nf-core/differentialabundance

Also used as an identifier in some processes

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

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.

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)

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.

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

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

Use this option to provide a GSE study identifier.

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'.

Use supplied control features in normalistion/ scaling operations?

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

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).

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

If the sample columns are e.g. called 'LFQ intensity sample1', 'LFQ intensity sample2' etc., please set this parameter to 'LFQ intensity'.

'normalizeMedian' or 'normalizeQuantiles'

'violin', 'dist' or 'box'

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

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.

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.

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.

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).

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

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

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)

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

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

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".

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

logical, whether independent filtering should be applied automatically

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.

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

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".

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.

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

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

'ashr' method is the only method currently implemented

Number of cores to use with DESeq()

logical, whether to blind the transformation to the experimental design

the number of genes to subset to (default 1000)

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

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.

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.

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)

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)

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

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

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

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

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

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

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

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

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 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.

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 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.

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.

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 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.

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

Default false; if true, will measure overrepresentation.

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

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.

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

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.

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.

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

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

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

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.

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.

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.

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.

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'

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

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.

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

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.

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