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

A string to identify results in the output directory

required
type: string
default: study

Also used as an identifier in some processes

A string identifying the technology used to produce the data

required
type: string

Currently 'rnaseq' or 'affy_array' may be specified.

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

required
type: string
pattern: ^\S+\.(csv|tsv|txt)$

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.

A CSV file describing sample contrasts

required
type: string
pattern: ^\S+\.(csv|tsv|txt)$

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)

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

required
type: string

Type of abundance measure used, platform-dependent

required
type: string
default: counts

Ways of providing your abundance values

TSV-format abundance matrix

type: string
pattern: ^\S+\.(tsv|csv|txt)$

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.

Not a required input if providing CEL files for affymetrix preprocessing.

(RNA-seq only): optional transcript length matrix with samples and genes as the abundance matrix

type: string

If provided, this file willl be used to provide transcript lengths to DESeq2 to model length bias across samples

Alternative to matrix: a compressed CEL files archive such as often found in GEO

type: string
default: null

Use this option to provide a raw archive of CEL files from Affymetrix arrays. Will be ignored if a matrix is specified.

Use SOFT files from GEO by providing the GSE study identifier

type: string
default: null

Use this option to provide a GSE study identifier.

Column in the samples sheet to be used as the primary sample identifier

required
type: string
default: sample

Type of observation

required
type: string
default: sample

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'.

Column in the sample sheet to be used as the display identifier for observations. If unset, will use value of --observations_id_col.

type: string

Options related to features

Feature ID attribute in the abundance table as well as in the GTF file (e.g. the gene_id field)

required
type: string
default: gene_id

Feature name attribute in the abundance table as well as in the GTF file (e.g. the gene symbol field)

required
type: string
default: gene_name

Type of feature we have, often 'gene'

required
type: string
default: gene

When set, use the control features in scaling/ normalisation

type: boolean

Use supplied control features in normalistion/ scaling operations?

A text file listing technical features (e.g. spikes)

type: string

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

Comma-separated string, specifies feature metadata columns to be used for exploratory analysis, platform-specific

type: string
default: gene_id,gene_name,gene_biotype

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).

type: string
pattern: ^\S+\.(csv|tsv|txt)$

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).

Where a GTF file is supplied, which feature type to use

type: string
default: transcript

Where a GTF file is supplied, which field should go first in the converted output table

type: string
default: gene_id

Of which assays to compute the log2. Not necessary for maxquant data as this is controlled by the pipeline.

type: string

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).

Options for processing of affy arrays with justRMA()

Column of the sample sheet containing the Affymetrix CEL file name

type: string
default: file

logical value. If TRUE, then background correct using RMA background correction.

type: boolean
default: true

integer value indicating which RMA background to use

type: integer
default: 2

1: use background similar to pure R rma background given in affy version 1.0 - 1.0.2
2: use background similar to pure R rma background given in affy version 1.1 and above

logical value. If TRUE, then works on the PM matrix in place as much as possible, good for large datasets.

type: boolean

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.

type: string
default: null

logical value. If TRUE, a matrix of probe annotations will be derived.

type: boolean
default: true

should the spots marked as 'MASKS' set to NA?

type: boolean

should the spots marked as 'OUTLIERS' set to NA?

type: boolean

if TRUE, then overrides what is in rm.mask and rm.oultiers.

type: boolean

Options for processing of proteomics MaxQuant tables with the Proteus R package

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.

type: string
default: LFQ intensity

Normalization function to use on the MaxQuant intensities.

type: string

'normalizeMedian' or 'normalizeQuantiles'

Which method to use for plotting sample distributions of the MaxQuant intensities; one of 'violin', 'dist', 'box'.

type: string

'violin', 'dist' or 'box'

Should a loess line be added to the plot of mean-variance relationship of the conditions? Default: true.

type: boolean
default: true

Valid R palette name

type: string
default: Set1

Check the content of RColorBrewer::brewer.pal.info from an R terminal for valid palette names.

Options related to filtering upstream of differential analysis

Minimum abundance value

required
type: number
default: 1

Minimum observations that must pass the threshold to retain the row/ feature (e.g. gene).

type: number
default: 1

A minimum proportion of observations, given as a number between 0 and 1, that must pass the threshold. Overrides minimum_samples

type: number

An optional grouping variable to be used to calculate a min_samples value

type: string

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.

A minimum proportion of observations, given as a number between 0 and 1, that must have a value (not NA) to retain the row/ feature (e.g. gene).

type: number
default: 0.5

Minimum observations that must have a value (not NA) to retain the row/ feature (e.g. gene). Overrides filtering_min_proportion_not_na.

type: number

Options related to data exploration

Clustering method used in dendrogram creation

required
type: string
default: ward.D2

Correlation method used in dendrogram creation

required
type: string
default: spearman

Number of features selected before certain exploratory analyses. If -1, will use all features.

required
type: integer
default: 500

Length of the whiskers in boxplots as multiple of IQR. Defaults to 1.5.

type: number
default: 1.5

Threshold on MAD score for outlier identification

type: integer
default: -5

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.

How should the main grouping variable be selected? 'auto_pca', 'contrasts', or a valid column name from the observations table.

required
type: string
default: auto_pca

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.

Specifies assay names to be used for matrices, platform-specific.

hidden
type: string
default: raw,normalised,variance_stabilised

Specifies final assay to be used for exploratory analysis, platform-specific

hidden
type: string
default: variance_stabilised

Valid R palette name

required
type: string
default: Set1

Check the content of RColorBrewer::brewer.pal.info from an R terminal for valid palette names.

Options related to differential operations

Advanced option: the suffix associated tabular differential results tables. Will by default use the appropriate suffix according to the study_type.

type: string

The feature identifier column in differential results tables

required
type: string
default: gene_id

The fold change column in differential results tables

required
type: string
default: log2FoldChange

The p value column in differential results tables

type: string
default: pvalue

The q value column in differential results tables.

required
type: string
default: padj

Minimum fold change used to calculate differential feature numbers

required
type: number
default: 2

Maximum p value used to calculate differential feature numbers

required
type: number
default: 1

Maximum q value used to calculate differential feature numbers

required
type: number
default: 0.05

Where a features file (GTF) has been provided, what attributed to use to name features

type: string
default: gene_name

Indicate whether or not fold changes are on the log scale (default is to assume they are)

type: boolean
default: true

Valid R palette name

required
type: string
default: Set1

Check the content of RColorBrewer::brewer.pal.info from an R terminal for valid palette names.

In differential analysis (DEseq2 or Limma), subset to the contrast samples before modelling variance?

type: boolean

test parameter passed to DESeq()

type: string

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)

fitType parameter passed to DESeq()

type: string

either "parametric", "local", "mean", or "glmGamPoi" for the type of fitting of dispersions to the mean intensity. See estimateDispersions for description.

sfType parameter passed to DESeq()

type: string

either "ratio", "poscounts", or "iterate" for the type of size factor estimation. See estimateSizeFactors for description.

'minReplicatesForReplace' parameter passed to DESeq()

type: integer
default: 7

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".

useT parameter passed to DESeq2

type: boolean

logical, passed to nbinomWaldTest, default is FALSE, where Wald statistics are assumed to follow a standard Normal

independentFiltering parameter passed to results()

type: boolean
default: true

logical, whether independent filtering should be applied automatically

lfcThreshold parameter passed to results()

type: integer

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.

altHypothesis parameter passed to results()

type: string
default: greaterAbs

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

pAdjustMethod parameter passed to results()

type: string
default: BH

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", "null".

alpha parameter passed to results()

type: number
default: 0.1

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.

minmu parameter passed to results()

type: number
default: 0.5

lower bound on the estimated count (used when calculating contrasts)

variance stabilisation method to use when making a variance stabilised matrix

type: string

'rlog', 'vst' or 'rlog,vst'

Shink fold changes in results?

type: boolean
default: true

'ashr' method is the only method currently implemented

Number of cores

type: integer
default: 1

Number of cores to use with DESeq()

blind parameter for rlog() and/ or vst()

type: boolean
default: true

logical, whether to blind the transformation to the experimental design

nsub parameter passed to vst()

type: integer
default: 1000

the number of genes to subset to (default 1000)

passed to lmFit(), positive integer giving the number of times each distinct probe is printed on each array.

type: number

passed to lmFit(), positive integer giving the spacing between duplicate occurrences of the same probe, spacing=1 for consecutive rows.

type: string
default: null

Sample sheet column to be used to derive a vector or factor specifying a blocking variable on the arrays

type: string
default: null

passed to lmFit(), the inter-duplicate or inter-technical replicate correlation

type: string
default: null

passed to lmFit(), the fitting method

type: string

"ls" for least squares or "robust" for robust regression

passed to eBayes(), a numeric value between 0 and 1, assumed proportion of genes which are differentially expressed

type: number
default: 0.01

passed to eBayes(), logical, should an intensity-dependent trend be allowed for the prior variance?

type: boolean

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.

passed to eBayes(), logical, should the estimation of df.prior and var.prior be robustified against outlier sample variances?

type: boolean

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

type: string
default: 0.1,4

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.

type: string
default: 0.05,0.1

passed to topTable(), minimum absolute log2-fold-change required

type: integer

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.

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.

type: boolean

passed to topTable(), method used to adjust the p-values for multiple testing.

type: string

cutoff value for adjusted p-values. Only genes with lower p-values are listed.

type: number
default: 1

Set to run GSEA to infer differential gene sets in contrasts

type: boolean

Permutation type

type: string

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)

Number of permutations

type: integer
default: 1000

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)

Enrichment statistic

type: string

See 'basic fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page for a detailed explanation.

Metric for ranking genes

type: string

See https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideTEXT.htm#_Metrics_for_Ranking for a detailed explanation.

Gene list sorting mode

type: string

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.

See 'basic fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page

Gene list ordering mode

type: string

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.

See 'basic fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page

Max size: exclude larger sets

type: integer
default: 500

After filtering from the gene sets any gene not in the expression dataset, gene sets larger than this are excluded from the analysis.

See 'basic fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page

Min size: exclude smaller sets

type: integer
default: 15

After filtering from the gene sets any gene not in the expression dataset, gene sets smaller than this are excluded from the analysis.

See 'basic fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page

Normalisation mode

type: string

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 'null' (GSEA does not normalize the enrichment scores).

See 'advanced fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page

Randomization mode

type: string

Method used to randomly assign phenotype labels to samples for phenotype permutations. Not used for gene_set permutations.

See 'advanced fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page

Make detailed geneset report?

type: boolean
default: true

Use median for class metrics

type: boolean

Set to true (default=false) to use the median of each class, instead of the mean, in the metrics for ranking for genes

See 'advanced fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page.

Number of markers

type: integer
default: 100

Number of features (gene or probes) to include in the butterfly plot in the Gene Markers section of the gene set enrichment report.

See 'advanced fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page.

Plot graphs for the top sets of each phenotype

type: integer
default: 20

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.

See 'advanced fields' at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?Run_GSEA_Page.

Seed for permutation

type: string
default: timestamp

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.

Save random ranked lists

type: boolean

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.

Make a zipped file with all reports

type: boolean

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

Set to run gprofiler2 and do a pathway enrichment analysis.

type: boolean

Short name of the organism that is analyzed, e.g. hsapiens for homo sapiens.

type: string

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. This has second priority and will be overridden by --gprofiler2_token.

Should only significant enrichment results be considered?

type: boolean
default: true

Default true; if false, will consider all enrichment results regardless of significance.

Should underrepresentation be measured instead of overrepresentation?

type: boolean

Default false; if true, will measure overrepresentation.

The method that should be used for multiple testing correction.

type: string

One of gSCS (synonyms: analytical, g_SCS), fdr (synonyms: false_discovery_rate), bonferroni.

On which source databases to run the gprofiler query

type: string

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'. This works if --gprofiler2_organism is used; if a GMT file is provided with --gene_sets_files, should also work; the module will then remove any lines not starting with any of the source names. Does not work for --gprofiler2_token as g:Profiler will not filter such a run.

Whether to include evcodes in the results.

type: boolean

This can decrease performance and make the query slower. See https://rdrr.io/cran/gprofiler2/man/gost.html

Maximum q value used for significance testing.

type: number
default: 0.05

Token that should be used as a query.

type: string

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. This has highest priority and will override --gprofiler2_organism and --gene_sets_files.

Path to CSV/TSV/TXT file that should be used as a background for the query; alternatively, 'auto' (default) or 'false'.

type: string
pattern: ^\S+\.(csv|tsv|txt)$|auto|false

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. This parameter is by default set to 'auto', meaning that the filtered input abundance matrix will be used. Alternatively, 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. If a custom CSV/TSV is used, all genes will be considered which had at least some abundance (i.e. sum of all abundance values in a row > 0). Set to 'false' if you do not want to use a background.

Which column to use as gene IDs in the background matrix.

type: string

If a background matrix is provided but this parameter is not set, will assume that the first matrix column contains the IDs.

How to calculate the statistical domain size.

type: string

One of annotated (default), known, custom or custom_annotated; see https://rdrr.io/cran/gprofiler2/man/gost.html

How many genes must be differentially expressed in a pathway for it to be considered enriched? Default 1.

type: integer
default: 1

Valid R palette name

type: string
default: Blues

Check the content of RColorBrewer::brewer.pal.info from an R terminal for valid palette names.

Should a Shiny app be built?

type: boolean
default: true

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.

Should the app be deployed to shinyapps.io?

type: boolean

Your shinyapps.io account name

type: string
default: null

The name of the app to push to in your shinyapps.io account

type: string
default: null

Should we guess the log status of matrices and unlog for the app?

type: boolean
default: true

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.

Files and options used by gene set analysis modules.

Gene sets in GMT or GMX-format; for GSEA: multiple comma-separated input files in either format are possible. For gprofiler2: A single file in GMT format is possible; this has lowest priority and will be overridden by --gprofiler2_token and --gprofiler2_organism.

type: string
default: null

Rmd report template from which to create the pipeline report

required
type: string
pattern: ^\S+\.Rmd$

The pipeline will always generate a default report which gives a good overview of the analysis results. Should this default report not suit your needs, you can provide the path to a custom report instead.

Email address for completion summary.

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

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

A logo to display in the report instead of the generic pipeline logo

required
type: string
default: docs/images/nf-core-differentialabundance_logo_light.png

CSS to use to style the output, in lieu of the default nf-core styling

required
type: string
default: assets/nf-core_style.css

A markdown file containing citations to include in the fiinal report

type: string
default: CITATIONS.md

A title for reporting outputs

type: string
default: null

An author for reporting outputs

type: string
default: null

Semicolon-separated string of contributor info that should be listed in the report.

type: string

List here names, roles, affiliations, contact info etc. of contributors to your project. Entries of different contributors are separated by semicolons, linebreaks within a contributor are separated by
. The first line of each contributor will be bold in the report. E.g.: 'Jane Doe
Director of Institute of Microbiology
University of Smallville;John Smith
PhD student
University of Smallville'

A description for reporting outputs

type: string
default: null

Whether to generate a scree plot in the report

type: boolean
default: true

To how many digits should numeric output in different modules be rounded? If -1, will not round.

type: integer
default: 4

This affects output from the following modules (both their tabular output and their result sections in the report): proteus, gprofiler2.

Reference genome related files and options required for the workflow.

Name of iGenomes reference.

type: string

If using a reference genome configured in the pipeline using iGenomes, use this parameter to give the ID for the reference. This is then used to build the full paths for all required reference genome files e.g. --genome GRCh38.

See the nf-core website docs for more details.

Genome annotation file in GTF format

type: string
pattern: ^\S+\.gtf(\.gz)?

"This parameter is mandatory if --genome is not specified."

Do not load the iGenomes reference config.

hidden
type: boolean

Do not load igenomes.config when running the pipeline. You may choose this option if you observe clashes between custom parameters and those supplied in igenomes.config.

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

Git commit id for Institutional configs.

hidden
type: string
default: master

Base directory for Institutional configs.

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

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

Institutional config name.

hidden
type: string

Institutional config description.

hidden
type: string

Institutional config contact information.

hidden
type: string

Institutional config URL link.

hidden
type: string

Set the top limit for requested resources for any single job.

Maximum number of CPUs that can be requested for any single job.

hidden
type: integer
default: 16

Use to set an upper-limit for the CPU requirement for each process. Should be an integer e.g. --max_cpus 1

Maximum amount of memory that can be requested for any single job.

hidden
type: string
default: 128.GB
pattern: ^\d+(\.\d+)?\.?\s*(K|M|G|T)?B$

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'

Maximum amount of time that can be requested for any single job.

hidden
type: string
default: 240.h
pattern: ^(\d+\.?\s*(s|m|h|d|day)\s*)+$

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'

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

Display help text.

hidden
type: boolean

Display version and exit.

hidden
type: boolean

Method used to save pipeline results to output directory.

type: string

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

Email address for completion summary, only when pipeline fails.

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

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

Send plain-text email instead of HTML.

hidden
type: boolean

Do not use coloured log outputs.

hidden
type: boolean

Incoming hook URL for messaging service

hidden
type: string

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

Boolean whether to validate parameters against the schema at runtime

type: boolean
default: true

Show all params when using --help

hidden
type: boolean

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.

Validation of parameters fails when an unrecognised parameter is found.

hidden
type: boolean

By default, when an unrecognised parameter is found, it returns a warinig.

Validation of parameters in lenient more.

hidden
type: boolean

Allows string values that are parseable as numbers or booleans. For further information see JSONSchema docs.