer-group"
                },
                "reddim_umap_pca_dims": {
                    "type": "integer",
                    "default": 30,
                    "description": "Number of PCA dimensions.",
                    "help_text": "See uwot::umap().",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_umap_n_neighbors": {
                    "type": "integer",
                    "default": 35,
                    "description": "Number of nearest neighbours to use.",
                    "help_text": "See uwot::umap().",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_umap_n_components": {
                    "type": "integer",
                    "default": 2,
                    "description": "The dimension of the space to embed into.",
                    "help_text": "See uwot::umap(). The dimension of the space to embed into. This defaults to 2 to provide easy visualization, but can reasonably be set to any integer value in the range 2 to 100.",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_umap_init": {
                    "type": "string",
                    "default": "spectral",
                    "description": "Type of initialization for the coordinates.",
                    "help_text": "See uwot::umap().",
                    "enum": [
                        "spectral",
                        "normlaplacian",
                        "random",
                        "lvrandom",
                        "laplacian",
                        "pca",
                        "spca",
                        "agspectral"
                    ],
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_umap_metric": {
                    "type": "string",
                    "default": "euclidean",
                    "description": "Distance metric for finding nearest neighbours.",
                    "help_text": "See uwot::umap().",
                    "enum": [
                        "euclidean",
                        "cosine",
                        "manhattan",
                        "hamming",
                        "correlation",
                        "categorical"
                    ],
                    "fa_icon": "fas fa-digital-tachograph"
                },
                "reddim_umap_n_epochs": {
                    "type": "integer",
                    "default": 200,
                    "description": "Number of epochs to us during optimization of embedded coordinates.",
                    "help_text": "See uwot::umap().",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_umap_learning_rate": {
                    "type": "integer",
                    "default": 1,
                    "description": "Initial learning rate used in optimization of coordinates.",
                    "help_text": "See uwot::umap().",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_umap_min_dist": {
                    "type": "number",
                    "default": 0.4,
                    "description": "Effective minimum distance between embedded points.",
                    "help_text": "See uwot::umap().  Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. The value should be set relative to the spread value, which determines the scale at which embedded points will be spread out.",
                    "fa_icon": "fas fa-greater-than-equal"
                },
                "reddim_umap_spread": {
                    "type": "number",
                    "default": 0.85,
                    "description": "Effective scale of embedded points.",
                    "help_text": "See uwot::umap().  In combination with min_dist, this determines how clustered/clumped the embedded points are.",
                    "fa_icon": "fas fa-arrows-alt-h"
                },
                "reddim_umap_set_op_mix_ratio": {
                    "type": "number",
                    "default": 1,
                    "description": "Interpolation to combine local fuzzy sets.",
                    "help_text": "See uwot::umap().  The value of this parameter should be between 0.0 and 1.0; a value of 1.0 will use a pure fuzzy union, while 0.0 will use a pure fuzzy intersection.",
                    "minimum": 0,
                    "maximum": 1,
                    "fa_icon": "fas fa-adjust"
                },
                "reddim_umap_local_connectivity": {
                    "type": "integer",
                    "default": 1,
                    "description": "Local connectivity required.",
                    "help_text": "See uwot::umap().  The local connectivity required \u2013 i.e. the number of nearest neighbors that should be assumed to be connected at a local level. The higher this value the more connected the manifold becomes locally.",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_umap_repulsion_strength": {
                    "type": "integer",
                    "default": 1,
                    "description": "Weighting applied to negative samples in embedding optimization.",
                    "help_text": "See uwot::umap().  Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples.",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_umap_negative_sample_rate": {
                    "type": "integer",
                    "default": 5,
                    "description": "Number of negative edge samples to use per positive edge sample.",
                    "help_text": "See uwot::umap().  The number of negative edge/1-simplex samples to use per positive edge/1-simplex sample in optimizing the low dimensional embedding.",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_umap_fast_sgd": {
                    "type": "string",
                    "default": "false",
                    "description": "Use fast SGD.",
                    "help_text": "See uwot::umap().  Setting this to TRUE will speed up the stochastic optimization phase, but give a potentially less accurate embedding, and which will not be exactly reproducible even with a fixed seed. For visualization, fast_sgd = TRUE will give perfectly good results. For more generic dimensionality reduction, it's safer to leave fast_sgd = FALSE.",
                    "fa_icon": "fas fa-skiing"
                },
                "reddim_tsne_dims": {
                    "type": "integer",
                    "default": 2,
                    "description": "Output dimensionality.",
                    "help_text": "See Rtsne::Rtsne().",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_tsne_initial_dims": {
                    "type": "integer",
                    "default": 50,
                    "description": "Number of dimensions retained in the initial PCA step.",
                    "help_text": "See Rtsne::Rtsne().",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_tsne_perplexity": {
                    "type": "integer",
                    "default": 150,
                    "description": "Perplexity parameter.",
                    "help_text": "See Rtsne::Rtsne().",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_tsne_theta": {
                    "type": "number",
                    "default": 0.5,
                    "description": "Speed/accuracy trade-off.",
                    "help_text": "See Rtsne::Rtsne().  Speed/accuracy trade-off (increase for less accuracy), set to 0.0 for exact TSNE (default: 0.5).",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_tsne_stop_lying_iter": {
                    "type": "integer",
                    "default": 250,
                    "description": "Iteration after which perplexities are no longer exaggerated.",
                    "help_text": "See Rtsne::Rtsne().  Iteration after which the perplexities are no longer exaggerated (default: 250, except when Y_init is used, then 0).",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_tsne_mom_switch_iter": {
                    "type": "integer",
                    "default": 250,
                    "description": "Iteration after which the final momentum is used.",
                    "help_text": "See Rtsne::Rtsne().  Iteration after which the final momentum is used (default: 250, except when Y_init is used, then 0).",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_tsne_max_iter": {
                    "type": "integer",
                    "default": 1000,
                    "description": "Number of iterations.",
                    "help_text": "See Rtsne::Rtsne(). ",
                    "fa_icon": "fas fa-less-than-equal"
                },
                "reddim_tsne_pca_center": {
                    "type": "string",
                    "default": "true",
                    "description": "Center data before PCA.",
                    "help_text": "See Rtsne::Rtsne(). Should data be centered before pca is applied? (default: TRUE)",
                    "fa_icon": "fas fa-compress-arrows-alt"
                },
                "reddim_tsne_pca_scale": {
                    "type": "string",
                    "default": "false",
                    "description": "Scale data before PCA.",
                    "help_text": "See Rtsne::Rtsne().  Should data be scaled before pca is applied? (default: FALSE).",
                    "fa_icon": "fas fa-balance-scale"
                },
                "reddim_tsne_normalize": {
                    "type": "string",
                    "default": "true",
                    "description": "Normalize data before distance calculations.",
                    "help_text": "See Rtsne::Rtsne(). Should data be normalized internally prior to distance calculations with normalize_input? (default: TRUE)",
                    "fa_icon": "fas fa-balance-scale"
                },
                "reddim_tsne_momentum": {
                    "type": "number",
                    "default": 0.5,
                    "description": "Momentum used in the first part of optimization.",
                    "help_text": "See Rtsne::Rtsne(). ",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_tsne_final_momentum": {
                    "type": "number",
                    "default": 0.8,
                    "description": "Momentum used in the final part of optimization.",
                    "help_text": "See Rtsne::Rtsne(). ",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_tsne_eta": {
                    "type": "integer",
                    "default": 1000,
                    "description": "Learning rate.",
                    "help_text": "See Rtsne::Rtsne(). ",
                    "fa_icon": "fas fa-calculator"
                },
                "reddim_tsne_exaggeration_factor": {
                    "type": "integer",
                    "default": 12,
                    "description": "Exaggeration factor used in the first part of the optimization.",
                    "help_text": "See Rtsne::Rtsne().  Exaggeration factor used to multiply the P matrix in the first part of the optimization (default: 12.0).",
                    "fa_icon": "fas fa-calculator"
                }
            },
            "fa_icon": "fas fa-cubes",
            "required": [
                "reddim_input_reduced_dim",
                "reddim_reduction_methods",
                "reddim_vars_to_regress_out",
                "reddim_umap_pca_dims",
                "reddim_umap_n_neighbors",
                "reddim_umap_n_components",
                "reddim_umap_init",
                "reddim_umap_metric",
                "reddim_umap_n_epochs",
                "reddim_umap_learning_rate",
                "reddim_umap_min_dist",
                "reddim_umap_spread",
                "reddim_umap_set_op_mix_ratio",
                "reddim_umap_local_connectivity",
                "reddim_umap_repulsion_strength",
                "reddim_umap_negative_sample_rate",
                "reddim_umap_fast_sgd",
                "reddim_tsne_dims",
                "reddim_tsne_initial_dims",
                "reddim_tsne_perplexity",
                "reddim_tsne_theta",
                "reddim_tsne_stop_lying_iter",
                "reddim_tsne_mom_switch_iter",
                "reddim_tsne_max_iter",
                "reddim_tsne_pca_center",
                "reddim_tsne_pca_scale",
                "reddim_tsne_normalize",
                "reddim_tsne_momentum",
                "reddim_tsne_final_momentum",
                "reddim_tsne_eta",
                "reddim_tsne_exaggeration_factor"
            ]
        },
        "clustering": {
            "title": "Clustering",
            "type": "object",
            "description": "Parameters used to tune louvain/leiden clustering.",
            "default": "",
            "properties": {
                "clust_cluster_method": {
                    "type": "string",
                    "default": "leiden",
                    "description": "Clustering method.",
                    "help_text": "Specify \"leiden\" or \"louvain\".",
                    "fa_icon": "fas fa-toolbox"
                },
                "clust_reduction_method": {
                    "type": "string",
                    "default": "UMAP_Liger",
                    "description": "Reduced dimension input(s) for clustering.",
                    "help_text": "One or more of \"UMAP\", \"tSNE\", \"PCA\", \"LSI\".",
                    "fa_icon": "fas fa-chess-board"
                },
                "clust_res": {
                    "type": "number",
                    "default": 0.001,
                    "description": "The resolution of clustering.",
                    "fa_icon": "fas fa-calculator"
                },
                "clust_k": {
                    "type": "integer",
                    "default": 50,
                    "description": "Integer number of nearest neighbours for clustering.",
                    "help_text": "Integer number of nearest neighbors to use when creating the k nearest neighbor graph for Louvain/Leiden clustering. k is related to the resolution of the clustering result, a bigger k will result in lower resolution and vice versa.",
                    "fa_icon": "fas fa-calculator"
                },
                "clust_louvain_iter": {
                    "type": "integer",
                    "default": 1,
                    "description": "The number of iterations for clustering.",
                    "fa_icon": "fas fa-recycle"
                }
            },
            "fa_icon": "fas fa-braille",
            "required": [
                "clust_cluster_method",
                "clust_reduction_method",
                "clust_res",
                "clust_k",
                "clust_louvain_iter"
            ]
        },
        "cell_type_annotation": {
            "title": "Cell-type Annotation",
            "type": "object",
            "description": "Parameters used for cell-type annotation and the associated report.",
            "default": "",
            "properties": {
                "cta_clusters_colname": {
                    "type": "string",
                    "default": "clusters",
                    "description": "SingleCellExperiment clusters colData variable name.",
                    "fa_icon": "fas fa-quote-left"
                },
                "cta_cells_to_sample": {
                    "type": "integer",
                    "default": 10000,
                    "description": "Max cells to sample.",
                    "fa_icon": "fas fa-calculator"
                },
                "cta_unique_id_var": {
                    "type": "string",
                    "default": "individual",
                    "description": "A sample metadata unique sample ID.",
                    "fa_icon": "fas fa-key"
                },
                "cta_celltype_var": {
                    "type": "string",
                    "default": "cluster_celltype",
                    "description": "SingleCellExperiment cell-type colData variable name.",
                    "fa_icon": "fas fa-quote-left"
                },
                "cta_facet_vars": {
                    "type": "string",
                    "default": "manifest,diagnosis,sex,capdate,prepdate,seqdate",
                    "description": "Cell-type metrics for categorical variables.",
                    "fa_icon": "fas fa-layer-group"
                },
                "cta_metric_vars": {
                    "type": "string",
                    "default": "pc_mito,pc_ribo,total_counts,total_features_by_counts",
                    "description": "Cell-type metrics for numeric variables.",
                    "fa_icon": "fas fa-layer-group"
                },
                "cta_top_n": {
                    "type": "integer",
                    "default": 5,
                    "description": "Number of top marker genes for plot/table generation.",
                    "fa_icon": "fas fa-calculator"
                }
            },
            "fa_icon": "fas fa-brain",
            "required": [
                "cta_clusters_colname",
                "cta_cells_to_sample",
                "cta_unique_id_var",
                "cta_celltype_var",
                "cta_facet_vars",
                "cta_metric_vars",
                "cta_top_n"
            ]
        },
        "differential_gene_expression": {
            "title": "Differential Gene Expression",
            "type": "object",
            "description": "Parameters for differential gene expression.",
            "default": "",
            "properties": {
                "dge_de_method": {
                    "type": "string",
                    "default": "MASTZLM",
                    "description": "Differential gene expression method.",
                    "fa_icon": "fas fa-toolbox"
                },
                "dge_mast_method": {
                    "type": "string",
                    "default": "bayesglm",
                    "help_text": "See MAST::zlm().  Either 'glm', 'glmer' or 'bayesglm'.",
                    "description": "MAST method.",
                    "enum": [
                        "glm",
                        "glmer",
                        "bayesglm"
                    ],
                    "fa_icon": "fas fa-toolbox"
                },
                "dge_min_counts": {
                    "type": "integer",
                    "default": 1,
                    "description": "Expressive gene minimum counts.",
                    "help_text": "Only genes with at least min_counts in min_cells_pc will be tested for differential gene expression.",
                    "fa_icon": "fas fa-greater-than-equal"
                },
                "dge_min_cells_pc": {
                    "type": "number",
                    "default": 0.1,
                    "minimum": 0,
                    "maximum": 1,
                    "description": "Expressive gene minimum cells fraction.",
                    "help_text": "Only genes with at least min_counts in min_cells_pc will be tested for differential gene expression.  Default 0.1 (i.e. 10% of cells).",
                    "fa_icon": "fas fa-greater-than-equal"
                },
                "dge_rescale_numerics": {
                    "type": "string",
                    "default": "true",
                    "description": "Re-scale numeric covariates.",
                    "help_text": "Re-scaling and centring numeric covariates in a model can improve model performance.",
                    "fa_icon": "fas fa-balance-scale"
                },
                "dge_pseudobulk": {
                    "type": "string",
                    "default": "false",
                    "description": "Pseudobulked differential gene expression.",
                    "help_text": "Perform differential gene expression on a smaller matrix where counts are first summed across all cells within a sample (defined by dge_sample_var level).",
                    "fa_icon": "far fa-object-group"
                },
                "dge_celltype_var": {
                    "type": "string",
                    "default": "cluster_celltype",
                    "description": "Cell-type annotation variable name.",
                    "help_text": "Differential gene expression is performed separately for each cell-type of this colData variable.",
                    "fa_icon": "fas fa-quote-left"
                },
                "dge_sample_var": {
                    "type": "string",
                    "default": "manifest",
                    "description": "Unique sample identifier variable.",
                    "fa_icon": "fas fa-key"
                },
                "dge_dependent_var": {
                    "type": "string",
                    "default": "group",
                    "description": "Dependent variable of DGE model.",
                    "help_text": "The dependent variable may be a categorical (e.g. diagnosis) or a numeric (e.g. histopathology score) variable.",
                    "fa_icon": "fas fa-quote-left"
                },
                "dge_ref_class": {
                    "type": "string",
                    "default": "Control",
                    "help_text": "If a categorical dependent variable is specified, then the reference class of the dependent variable is specified here (e.g. 'Control').",
                    "description": "Reference class of categorical dependent variable.",
                    "fa_icon": "fas fa-quote-left"
                },
                "dge_confounding_vars": {
                    "type": "string",
                    "default": "cngeneson,seqdate,pc_mito",
                    "description": "Confounding variables.",
                    "help_text": "A comma-separated list of confounding variables to account for in the DGE model.",
                    "fa_icon": "fas fa-layer-group"
                },
                "dge_random_effects_var": {
                    "type": "string",
                    "default": "NULL",
                    "description": "Random effect confounding variable.",
                    "help_text": "If specified, the term `+ (1 | x ) +`is added to the model, where x is the specified random effects variable.",
                    "fa_icon": "fas fa-quote-left"
                },
                "dge_fc_threshold": {
                    "type": "number",
                    "default": 1.1,
                    "description": "Fold-change threshold for plotting.",
                    "help_text": "This absolute fold-change cut-off value is used in plots (e.g. volcano) and the DGE report.",
                    "fa_icon": "fas fa-calculator"
                },
                "dge_pval_cutoff": {
                    "type": "number",
                    "default": 0.05,
                    "description": "Adjusted p-value cutoff.",
                    "help_text": "The adjusted p-value cutoff value is used in plots (e.g. volcano) and the DGE report.",
                    "fa_icon": "fas fa-less-than-equal"
                },
                "dge_force_run": {
                    "type": "string",
                    "default": "false",
                    "description": "Force model fit for non-full rank.",
                    "help_text": "A non-full rank model specification will return an error; to override this to return a warning only, set to TRUE.",
                    "fa_icon": "fas fa-exclamation"
                },
                "dge_max_cores": {
                    "type": "string",
                    "default": "'null'",
                    "description": "Maximum CPU cores.",
                    "help_text": "The default value of 'null' utilizes all available CPU cores.  As each additional CPU core increases the number of genes simultaneously fit, the RAM/memory demand increases concomitantly.  Manually overriding this parameter can reduce the memory demands of parallelization across multiple cores.",
                    "fa_icon": "fas fa-microchip"
                }
            },
            "fa_icon": "fas fa-chart-bar",
            "required": [
                "dge_de_method",
                "dge_mast_method",
                "dge_min_counts",
                "dge_min_cells_pc",
                "dge_rescale_numerics",
                "dge_pseudobulk",
                "dge_celltype_var",
                "dge_sample_var",
                "dge_dependent_var",
                "dge_ref_class",
                "dge_confounding_vars",
                "dge_random_effects_var",
                "dge_fc_threshold",
                "dge_pval_cutoff",
                "dge_force_run",
                "dge_max_cores"
            ]
        },
        "impacted_pathway_analysis": {
            "title": "Impacted Pathway Analysis",
            "type": "object",
            "description": "Parameters for impacted pathway analysis of differentially expressed genes.",
            "default": "",
            "properties": {
                "ipa_enrichment_tool": {
                    "type": "string",
                    "default": "WebGestaltR",
                    "description": "Pathway enrichment tool(s) to use.",
                    "enum": [
                        "WebGestaltR",
                        "ROntoTools",
                        "enrichR"
                    ],
                    "fa_icon": "fas fa-toolbox"
                },
                "ipa_enrichment_method": {
                    "type": "string",
                    "default": "ORA",
                    "description": "Enrichment method.",
                    "fa_icon": "fas fa-layer-group"
                },
                "ipa_enrichment_database": {
                    "type": "string",
                    "default": "GO_Biological_Process",
                    "description": "Database(s) to use for enrichment.",
                    "help_text": "See scFlow::list_databases().  Name of the database(s) for enrichment. Examples include \"GO_Biological_Process\", \"GO_Cellular_Component\", \"GO_Molecular_Function\", \"KEGG\", \"Reactome\", \"Wikipathway\".",
                    "fa_icon": "fas fa-layer-group"
                }
            },
            "fa_icon": "fas fa-project-diagram",
            "required": [
                "ipa_enrichment_tool",
                "ipa_enrichment_method",
                "ipa_enrichment_database"
            ]
        },
        "dirichlet_modeling": {
            "title": "Dirichlet Modeling",
            "type": "object",
            "description": "Parameters for dirichlet modeling of relative cell-type proportions.",
            "default": "",
            "properties": {
                "dirich_unique_id_var": {
                    "type": "string",
                    "default": "individual",
                    "description": "Unique sampler identifier.",
                    "fa_icon": "fas fa-key"
                },
                "dirich_celltype_var": {
                    "type": "string",
                    "default": "cluster_celltype",
                    "description": "Cell-type annotation variable name.",
                    "fa_icon": "fas fa-quote-left"
                },
                "dirich_dependent_var": {
                    "type": "string",
                    "default": "group",
                    "description": "Dependent variable of Dirichlet model.",
                    "fa_icon": "fas fa-quote-left"
                },
                "dirich_ref_class": {
                    "type": "string",
                    "default": "Control",
                    "description": "Reference class of categorical dependent variable.",
                    "fa_icon": "fas fa-quote-left"
                },
                "dirich_var_order": {
                    "type": "string",
                    "default": "Control,Low,High",
                    "description": "Dependent variable classes order.",
                    "help_text": "For plotting and reports, the order of classes for the dependent variable can be manually specified (e.g. 'Control,Low,High').",
                    "fa_icon": "fas fa-layer-group"
                }
            },
            "fa_icon": "fas fa-chart-pie",
            "required": [
                "dirich_unique_id_var",
                "dirich_celltype_var",
                "dirich_dependent_var",
                "dirich_ref_class",
                "dirich_var_order"
            ]
        },
        "general_plotting": {
            "title": "General - Plotting",
            "type": "object",
            "description": "General parameters for plotting.",
            "default": "",
            "properties": {
                "plotreddim_reduction_methods": {
                    "type": "string",
                    "default": "UMAP_Liger",
                    "description": "Preferred embedding for plots.",
                    "fa_icon": "fas fa-hand-pointer"
                },
                "reddimplot_pointsize": {
                    "type": "number",
                    "description": "Point size for reduced dimension plots.",
                    "default": 0.1,
                    "help_text": "To improve visualization the point size should be adjusted according to the total number of cells plotted.",
                    "fa_icon": "fas fa-expand-alt"
                },
                "reddimplot_alpha": {
                    "type": "number",
                    "description": "Alpha (transparency) value for reduced dimension plots.",
                    "default": 0.2,
                    "help_text": "To improve visualization the alpha (transparency) value should be adjusted according to the total number of cells plotted.",
                    "fa_icon": "fas fa-braille"
                }
            },
            "fa_icon": "fas fa-chart-area",
            "required": [
                "plotreddim_reduction_methods",
                "reddimplot_pointsize",
                "reddimplot_alpha"
            ]
        },
        "institutional_config_options": {
            "title": "Institutional config options",
            "type": "object",
            "fa_icon": "fas fa-university",
            "description": "Parameters used to describe centralised config profiles. These should not be edited.",
            "help_text": "The centralised nf-core configuration profiles use a handful of pipeline parameters to describe themselves. This information is then printed to the Nextflow log when you run a pipeline. You should not need to change these values when you run a pipeline.",
            "properties": {
                "custom_config_version": {
                    "type": "string",
                    "description": "Git commit id for Institutional configs.",
                    "default": "master",
                    "hidden": true,
                    "fa_icon": "fas fa-users-cog"
                },
                "custom_config_base": {
                    "type": "string",
                    "description": "Base directory for Institutional configs.",
                    "default": "https://raw.githubusercontent.com/nf-core/configs/master",
                    "hidden": true,
                    "help_text": "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.",
                    "fa_icon": "fas fa-users-cog"
                },
                "hostnames": {
                    "type": "string",
                    "description": "Institutional configs hostname.",
                    "hidden": true,
                    "fa_icon": "fas fa-users-cog"
                },
                "config_profile_name": {
                    "type": "string",
                    "description": "Institutional config name.",
                    "hidden": true,
                    "fa_icon": "fas fa-users-cog"
                },
                "config_profile_description": {
                    "type": "string",
                    "description": "Institutional config description.",
                    "hidden": true,
                    "fa_icon": "fas fa-users-cog"
                },
                "config_profile_contact": {
                    "type": "string",
                    "description": "Institutional config contact information.",
                    "hidden": true,
                    "fa_icon": "fas fa-users-cog"
                },
                "config_profile_url": {
                    "type": "string",
                    "description": "Institutional config URL link.",
                    "hidden": true,
                    "fa_icon": "fas fa-users-cog"
                }
            }
        },
        "max_job_request_options": {
            "title": "Max job request options",
            "type": "object",
            "fa_icon": "fab fa-acquisitions-incorporated",
            "description": "Set the top limit for requested resources for any single job.",
            "help_text": "If you are running on a smaller system, a pipeline step requesting more resources than are available may cause the Nextflow to stop the run with an error. These options allow you to cap the maximum resources requested by any single job so that the pipeline will run on your system.\n\nNote that you can not _increase_ the resources requested by any job using these options. For that you will need your own configuration file. See [the nf-core website](https://nf-co.re/usage/configuration) for details.",
            "properties": {
                "max_cpus": {
                    "type": "integer",
                    "description": "Maximum number of CPUs that can be requested for any single job.",
                    "default": 16,
                    "fa_icon": "fas fa-microchip",
                    "hidden": true,
                    "help_text": "Use to set an upper-limit for the CPU requirement for each process. Should be an integer e.g. `--max_cpus 1`"
                },
                "max_memory": {
                    "type": "string",
                    "description": "Maximum amount of memory that can be requested for any single job.",
                    "default": "256.GB",
                    "fa_icon": "fas fa-memory",
                    "pattern": "^\\d+(\\.\\d+)?\\.?\\s*(K|M|G|T)?B$",
                    "hidden": true,
                    "help_text": "Use to set an upper-limit for the memory requirement for each process. Should be a string in the format integer-unit e.g. `--max_memory '8.GB'`"
                },
                "max_time": {
                    "type": "string",
                    "description": "Maximum amount of time that can be requested for any single job.",
                    "default": "240.h",
                    "fa_icon": "far fa-clock",
                    "pattern": "^(\\d+\\.?\\s*(s|m|h|day)\\s*)+$",
                    "hidden": true,
                    "help_text": "Use to set an upper-limit for the time requirement for each process. Should be a string in the format integer-unit e.g. `--max_time '2.h'`"
                }
            }
        },
        "generic_options": {
            "title": "Generic options",
            "type": "object",
            "fa_icon": "fas fa-file-import",
            "description": "Less common options for the pipeline, typically set in a config file.",
            "help_text": "These options are common to all nf-core pipelines and allow you to customise some of the core preferences for how the pipeline runs.\n\nTypically these options would be set in a Nextflow config file loaded for all pipeline runs, such as `~/.nextflow/config`.",
            "properties": {
                "help": {
                    "type": "boolean",
                    "description": "Display help text.",
                    "fa_icon": "fas fa-question-circle",
                    "hidden": true
                },
                "publish_dir_mode": {
                    "type": "string",
                    "default": "copy",
                    "description": "Method used to save pipeline results to output directory.",
                    "help_text": "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](https://www.nextflow.io/docs/latest/process.html#publishdir) for details.",
                    "fa_icon": "fas fa-copy",
                    "enum": [
                        "symlink",
                        "rellink",
                        "link",
                        "copy",
                        "copyNoFollow",
                        "move"
                    ],
                    "hidden": true
                },
                "email_on_fail": {
                    "type": "string",
                    "description": "Email address for completion summary, only when pipeline fails.",
                    "fa_icon": "fas fa-exclamation-triangle",
                    "pattern": "^([a-zA-Z0-9_\\-\\.]+)@([a-zA-Z0-9_\\-\\.]+)\\.([a-zA-Z]{2,5})$",
                    "help_text": "An email address to send a summary email to when the pipeline is completed - ONLY sent if the pipeline does not exit successfully.",
                    "hidden": true
                },
                "monochrome_logs": {
                    "type": "boolean",
                    "description": "Do not use coloured log outputs.",
                    "fa_icon": "fas fa-palette",
                    "hidden": true
                },
                "tracedir": {
                    "type": "string",
                    "description": "Directory to keep pipeline Nextflow logs and reports.",
                    "default": "${params.outdir}/pipeline_info",
                    "fa_icon": "fas fa-cogs",
                    "hidden": true
                },
                "validate_params": {
                    "type": "boolean",
                    "description": "Boolean whether to validate parameters against the schema at runtime",
                    "default": true,
                    "fa_icon": "fas fa-check-square",
                    "hidden": true
                },
                "show_hidden_params": {
                    "type": "boolean",
                    "fa_icon": "far fa-eye-slash",
                    "description": "Show all params when using `--help`",
                    "hidden": true,
                    "help_text": "By default, parameters set as _hidden_ in the schema are not shown on the command line when a user runs with `--help`. Specifying this option will tell the pipeline to show all parameters."
                },
                "enable_conda": {
                    "type": "boolean",
                    "description": "Run this workflow with Conda. You can also use '-profile conda' instead of providing this parameter.",
                    "hidden": true,
                    "fa_icon": "fas fa-bacon"
                },
                "singularity_pull_docker_container": {
                    "type": "boolean",
                    "description": "Instead of directly downloading Singularity images for use with Singularity, force the workflow to pull and convert Docker containers instead.",
                    "hidden": true,
                    "fa_icon": "fas fa-toolbox",
                    "help_text": "This may be useful for example if you are unable to directly pull Singularity containers to run the pipeline due to http/https proxy issues."
                },
                "email": {
                    "type": "string",
                    "description": "E-mail address for optional workflow completion notification.",
                    "hidden": true,
                    "fa_icon": "fas fa-envelope"
                },
                "plaintext_email": {
                    "type": "boolean",
                    "description": "Send plain-text email instead of HTML.",
                    "hidden": true,
                    "fa_icon": "fas fa-envelope"
                },
                "options": {
                    "type": "string",
                    "description": "NA",
                    "hidden": true,
                    "fa_icon": "fas fa-filter"
                }
            }
        }
    },
    "allOf": [
        {
            "$ref": "#/definitions/input_output_options"
        },
        {
            "$ref": "#/definitions/quality_control"
        },
        {
            "$ref": "#/definitions/ambient_rna_profiling"
        },
        {
            "$ref": "#/definitions/multiplet_identification"
        },
        {
            "$ref": "#/definitions/merge"
        },
        {
            "$ref": "#/definitions/integration"
        },
        {
            "$ref": "#/definitions/dimensionality_reduction"
        },
        {
            "$ref": "#/definitions/clustering"
        },
        {
            "$ref": "#/definitions/cell_type_annotation"
        },
        {
            "$ref": "#/definitions/differential_gene_expression"
        },
        {
            "$ref": "#/definitions/impacted_pathway_analysis"
        },
        {
            "$ref": "#/definitions/dirichlet_modeling"
        },
        {
            "$ref": "#/definitions/general_plotting"
        },
        {
            "$ref": "#/definitions/institutional_config_options"
        },
        {
            "$ref": "#/definitions/max_job_request_options"
        },
        {
            "$ref": "#/definitions/generic_options"
        }
    ]
}