nf-core/tumourevo

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

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

Path to reference fasta file.

Reference genome name.

List of tools for running the pipeline.

Flag for filtering or not QC mutations.

Method used to save pipeline results to output directory.

Parameter for downloading VEP cache.

Path to VEP cache.

VEP cache version.

VEP species.

VEP reference genome name.

Add an extra custom argument to VEP.

Path to driver table.

Flag for filtering mutations from vcf.

For clonal simple CNAs, the list of segments to test.

For clonal simple CNAs, a filter for the segments to test.

For subclonal segments, should the tool try to merge segments with similar CCF and the same copy number alteration?

Used in the CCF estimation step.

For clonal simple CNAs, this is the same as cnaqc_min_karyotype_size, but with a cut measured on absolute mutation counts.

For clonal simple CNAs, peaks detected will be filtered if, in a peak, we map less than cnaqc_p_binsize_peaks * N mutations.

For clonal simple CNAs, the purity error tolerance to determine QC pass or fail.

For clonal simple CNAs, a tolerance in comparing bands overlaps which is applied to the raw VAF values.

For clonal simple CNAs, the number of times peak detection is bootstrapped.

For KDE-based matches the adjust density parameter; see density.

For clonal simple CNAs, if “closest” the closest peak will be used to match the expected peak. If “rightmost” peaks are matched prioritizing right to left peaks (the higher-VAF gets matched first); this strategy is more correct in principle but works only if there are no spurious peaks in the estimated density.

For subclonal simple CNAs, the starting state to determine linear versus branching evolutionary models.

Minimum number of mutations that are required to be mapped to a karyotype in order to compute CCF values.

Either “ENTROPY” (default) or “ROUGH”, to reflect the two different algorithms to compute CCF.

If TRUE the original CNAqc object is kept in the joinCNAqc object, otherwise it is lost.

The probability density used to model the read count data. Choices are beta-binomial and binomial.

Number of random restarts of variational inference.

Number of grid points used for approximating the posterior distribution.

The number of clusters to use while fitting.

Whether to use tail mutations for subclonal deconvolution or not .

The minimum mixing proportion of a cluster to be returned as output.

A vector with the number of Beta components to use.

Threshold for minimum VAF to use in the deconvolution

The minimum number of mutations assigned to a cluster to be returned as output.

The number of fits to be computed.

The concentration parameter of the Dirichlet mixture.

The maximum number of clusters returned. Lower values speed up convergence.

Maximum number of fit iterations.

The prior Beta hyperparameter for each Binomial component a

The prior Beta hyperparameter for each Binomial component b

The epsilon to measure convergence as ELBO absolute difference

Initialization of the q-distribution to compute the approximation of the posterior distributions.

Whether points assigned to a cluster that is filtered out are re-assigned by the density function.urn the trace of model fit.

The minimum Binomial success probability when applying a heuristic procedure to filter clusters after Variational Inference.

The minimum size of the mixture component when applying a heuristic procedure to filter clusters after Variational Inference.

Boolean value whether points assigned to a cluster that is filtered our, are re-assigned from the density function.

The minimum number of dimensions where we want to detect a Binomial component when applying a heuristic procedure to filter clusters after Variational Inference.

The number of iterations of every single run of NMF LASSO.

Number of signatures to be discovered.

Number of iterations to estimate the length(K) matrices beta (including the background signature) in case the argument beta is NULL. Ignored if beta is given.

The number of sub-iterations involved in the sparsification phase, within a full NMF LASSO iteration.

The number of requested NMF worker subprocesses to spawn. If Inf, an adaptive maximum number is automatically chosen. If NA or NULL, the function is run as a single process.

The cross-validation test size, i.e., the percentage of entries set to zero during NMF and used for validation.

The number of repetitions of the cross-validation procedure.

The number of randomized restarts of a single cross-validation repetition, in case of poor fits.

The candidate values of the sparsity parameter for the signature matrix ‘beta’ whose goodness of fit is assessed by cross-validation.

The candidate values of the sparsity parameter for the exposure-matrix entries alpha whose goodness of fit is assessed by cross-validation.

Seed for the random number generation. To be set for reproducibility.

The candidate values of the sparsity parameter for the exposure-matrix entries alpha whose goodness of fit is assessed by cross-validation.

Mode of publishing the SigProfiler genome.

Specify True if the reference genome should be downloaded.

Specify the path to the reference genome (if downloaded by the user), e.g. path/to/genome/tsb

‘matrix’ is used for table format inputs using a tab separated file.

The minimum number of signatures to be extracted.

The maximum number of signatures to be extracted.

Mutation context name(s), separated by commas (,), that define the mutational contexts for signature extraction. In the default value, 96 represents the SBS96 context, DINUC represents the dinucleotide context, and ID represents the indel context.

The number of iterations to be performed to extract each number signature.

The minimum number of iterations to be completed before NMF converges.

The maximum number of iterations to be completed before NMF converges .

The number number of iterations to done between checking next convergence .

The seed ensuring reproducible NMF replicate resamples.

A number that represent the normal contamination level for which the sample is considered passed or failed.

Email address for completion summary.

Email address for completion summary, only when pipeline fails.

Do not use coloured log outputs.

Send plain-text email instead of HTML.

Git commit id for Institutional configs.

Base directory for Institutional configs.

Institutional config description.

Institutional config contact information.

Institutional config URL link.

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).