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nextflow_schema.json
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{
"$schema": "http://json-schema.org/draft-07/schema",
"$id": "https://raw.githubusercontent.com/nf-core/differentialabundance/master/nextflow_schema.json",
"title": "nf-core/differentialabundance pipeline parameters",
"description": "Differential abundance analysis",
"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.",
"required": ["study_name", "study_type", "input", "contrasts", "outdir", "study_abundance_type"],
"properties": {
"study_name": {
"type": "string",
"default": "study",
"description": "A string to identify results in the output directory",
"help_text": "Also used as an identifier in some processes",
"fa_icon": "fas fa-signature"
},
"study_type": {
"type": "string",
"default": "rnaseq",
"description": "A string identifying the technology used to produce the data",
"help_text": "Currently 'rnaseq' or 'affy_array' may be specified.",
"enum": ["rnaseq", "affy_array", "maxquant", "geo_soft_file"],
"fa_icon": "far fa-keyboard"
},
"input": {
"type": "string",
"format": "file-path",
"exists": true,
"mimetype": "text/csv",
"pattern": "^\\S+\\.(csv|tsv|txt)$",
"schema": "assets/schema_input.json",
"description": "Path to comma-separated file containing information about the samples in the experiment.",
"help_text": "You will need to create a design file with information about the samples in your experiment before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row. See [usage docs](https://nf-co.re/differentialabundance/usage#samplesheet-input).",
"fa_icon": "fas fa-file-csv"
},
"contrasts": {
"type": "string",
"description": "A CSV file describing sample contrasts",
"help_text": "This file is used to define groups of samples from 'input' to compare. It must contain at least the columns 'variable', 'reference', 'target' and 'blocking', where 'variable' is a column in the input sample sheet, 'reference' and 'target' are values in that column, and blocking is a colon-separated list of additional 'blocking' variables (can be an empty string)",
"pattern": "^\\S+\\.(csv|tsv|txt)$",
"format": "file-path",
"mimetype": "text/csv",
"fa_icon": "fas fa-adjust"
},
"outdir": {
"type": "string",
"format": "directory-path",
"description": "The output directory where the results will be saved. You have to use absolute paths to storage on Cloud infrastructure.",
"fa_icon": "fas fa-folder-open"
},
"study_abundance_type": {
"type": "string",
"default": "counts",
"description": "Type of abundance measure used, platform-dependent",
"fa_icon": "fas fa-keyboard"
}
}
},
"abundance_values": {
"title": "Abundance values",
"type": "object",
"description": "Ways of providing your abundance values",
"default": "",
"properties": {
"matrix": {
"type": "string",
"description": "TSV-format abundance matrix",
"help_text": "For example an expression matrix output from the nf-core/rnaseq workflow. There must be a column in this matrix for every row in the input sample sheet.\n\nNot a required input if providing CEL files for affymetrix preprocessing.",
"format": "file-path",
"mimetype": "text/tab-separated-values",
"pattern": "^\\S+\\.(tsv|csv|txt)$",
"fa_icon": "fas fa-border-all"
},
"transcript_length_matrix": {
"type": "string",
"fa_icon": "fas fa-border-all",
"description": "(RNA-seq only): optional transcript length matrix with samples and genes as the abundance matrix",
"help_text": "if provided, this file willl be used to provide transcript lengths to DESeq2 to model length bias across samples"
},
"affy_cel_files_archive": {
"type": "string",
"default": "None",
"description": "Alternative to matrix: a compressed CEL files archive such as often found in GEO",
"fa_icon": "fas fa-file-archive",
"help_text": "Use this option to provide a raw archive of CEL files from Affymetrix arrays. Will be ignored if a matrix is specified."
},
"querygse": {
"type": "string",
"default": "None",
"description": "Use SOFT files from GEO by providing the GSE study identifier",
"fa_icon": "fas fa-keyboard",
"help_text": "Use this option to provide a GSE study identifier."
}
}
},
"observations_options": {
"title": "Observations (e.g. samples) options",
"type": "object",
"description": "",
"default": "",
"properties": {
"observations_id_col": {
"type": "string",
"default": "sample",
"description": "Column in the samples sheet to be used as the primary sample identifier",
"fa_icon": "fas fa-address-card"
},
"observations_type": {
"type": "string",
"default": "sample",
"description": "Type of observation",
"help_text": "This is used in reporting to refer to the observations. Frequently this is 'sample' (e.g. in RNA-seq experiments), but it may also be desirable to refer to 'pool', or 'individual'.",
"fa_icon": "fas fa-keyboard"
},
"observations_name_col": {
"type": "string",
"default": "sample",
"description": "Column in the sample sheet to be used as the display identifier for observations",
"fa_icon": "fas fa-file-signature"
}
},
"required": ["observations_id_col", "observations_type"],
"fa_icon": "fas fa-eye"
},
"features_options": {
"title": "Features options",
"type": "object",
"description": "Options related to features",
"default": "",
"help_text": "",
"properties": {
"features_id_col": {
"type": "string",
"default": "gene_id",
"description": "Feature ID attribute in the GTF file (e.g. the gene_id field)",
"fa_icon": "fas fa-address-card"
},
"features_name_col": {
"type": "string",
"default": "gene_name",
"description": "Feature name attribute in the GTF file (e.g. the gene symbol field)",
"fa_icon": "fas fa-signature"
},
"features_type": {
"type": "string",
"default": "gene",
"description": "Type of feature we have, often 'gene'",
"fa_icon": "fas fa-keyboard"
},
"sizefactors_from_controls": {
"type": "boolean",
"help_text": "Use supplied control features in normalistion/ scaling operations?",
"description": "When set, use the control features in scaling/ normalisation",
"fa_icon": "fas fa-expand-arrows-alt"
},
"control_features": {
"type": "string",
"description": "A text file listing technical features (e.g. spikes)",
"help_text": "One feature per row. Note that by default these features will just be stripped from matrices prior to internal processing. To actually use them in e.g. normalisation, set `--sizefactors_from_controls`",
"format": "file-path",
"mimetype": "text/plain",
"fa_icon": "fas fa-gamepad"
},
"features_metadata_cols": {
"type": "string",
"default": "gene_id,gene_name,gene_biotype",
"description": "Comma-separated string, specifies feature metadata columns to be used for exploratory analysis, platform-specific",
"fa_icon": "fas fa-columns"
},
"features": {
"type": "string",
"mimetype": "text/csv",
"pattern": "^\\S+\\.(csv|tsv|txt)$",
"description": "This parameter allows you to supply your own feature annotations. These can often be automatically derived from the GTF used upstream for RNA-seq, or from the Bioconductor annotation package (for affy arrays). ",
"help_text": "This parameter allows you to supply your own feature annotations. These can often be automatically derived from the GTF used upstream for RNA-seq, or from the Bioconductor annotation package (for affy arrays). ",
"fa_icon": "fas fa-align-justify"
},
"features_gtf_feature_type": {
"type": "string",
"default": "transcript",
"description": "Where a GTF file is supplied, which feature type to use",
"fa_icon": "fas fa-keyboard"
},
"features_gtf_table_first_field": {
"type": "string",
"default": "gene_id",
"description": "Where a GTF file is supplied, which field should go first in the converted output table",
"fa_icon": "fas fa-fast-backward"
},
"features_log2_assays": {
"type": "string",
"description": "Of which assays to compute the log2. Not necessary for maxquant data as this is controlled by the pipeline.",
"help_text": "Either comma-separated of assay positions, e.g. '[1,2,3]', or empty list '[]' to not log any assay. If not set, will guess which assays need to be logged (those with a maximum > 20)."
}
},
"required": ["features_id_col", "features_name_col", "features_type"],
"fa_icon": "fas fa-sitemap"
},
"affy_input_options": {
"title": "Affy input options",
"type": "object",
"description": "Options for processing of affy arrays with justRMA()",
"default": "",
"properties": {
"affy_file_name_col": {
"type": "string",
"default": "file",
"description": "Column of the sample sheet containing the Affymetrix CEL file name",
"fa_icon": "fas fa-columns"
},
"affy_background": {
"type": "boolean",
"default": true,
"description": "logical value. If TRUE, then background correct using RMA background correction.",
"fa_icon": "fas fa-image"
},
"affy_bgversion": {
"type": "integer",
"default": 2,
"description": "integer value indicating which RMA background to use",
"help_text": "1: use background similar to pure R rma background given in affy version 1.0 - 1.0.2\n2: use background similar to pure R rma background given in affy version 1.1 and above",
"fa_icon": "fas fa-code-branch"
},
"affy_destructive": {
"type": "boolean",
"description": "logical value. If TRUE, then works on the PM matrix in place as much as possible, good for large datasets.",
"fa_icon": "fas fa-bomb"
},
"affy_cdfname": {
"type": "string",
"default": "None",
"description": "Used to specify the name of an alternative cdf package. If set to NULL, then the usual cdf package based on Affymetrix' mappings will be used.",
"fa_icon": "fas fa-signature"
},
"affy_build_annotation": {
"type": "boolean",
"default": true,
"description": "logical value. If TRUE, a matrix of probe annotations will be derived.",
"fa_icon": "fas fa-asterisk"
},
"affy_rm_mask": {
"type": "boolean",
"description": "should the spots marked as 'MASKS' set to NA?",
"fa_icon": "fas fa-mask"
},
"affy_rm_outliers": {
"type": "boolean",
"description": "should the spots marked as 'OUTLIERS' set to NA?",
"fa_icon": "fas fa-project-diagram"
},
"affy_rm_extra": {
"type": "boolean",
"description": "if TRUE, then overrides what is in rm.mask and rm.oultiers.",
"fa_icon": "fas fa-external-link-alt"
}
},
"fa_icon": "fas fa-table"
},
"proteus_input_options": {
"title": "Proteus input options",
"type": "object",
"description": "Options for processing of proteomics MaxQuant tables with the Proteus R package",
"default": "",
"properties": {
"proteus_measurecol_prefix": {
"type": "string",
"default": "LFQ intensity ",
"description": "Prefix of the column names of the MaxQuant proteingroups table in which the intensity values are saved; the prefix has to be followed by the sample names that are also found in the samplesheet. Default: 'LFQ intensity '; take care to also consider trailing whitespace between prefix and samplenames."
},
"proteus_norm_function": {
"type": "string",
"default": "normalizeMedian",
"description": "Normalization function to use on the MaxQuant intensities.",
"help_text": "'normalizeMedian' or 'normalizeQuantiles'",
"enum": ["normalizeMedian", "normalizeQuantiles"]
},
"proteus_plotsd_method": {
"type": "string",
"default": "violin",
"description": "Which method to use for plotting sample distributions of the MaxQuant intensities; one of 'violin', 'dist', 'box'.",
"help_text": "'violin', 'dist' or 'box'",
"enum": ["violin", "dist", "box"]
},
"proteus_plotmv_loess": {
"type": "boolean",
"default": true,
"description": "Should a loess line be added to the plot of mean-variance relationship of the conditions? Default: true."
},
"proteus_palette_name": {
"type": "string",
"default": "Set1",
"help_text": "Check the content of `RColorBrewer::brewer.pal.info` from an R terminal for valid palette names.",
"description": "Valid R palette name",
"fa_icon": "fas fa-palette"
}
},
"fa_icon": "fas fa-table"
},
"filtering": {
"title": "Filtering",
"type": "object",
"description": "Options related to filtering upstream of differential analysis",
"default": "",
"properties": {
"filtering_min_abundance": {
"type": "number",
"default": 1.0,
"description": "Minimum abundance value",
"fa_icon": "fas fa-compress-alt"
},
"filtering_min_samples": {
"type": "number",
"default": 1.0,
"description": "Minimum observations that must pass the threshold to retain the row/ feature (e.g. gene).",
"fa_icon": "fas fa-compress-alt"
},
"filtering_min_proportion": {
"type": "number",
"description": "A minimum proportion of observations, given as a number between 0 and 1, that must pass the threshold. Overrides minimum_samples",
"fa_icon": "fas fa-percent"
},
"filtering_grouping_var": {
"type": "string",
"help_text": "The variable can be used to define groups and derive a minimum group size upon which to base minimum observation numbers. The rationale for this is to allow retention of features that might be present in only one group. Note that this is consciously NOT filtering with an explicit awareness of groups (\"feature must be present in all samples of group A\"), since this is known to create biases towards discovery of differential features.",
"description": "An optional grouping variable to be used to calculate a min_samples value",
"fa_icon": "fas fa-users"
}
},
"fa_icon": "fas fa-filter",
"required": ["filtering_min_abundance"]
},
"exploratory_analysis": {
"title": "Exploratory analysis",
"type": "object",
"description": "Options related to data exploration",
"default": "",
"properties": {
"exploratory_clustering_method": {
"type": "string",
"default": "ward.D2",
"description": "Clustering method used in dendrogram creation",
"fa_icon": "fab fa-blackberry"
},
"exploratory_cor_method": {
"type": "string",
"default": "spearman",
"description": "Correlation method used in dendrogram creation",
"fa_icon": "fas fa-chart-line"
},
"exploratory_n_features": {
"type": "integer",
"default": 500,
"description": "Number of features selected before certain exploratory analyses. If -1, will use all features.",
"fa_icon": "fas fa-list-ol"
},
"exploratory_whisker_distance": {
"type": "number",
"default": 1.5,
"description": "Length of the whiskers in boxplots as multiple of IQR. Defaults to 1.5.",
"fa_icon": "fas fa-chart-area"
},
"exploratory_mad_threshold": {
"type": "integer",
"default": -5,
"help_text": "MAD = median absolute deviation. A threshold on this value is used to define observations (samples) as outliers, or not, in exploratory plots. Based on the definition at https://wiki.arrayserver.com/wiki/index.php?title=CorrelationQC.pdf. ",
"description": "Threshold on MAD score for outlier identification",
"fa_icon": "fas fa-angry"
},
"exploratory_main_variable": {
"type": "string",
"default": "auto_pca",
"description": "How should the main grouping variable be selected? 'auto_pca', 'contrasts', or a valid column name from the observations table.",
"help_text": "Some plots are only generated once, with a single sample grouping, this option defines how that sample grouping is selected. It should be 'auto_pca' (variable selected from the sample sheet with the most association with the first principal component), 'contrasts' (pick the variable associated with the first contrast), or a value specifying a specific column in the observations.",
"fa_icon": "fas fa-home"
},
"exploratory_assay_names": {
"type": "string",
"default": "raw,normalised,variance_stabilised",
"hidden": true,
"description": "Specifies assay names to be used for matrices, platform-specific",
"fa_icon": "fas fa-file-signature"
},
"exploratory_final_assay": {
"type": "string",
"default": "variance_stabilised",
"hidden": true,
"description": "Specifies final assay to be used for exploratory analysis, platform-specific",
"fa_icon": "fas fa-sort-down"
},
"exploratory_palette_name": {
"type": "string",
"default": "Set1",
"fa_icon": "fas fa-palette",
"description": "Valid R palette name",
"help_text": "Check the content of `RColorBrewer::brewer.pal.info` from an R terminal for valid palette names."
}
},
"fa_icon": "fas fa-chart-area",
"required": [
"exploratory_clustering_method",
"exploratory_cor_method",
"exploratory_n_features",
"exploratory_main_variable",
"exploratory_palette_name"
]
},
"differential_analysis": {
"title": "Differential analysis",
"type": "object",
"description": "Options related to differential operations",
"default": "",
"properties": {
"differential_file_suffix": {
"type": "string",
"default": ".deseq2.results.tsv",
"description": "The suffix associated tabular differential results tables",
"fa_icon": "fas fa-signature"
},
"differential_feature_id_column": {
"type": "string",
"default": "gene_id",
"description": "The feature identifier column in differential results tables",
"fa_icon": "fas fa-address-card"
},
"differential_fc_column": {
"type": "string",
"default": "log2FoldChange",
"description": "The fold change column in differential results tables",
"fa_icon": "fas fa-adjust"
},
"differential_pval_column": {
"type": "string",
"default": "pvalue",
"description": "The p value column in differential results tables",
"fa_icon": "fas fa-columns"
},
"differential_qval_column": {
"type": "string",
"default": "padj",
"description": "The q value column in differential results tables.",
"fa_icon": "fas fa-columns"
},
"differential_min_fold_change": {
"type": "number",
"default": 2.0,
"description": "Minimum fold change used to calculate differential feature numbers",
"fa_icon": "fas fa-angle-double-down"
},
"differential_max_pval": {
"type": "number",
"default": 1.0,
"description": "Maximum p value used to calculate differential feature numbers",
"fa_icon": "fas fa-angle-double-up"
},
"differential_max_qval": {
"type": "number",
"default": 0.05,
"description": "Maximum q value used to calculate differential feature numbers",
"fa_icon": "fas fa-angle-double-up"
},
"differential_feature_name_column": {
"type": "string",
"default": "gene_name",
"description": "Where a features file (GTF) has been provided, what attributed to use to name features",
"fa_icon": "fas fa-signature"
},
"differential_foldchanges_logged": {
"type": "boolean",
"default": true,
"description": "Indicate whether or not fold changes are on the log scale (default is to assume they are)",
"fa_icon": "fas fa-calculator"
},
"differential_palette_name": {
"type": "string",
"default": "Set1",
"help_text": "Check the content of `RColorBrewer::brewer.pal.info` from an R terminal for valid palette names.",
"description": "Valid R palette name",
"fa_icon": "fas fa-palette"
},
"differential_subset_to_contrast_samples": {
"type": "boolean",
"fa_icon": "far fa-object-group",
"description": "In differential analysis (DEseq2 or Limma), subset to the contrast samples before modelling variance?"
}
},
"required": [
"differential_file_suffix",
"differential_feature_id_column",
"differential_fc_column",
"differential_qval_column",
"differential_min_fold_change",
"differential_max_pval",
"differential_max_qval",
"differential_palette_name"
],
"fa_icon": "fas fa-adjust"
},
"deseq2_specific_options_rna_seq_only": {
"title": "DESeq2 specific options (RNA-seq only)",
"type": "object",
"description": "",
"default": "",
"properties": {
"deseq2_test": {
"type": "string",
"default": "Wald",
"description": "`test` parameter passed to DESeq()",
"help_text": "either \"Wald\" or \"LRT\", which will then use either Wald significance tests (defined by nbinomWaldTest), or the likelihood ratio test on the difference in deviance between a full and reduced model formula (defined by nbinomLRT)",
"enum": ["Wald", "LRT"],
"fa_icon": "fas fa-vial"
},
"deseq2_fit_type": {
"type": "string",
"default": "parametric",
"description": "`fitType` parameter passed to DESeq()",
"help_text": "either \"parametric\", \"local\", \"mean\", or \"glmGamPoi\" for the type of fitting of dispersions to the mean intensity. See estimateDispersions for description.",
"enum": ["parametric", "local", "mean", "glmGamPoi"],
"fa_icon": "fas fa-keyboard"
},
"deseq2_sf_type": {
"type": "string",
"default": "ratio",
"description": "`sfType` parameter passed to DESeq()",
"help_text": "either \"ratio\", \"poscounts\", or \"iterate\" for the type of size factor estimation. See estimateSizeFactors for description.",
"enum": ["ratio", "poscounts", "iterate"],
"fa_icon": "fas fa-keyboard"
},
"deseq2_min_replicates_for_replace": {
"type": "integer",
"default": 7,
"help_text": "the minimum number of replicates required in order to use replaceOutliers on a sample. If there are samples with so many replicates, the model will be refit after these replacing outliers, flagged by Cook's distance. Set to Inf in order to never replace outliers. It set to Inf for fitType=\"glmGamPoi\".",
"description": "'minReplicatesForReplace' parameter passed to DESeq()",
"fa_icon": "fas fa-angle-double-down"
},
"deseq2_use_t": {
"type": "boolean",
"description": "`useT` parameter passed to DESeq2",
"help_text": "logical, passed to nbinomWaldTest, default is FALSE, where Wald statistics are assumed to follow a standard Normal",
"fa_icon": "fas fa-mug-hot"
},
"deseq2_independent_filtering": {
"type": "boolean",
"default": true,
"description": "`independentFiltering` parameter passed to results()",
"help_text": "logical, whether independent filtering should be applied automatically",
"fa_icon": "fas fa-filter"
},
"deseq2_lfc_threshold": {
"type": "integer",
"default": 0,
"description": "`lfcThreshold` parameter passed to results()",
"help_text": "a non-negative value which specifies a log2 fold change threshold. The default value is 0, corresponding to a test that the log2 fold changes are equal to zero. The user can specify the alternative hypothesis using the altHypothesis argument, which defaults to testing for log2 fold changes greater in absolute value than a given threshold. If lfcThreshold is specified, the results are for Wald tests, and LRT p-values will be overwritten.",
"fa_icon": "fas fa-adjust"
},
"deseq2_alt_hypothesis": {
"type": "string",
"default": "greaterAbs",
"description": "`altHypothesis` parameter passed to results()",
"help_text": "character which specifies the alternative hypothesis, i.e. those values of log2 fold change which the user is interested in finding. The complement of this set of values is the null hypothesis which will be tested. If the log2 fold change specified by 'name' or by contrast' is written as beta , then the possible values for 'altHypothesis' represent the following alternate hypotheses: 1) greaterAbs: |beta| > lfcThreshold , and p-values are two-tailed 2) lessAbs: |beta| < lfcThreshold , p-values are the maximum of the upper and lower tests. The Wald statistic given is positive, an SE-scaled distance from the closest boundary 3) greater: beta > lfcThreshold 4) less: beta < -lfcThreshold",
"fa_icon": "fas fa-atom"
},
"deseq2_p_adjust_method": {
"type": "string",
"default": "BH",
"description": "`pAdjustMethod` parameter passed to results()",
"help_text": "the method to use for adjusting p-values, see help in R for the p.adjust() function (via ?p.adjust). At time of writing available values were \"holm\", \"hochberg\", \"hommel\", \"bonferroni\", \"BH\", \"BY\", \"fdr\", \"none\".",
"fa_icon": "fas fa-sliders-h"
},
"deseq2_alpha": {
"type": "number",
"default": 0.1,
"description": "`alpha` parameter passed to results()",
"help_text": "the significance cutoff used for optimizing the independent filtering (by default 0.1). If the adjusted p-value cutoff (FDR) will be a value other than 0.1, alpha should be set to that value.",
"fa_icon": "fas fa-font"
},
"deseq2_minmu": {
"type": "number",
"default": 0.5,
"description": "`minmu` parameter passed to results()",
"help_text": "lower bound on the estimated count (used when calculating contrasts)",
"fa_icon": "fas fa-cat"
},
"deseq2_vs_method": {
"type": "string",
"default": "vst",
"description": "variance stabilisation method to use when making a variance stabilised matrix",
"help_text": "'rlog', 'vst' or 'rlog,vst'",
"enum": ["rlog", "vst", "rlog,vst"],
"fa_icon": "fas fa-viruses"
},
"deseq2_shrink_lfc": {
"type": "boolean",
"default": true,
"description": "Shink fold changes in results?",
"help_text": "'ashr' method is the only method currently implemented",
"fa_icon": "fas fa-microscope"
},
"deseq2_cores": {
"type": "integer",
"default": 1,
"description": "Number of cores",
"help_text": "Number of cores to use with DESeq()",
"fa_icon": "fab fa-centercode"
},
"deseq2_vs_blind": {
"type": "boolean",
"default": true,
"description": "`blind` parameter for rlog() and/ or vst()",
"help_text": "logical, whether to blind the transformation to the experimental design ",
"fa_icon": "fas fa-eye-slash"
},
"deseq2_vst_nsub": {
"type": "integer",
"default": 1000,
"description": "`nsub` parameter passed to vst()",
"help_text": "the number of genes to subset to (default 1000)",
"fa_icon": "fas fa-train"
}
},
"fa_icon": "fas fa-adjust"
},
"limma_specific_options_microarray_only": {
"title": "Limma specific options (microarray only)",
"type": "object",
"description": "",
"default": "",
"properties": {
"limma_ndups": {
"type": "number",
"fa_icon": "fas fa-clone",
"description": "passed to lmFit(), positive integer giving the number of times each distinct probe is printed on each array."
},
"limma_spacing": {
"type": "string",
"default": "None",
"fa_icon": "fas fa-people-arrows",
"description": "passed to lmFit(), positive integer giving the spacing between duplicate occurrences of the same probe, spacing=1 for consecutive rows."
},
"limma_block": {
"type": "string",
"default": "None",
"fa_icon": "fas fa-cube",
"description": "Sample sheet column to be used to derive a vector or factor specifying a blocking variable on the arrays"
},
"limma_correlation": {
"type": "string",
"default": "None",
"fa_icon": "fas fa-chart-line",
"description": "passed to lmFit(), the inter-duplicate or inter-technical replicate correlation"
},
"limma_method": {
"type": "string",
"default": "ls",
"fa_icon": "fas fa-hammer",
"description": "passed to lmFit(), the fitting method",
"enum": ["ls", "robust"],
"help_text": " \"ls\" for least squares or \"robust\" for robust regression"
},
"limma_proportion": {
"type": "number",
"default": 0.01,
"fa_icon": "fas fa-percent",
"description": "passed to eBayes(), a numeric value between 0 and 1, assumed proportion of genes which are differentially expressed"
},
"limma_trend": {
"type": "boolean",
"fa_icon": "fas fa-chart-line",
"description": "passed to eBayes(), logical, should an intensity-dependent trend be allowed for the prior variance?",
"help_text": "If FALSE then the prior variance is constant. Alternatively, trend can be a row-wise numeric vector, which will be used as the covariate for the prior variance."
},
"limma_robust": {
"type": "boolean",
"fa_icon": "fab fa-android",
"description": "passed to eBayes(), logical, should the estimation of df.prior and var.prior be robustified against outlier sample variances?"
},
"limma_stdev_coef_lim": {
"type": "string",
"default": "0.1,4",
"description": "passed to eBayes, comma separated string of two values, assumed lower and upper limits for the standard deviation of log2-fold-changes for differentially expressed genes",
"fa_icon": "fas fa-angle-double-up"
},
"limma_winsor_tail_p": {
"type": "string",
"default": "0.05,0.1",
"description": "passed to eBayes, comma separated string of length 1 or 2, giving left and right tail proportions of x to Winsorize. Used only when robust=TRUE.",
"fa_icon": "fas fa-angle-double-up"
},
"limma_lfc": {
"type": "integer",
"default": 0,
"fa_icon": "fas fa-exchange-alt",
"description": "passed to topTable(), minimum absolute log2-fold-change required",
"help_text": "topTable and topTableF include only genes with (at least one) absolute log-fold-change greater than lfc. topTreat does not remove genes but ranks genes by evidence that their log-fold-change exceeds lfc."
},
"limma_confint": {
"type": "boolean",
"fa_icon": "fas fa-temperature-low",
"description": "passed to topTable(), logical, should confidence 95% intervals be output for logFC? Alternatively, can take a numeric value between zero and one specifying the confidence level required."
},
"limma_adjust_method": {
"type": "string",
"default": "BH",
"description": "passed to topTable(), method used to adjust the p-values for multiple testing.",
"enum": ["holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"],
"fa_icon": "fas fa-hammer"
},
"limma_p_value": {
"type": "number",
"default": 1.0,
"description": "cutoff value for adjusted p-values. Only genes with lower p-values are listed.",
"fa_icon": "fas fa-angle-double-up"
}
},
"fa_icon": "fas fa-border-all"
},
"gsea": {
"title": "GSEA",
"type": "object",
"description": "",
"default": "",
"properties": {
"gsea_run": {
"type": "boolean",
"description": "Set to run GSEA to infer differential gene sets in contrasts",
"fa_icon": "fas fa-running"
},
"gsea_permute": {
"type": "string",
"default": "phenotype",
"description": "Permutation type",
"help_text": "Select the type of permutation to perform in assessing the statistical significance of the enrichment score. (See 'required fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page for more info)",
"enum": ["phenotype", "gene_set"],
"fa_icon": "fas fa-exchange-alt"
},
"gsea_nperm": {
"type": "integer",
"default": 1000,
"description": "Number of permutations",
"help_text": "Specify the number of permutations to perform in assessing the statistical significance of the enrichment score. (See 'required fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page)",
"fa_icon": "fas fa-exchange-alt"
},
"gsea_scoring_scheme": {
"type": "string",
"default": "weighted",
"description": "Enrichment statistic",
"enum": ["weighted", "weighted_p2", "weighted_p1.5", "classic"],
"help_text": "See 'basic fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page for a detailed explanation.",
"fa_icon": "fas fa-star"
},
"gsea_metric": {
"type": "string",
"default": "Signal2Noise",
"enum": ["Signal2Noise", "tTest", "Ratio_of_Classes", "Diff_of_Classes", "log2_Ratio_of_Classes"],
"description": "Metric for ranking genes",
"help_text": "See https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideTEXT.htm#_Metrics_for_Ranking for a detailed explanation.",
"fa_icon": "fas fa-temperature-high"
},
"gsea_sort": {
"type": "string",
"default": "real",
"description": "Gene list sorting mode",
"help_text": "GSEA ranks the genes in the expression dataset and then analyzes that ranked list of genes. Use this parameter to determine whether to sort the genes using the real (default) or absolute value of the ranking metric.\n\nSee 'basic fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page",
"enum": ["real", "absolute"],
"fa_icon": "fas fa-bars"
},
"gsea_order": {
"type": "string",
"default": "descending",
"description": "Gene list ordering mode",
"help_text": "GSEA ranks the genes in the expression dataset and then analyzes that ranked list of genes. Use this parameter to determine whether to sort the genes in descending (default) or ascending order. Ascending order is usually applicable when the ranking metric is a measure of nearness (how close the genes are to one another) rather than distance.\n\nSee 'basic fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page",
"enum": ["descending", "ascending"],
"fa_icon": "fas fa-sort-alpha-down"
},
"gsea_set_max": {
"type": "integer",
"default": 500,
"description": "Max size: exclude larger sets",
"help_text": "After filtering from the gene sets any gene not in the expression dataset, gene sets larger than this are excluded from the analysis.\n\nSee 'basic fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page",
"fa_icon": "fas fa-angle-double-up"
},
"gsea_set_min": {
"type": "integer",
"default": 15,
"description": "Min size: exclude smaller sets",
"help_text": "After filtering from the gene sets any gene not in the expression dataset, gene sets smaller than this are excluded from the analysis.\n\nSee 'basic fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page",
"fa_icon": "fas fa-angle-double-down"
},
"gsea_norm": {
"type": "string",
"default": "meandiv",
"description": "Normalisation mode",
"enum": ["meandiv", "none"],
"help_text": "Normalization mode. Method used to normalize the enrichment scores across analyzed gene sets: 'meandiv' (default, GSEA normalizes the enrichment scores as described in Normalized Enrichment Score (NES)) OR 'none' (GSEA does not normalize the enrichment scores).\n\nSee 'advanced fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page",
"fa_icon": "fas fa-align-justify"
},
"gsea_rnd_type": {
"type": "string",
"default": "no_balance",
"help_text": "Method used to randomly assign phenotype labels to samples for phenotype permutations. Not used for gene_set permutations.\n\nSee 'advanced fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page",
"enum": ["no_balance", "equalize_and_balance"],
"description": "Randomization mode",
"fa_icon": "fas fa-random"
},
"gsea_make_sets": {
"type": "boolean",
"default": true,
"description": "Make detailed geneset report?",
"fa_icon": "fas fa-object-ungroup"
},
"gsea_median": {
"type": "boolean",
"help_text": "Set to true (default=false) to use the median of each class, instead of the mean, in the metrics for ranking for genes\n\nSee 'advanced fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page. ",
"description": "Use median for class metrics",
"fa_icon": "fas fa-align-center"
},
"gsea_num": {
"type": "integer",
"default": 100,
"description": "Number of markers",
"help_text": "Number of features (gene or probes) to include in the butterfly plot in the Gene Markers section of the gene set enrichment report.\n\nSee 'advanced fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page. ",
"fa_icon": "fas fa-list-ol"
},
"gsea_plot_top_x": {
"type": "integer",
"default": 20,
"description": "Plot graphs for the top sets of each phenotype",
"help_text": "Generates summary plots and detailed analysis results for the top x genes in each phenotype, where x is 20 by default. The top genes are those with the largest normalized enrichment scores.\n\nSee 'advanced fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page. ",
"fa_icon": "fas fa-angle-up"
},
"gsea_rnd_seed": {
"type": "string",
"default": "timestamp",
"description": "Seed for permutation",
"help_text": "Seed used to generate a random number for phenotype and gene_set permutations: timestamp (default), 149, or user input. The specific seed value (149) generates consistent results, which is useful when testing software.",
"fa_icon": "fas fa-random"
},
"gsea_save_rnd_lists": {
"type": "boolean",
"description": "Save random ranked lists",
"help_text": "Set to 'true' (default=false) to save the random ranked lists of genes created by phenotype permutations. When you save random ranked lists, for each permutation, GSEA saves the rank metric score for each gene (the score used to position the gene in the ranked list). Saving random ranked lists is memory intensive; therefore, this parameter is set to false by default.",
"fa_icon": "fas fa-random"
},
"gsea_zip_report": {
"type": "boolean",
"description": "Make a zipped file with all reports",
"help_text": "Set to True (default=false) to create a zip file of the analysis results. The zip file is saved to the output folder with all of the other files generated by the analysis. This is useful for sharing analysis results",
"fa_icon": "fas fa-file-archive"
}
},
"fa_icon": "fas fa-layer-group"
},
"gprofiler2": {
"title": "gprofiler2",
"type": "object",
"description": "",
"default": "",
"properties": {
"gprofiler2_run": {
"type": "boolean",
"description": "Set to run gprofiler2 and do a pathway enrichment analysis.",
"fa_icon": "fas fa-running"
},
"gprofiler2_organism": {
"type": "string",
"description": "Short name of the organism that is analyzed, e.g. hsapiens for homo sapiens.",
"help_text": "Set this to the short organism name consisting of the first letter of the genus and the full species name, e.g. hsapiens for Homo sapiens, mmusculus for Mus musculus."
},
"gprofiler2_significant": {
"type": "boolean",
"default": true,
"description": "Should only significant enrichment results be considered?",
"help_text": "Default true; if false, will consider all enrichment results regardless of significance."
},
"gprofiler2_measure_underrepresentation": {
"type": "boolean",
"default": false,
"description": "Should underrepresentation be measured instead of overrepresentation?",
"help_text": "Default false; if true, will measure overrepresentation."
},
"gprofiler2_correction_method": {
"type": "string",
"description": "The method that should be used for multiple testing correction.",
"help_text": "One of gSCS (synonyms: analytical, g_SCS), fdr (synonyms: false_discovery_rate), bonferroni.",
"enum": ["gSCS", "analytical", "g_SCS", "fdr", "false_discovery_rate", "bonferroni"]
},
"gprofiler2_sources": {
"type": "string",
"description": "On which source databases to run the gprofiler query.",
"help_text": "GO, GO:MF, GO:BP, GO:CC, KEGG, REAC, WP, TF, MIRNA, HPA, CORUM, HP, or any comma-reparated combination thereof, e.g. 'KEGG,REAC'."
},
"gprofiler2_evcodes": {
"type": "boolean",
"default": false,
"description": "Whether to include evcodes in the results.",
"help_text": "This can decrease performance and make the query slower. See https://rdrr.io/cran/gprofiler2/man/gost.html"
},
"gprofiler2_max_qval": {
"type": "number",
"default": 0.05,
"description": "Maximum q value used for significance testing."
},
"gprofiler2_gost_token": {
"type": "string",
"description": "Token that should be used as a query.",
"help_text": "For reproducibility, instead of querying the online databases, you can provide a token, e.g. from a previous pipeline run or from a manual query on https://biit.cs.ut.ee/gprofiler/gost."
},
"gprofiler2_background_file": {
"type": "string",
"pattern": "^\\S+\\.(csv|tsv|txt)$|auto|null",
"description": "Path to CSV/TSV/TXT file that should be used as a background for the query; alternatively, 'auto'.",
"help_text": "It is advisable to run pathway analysis with a set of background genes describing which genes exist in the target organism in the first place so that other genes are not at all considered. You can provide a CSV/TSV table where one column contains gene IDs and the other rows contain abundance values, or a TXT file that simply contains one gene ID per line. Alternatively, you can set this parameter to 'auto'; in that case, the input abundance matrix will be used. If a matrix is used, all genes will be considered which had at least some abundance (i.e. sum of all abundance values in a row > 0)."
},
"gprofiler2_background_column": {
"type": "string",
"description": "Which column to use as gene IDs in the background matrix.",
"help_text": "If a background matrix is provided but this parameter is not set, will assume that the first matrix column contains the IDs."
},
"gprofiler2_domain_scope": {
"type": "string",
"default": "annotated",
"description": "How to calculate the statistical domain size.",
"help_text": "One of annotated (default), known, custom or custom_annotated; see https://rdrr.io/cran/gprofiler2/man/gost.html",
"enum": ["annotated", "known", "custom", "custom_annotated"]
},
"gprofiler2_min_diff": {
"type": "integer",
"default": 1,
"description": "How many genes must be differentially expressed in a pathway for it to be considered enriched? Default 1."
},
"gprofiler2_palette_name": {
"type": "string",
"default": "Blues",
"description": "Valid R palette name",
"help_text": "Check the content of `RColorBrewer::brewer.pal.info` from an R terminal for valid palette names."
}
},
"fa_icon": "fas fa-layer-group"
},
"shiny_app_settings": {
"title": "Shiny app settings",
"type": "object",
"description": "",
"default": "",
"properties": {
"shinyngs_build_app": {
"type": "boolean",
"default": true,
"description": "Should a Shiny app be built?",
"help_text": "At a minimum this will trigger generation of files you can quickly use to spin up a shiny app locally. But you can also use the 'shinyapps' settings to deploy an app straight to shinyapps.io.",
"fa_icon": "fas fa-wrench"
},
"shinyngs_deploy_to_shinyapps_io": {
"type": "boolean",
"description": "Should the app be deployed to shinyapps.io?",
"fa_icon": "fas fa-file-import"
},
"shinyngs_shinyapps_account": {
"type": "string",
"default": "None",
"description": "Your shinyapps.io account name",
"fa_icon": "fas fa-user"
},
"shinyngs_shinyapps_app_name": {
"type": "string",
"default": "None",
"description": "The name of the app to push to in your shinyapps.io account",
"fa_icon": "fas fa-file-signature"
},
"shinyngs_guess_unlog_matrices": {
"type": "boolean",
"default": true,
"description": "Should we guess the log status of matrices and unlog for the app?",
"help_text": "In the app context, it's usually helpful if things are not in log scale, so that e.g. fold changes make some sense with respect to observed values. This flag will cause the shinyngs app-building script to make a guess based on observed values as to the log status of input matrices, and adjust the loading accordingly."
}
},
"fa_icon": "fab fa-app-store-ios"
},
"gene_set_options": {
"title": "Options related to gene set analysis",
"type": "object",
"fa_icon": "fas fa-cogs",
"description": "Files and options used by gene set analysis modules.",
"properties": {
"gene_set_files": {
"type": "string",
"default": "None",
"description": "Gene sets in GMT or GMX-format; for GSEA: multiple comma-separated input files are possible. For gprofiler2: One GMT file is possible.",
"fa_icon": "fas fa-bars"
}
}
},
"reporting_options": {
"title": "Reporting options",
"type": "object",
"description": "",