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nextflow_schema.json
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nextflow_schema.json
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{
"$schema": "http://json-schema.org/draft-07/schema",
"$id": "https://github.com/raw/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://github.com/raw/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": [