nf-core/differentialabundance

A string identifier used to name result files in the output directory

A string identifying the technology used to produce the data

Path to CSV/TSV file containing information about the samples in the experiment.

A CSV/TSV/YML/YAML file describing sample contrasts to compare groups.

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

Type of abundance measure used, platform-dependent.

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

TSV/CSV-format abundance matrix

(RNA-seq only): optional transcript/gene length matrix with samples and transcript_ids/gene_ids as in the abundance matrix.

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

Use SOFT files from GEO by providing the GSE study identifier

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

Type of observation

Column in the sample sheet to be used as identifier for observations. If unset, the —observations_id_col is used.

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

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

Type of feature. Often ‘gene’

When set, use the control features in scaling/ normalization (currently only supported for differential_method deseq2)

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

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

Supply your own feature annotations. Can be derived from the GTF (rnaseq) or from the Bioconductor annotation package (affy arrays).

Name of the paramset to run. In profile mode, set by the analysis profile for output directory naming. In paramsheet mode, selects which paramset(s) to run (comma-separated).

Path to a paramsheet YAML file. Setting this activates multi-run (paramsheet) mode where paramsheet values take priority over CLI flags.

Column of the sample sheet containing the Affymetrix CEL file name

logical value. If set to true, apply background correction using RMA.

integer value indicating which RMA background to use

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

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.

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

should the spots marked as ‘MASKS’ set to NA?

should the spots marked as ‘OUTLIERS’ set to NA?

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

Genome annotation file in GTF format

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

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

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’; will search for both the prefix as entered and the prefix followed by one whitespace.

Normalization function to use on the MaxQuant intensities.

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

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

Valid R palette name

Minimum abundance value. Set to false to disable abundance filtering.

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

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

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

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).

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

Set to run IMMUNEDECONV

Set method to run with IMMUNEDECONV. Available options can be found in ‘https://omnideconv.org/immunedeconv/articles/immunedeconv.html’

Set function to run with IMMUNEDECONV. Available options can be found in ‘https://omnideconv.org/immunedeconv/articles/immunedeconv.html’

Clustering method used in dendrogram creation

Correlation method used in dendrogram creation

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

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

Threshold on MAD score for outlier identification

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

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

Valid R palette name

Differential analysis method

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

The feature identifier column in differential results tables

The fold change column in differential results tables

The p value column in differential results tables

The q value column in differential results tables (adjust p values/ q values).

Minimum fold change used to calculate differential feature numbers. Note that this number will be log2 transformed

Maximum p value used to calculate differential feature numbers

Maximum q value used to calculate differential feature numbers

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

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

Valid R palette name

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

test parameter passed to DESeq()

fitType parameter passed to DESeq()

sfType parameter passed to DESeq()

‘minReplicatesForReplace’ parameter passed to DESeq()

useT parameter passed to DESeq2

independentFiltering parameter passed to results()

lfcThreshold parameter passed to results()

altHypothesis parameter passed to results()

pAdjustMethod parameter passed to results()

alpha parameter passed to results()

minmu parameter passed to results()

variance stabilisation method to use when making a variance stabilised matrix

Shrink fold changes in results?

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

nsub parameter passed to vst()

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

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

Sample sheet column to be used to derive a vector or factor specifying a blocking variable on the arrays for limma::lmFit(); however, for random effects models, DREAM is the recommended approach in this pipeline

passed to limma::lmFit(), the inter-duplicate or inter-technical replicate correlation; however for random effects models, DREAM is the recommended approach in this pipeline

passed to lmFit(), the fitting method

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

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

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

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

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.

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

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.

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

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

Turns on and off usage of voom normalization in the Limma module.

Functional analysis method

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.

Permutation type

Number of permutations

Enrichment statistic

Metric for ranking genes

Gene list sorting mode

Gene list ordering mode

Max size: exclude larger sets

Min size: exclude smaller sets

Normalisation mode

Randomization mode

Make detailed geneset report?

Use median for class metrics

Number of markers

Plot graphs for the top sets of each phenotype

Seed for permutation

Save random ranked lists

Make a zipped file with all reports

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

Should only significant enrichment results be considered?

Should underrepresentation be measured instead of overrepresentation?

The method that should be used for multiple testing correction.

On which source databases to run the gprofiler query

Whether to include evcodes in the results.

Maximum q value used for significance testing.

Token that should be used as a query.

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

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

How to calculate the statistical domain size.

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

Valid R palette name

Path to TSV file containing network file for decoupler

Removes sources of a net with less than min_n targets

Comma-separated list of methods to use (e.g., ‘ora,ulm’)

Should a Shiny app be built?

Should the app be deployed to shinyapps.io?

Your shinyapps.io account name

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

Qmd report template from which to create the pipeline report

Email address for completion summary.

A markdown file containing citations to include in the final report

A title for reporting outputs

An author for reporting outputs

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

A description for reporting outputs

Whether to generate a scree plot in the report

Skip generation of reports

Name of iGenomes reference.

Method used to save pipeline results to output directory.

Email address for completion summary, only when pipeline fails.

Boolean whether to validate parameters against the schema at runtime

Display the help message.

Display the full detailed help message.

Display hidden parameters in the help message (only works when —help or —help_full are provided).