nf-core/hadge

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

Mode of the pipeline.

Tools used for hash demultiplexing.

Tools used for genetic demultiplexing.

Perform BAM QC.

File with common variants. If provided, the BAM files will be filtered to only include reads that overlap with the common variants.

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

Save intermediate files.

Email address for completion summary.

MultiQC report title. Printed as page header, used for filename if not otherwise specified.

Name of iGenomes reference.

Path to FASTA genome file.

Match donor between different demultiplexing methods.

Path to demultiplexing result CSV file (necessary only for donor_match mode).

Path to Vireo filtered variants file (necessary only for donor_match mode).

Path to cell genotype file (necessary only for donor_match mode).

First method to use for donor matching.

Second method to use for donor matching.

Find variants for donor matching.

Option to subset the donor genotype based on detected variants.

Minimum variant count threshold.

The Minimal percentage of a variant for filtering. Has to be in a range between [0,5;1[. For example, 0.9 means that we only keep variants with a frequency higher than 90% or lower than 10%.

Path to cell genotype file (necessary only for donor_match mode).

Generate diagnostic plots.

The Dirichlet prior concentration parameter (alpha) on samples. An alpha value < 1.0 will make the prior sparse.

Only demultiplex cells/nuclei with at least this number of expressed genes.

Only demultiplex cells/nuclei with at least this number of UMIs.

Any cell/nucleus with less than this count of hashtags from the signal will be marked as unknown.

The random seed used in the KMeans algorithm to separate empty ADT droplets from others.

Comma-separated list of gender-specific genes (e.g. Xist) for generating violin plots.

Method(s) to use within BFF.

Whether to run preprocessing steps for BFF.

Path to barcode whitelist for preprocessing.

Path to cell barcode whitelist for GenerateCellHashingCalls().

Methods to use for consensus calling.

Optional metrics file path.

Whether to compute tSNE visualization in BFF.

Whether to generate heatmaps in BFF.

Per-cell saturation value.

Majority consensus threshold.

Library chemistry (e.g., 10xV3).

Threshold for caller disagreement.

Comma separated list of HTO names, without whitespace. If null, hto_names are extracted from the input hto matrix from features.tsv.gz.

If specified, it will generate the statistic summary of the dataset, including MSM and SSM rates. This requires an estimated total number of cells in the assay as input.

If true, full classification report is generated, otherwise the simplified classification report.

If true, summary report is generated.

Load a full classification report and skip the mtx folder as input. Requires a file path argument.

Provide the cell list. Requires a file path argument. Only executes if -u is set.

Names of the HTO tag(s) to extract, separated by ’,’. Joint HTO samples are combined with ’+’, such as ‘HTO_1+HTO_2’.

The confidence threshold value for classification. A higher value leads to more stringent classification.

The random seed used in the GaussianMixture algorithm.

Groovy list ([‘hash_1’, ‘hash_2’]) of .obs columns that contain cell hashing counts. Can be null if the data is in 10x Genomics format, as the columns are derived from the input.

List of comma-separated priors for each hypothesis: NEGATIVE, SINGLET, DOUBLET.

Column in cell_hashing_adata.obs for how to break up demultiplexing.

Input directory containing transcriptomic data in 10x mtx format.

Number of barcodes to use to create noise distribution.

Number of decimal places to round numeric values in cell_hashing_data.obs before saving. If omitted, no rounding is applied.

The quantile to use for thresholding.

Initialization method for clustering.

Number of starts for clustering.

Clustering function to use.

Number of samples for clustering.

Random seed for reproducibility.

Whether to print verbose output.

Generate ridge plot.

The number of plots that are dispalyed next to each other in one row. The number of plots corresponds to the number of Hash Tag Oligo (HTO) identifiers.

Generate feature scatter plot. If no features are provided (one of them is null), the first two features from the assay will be used.

Name of a Hash Tag Oligo (HTO) identifiers, usually defined in the feature.tsv of the hto matrix folder.

Name of a Hash Tag Oligo (HTO) identifiers, usually defined in the feature.tsv of the hto matrix folder.

Generate violin plot.

Features to plot (gene expression, metrics, PC scores, anything that can be retrieved by FetchData).

Plot the feature axis on log scale.

Generate a two dimensional tSNE embedding for HTOs.

What should we remove from the object (we have Singlet, Doublet and Negative).

Invert tSNE selection.

Verbose tSNE.

Approximate tSNE.

Max number of donors.

Value for perplexity.

Generate heatmap.

Number of cells for heatmap.

The quantile to use for thresholding.

Whether to automatically determine thresholds.

Maximum number of iterations.

Start of quantile range.

End of quantile range.

Step size for quantile range.

Whether to print verbose output.

Lower bound on total UMI count for empty droplets.

Number of iterations for Monte Carlo p-value calculations.

Whether to test ambient RNA.

Whether to round non-integer values.

Alternative method for identifying empty droplets.

FDR threshold for cell filtering.

Column to use for gene names.

Lower bound for ignoring barcodes.

Scaling parameter for Dirichlet-multinomial sampling.

Whether to use ambient solution abundance.

Minimum proportion for ambient profile inference.

Minimum pseudo-count for log-fold change computation.

Whether to use constant ambient contamination level.

Number of MADs to identify doublets.

Minimum threshold for doublet identification.

Whether to use 2-component mixture model for doublets.

Number of MADs to identify confident singlets.

Minimum threshold for confident singlet identification.

An integer matrix specifying valid combinations of HTOs. Number of items in each row has to be the same.

Whether to run EmptyDrops analysis as part of HashedDrops.

Method for feature selection.

Delimiter for parsing feature names.

Number of features to select.

Assay type for preprocessing.

Margin parameter for preprocessing.

Normalization method to use.

Column containing gene information.

Generate AnnData (.h5ad) outputs for hashing results.

Generate MuData outputs for hashing results.

Tag for cell barcodes, e.g., CB for 10x Genomics. Set to ‘None’ for bulk RNA-seq or SMART-seq2.

Tag for UMI barcodes, e.g., UB for 10x Genomics. Set to ‘None’ for bulk RNA-seq or SMART-seq2 without UMIs.

Minimum aggregated count (across cells) for SNPs to be included in the output.

Minimum minor allele frequency (MAF) for SNPs to be included in the output.

Required flags in SAM/BAM: skip reads that don’t have ALL of these flags. See SAM format specification for details.

Excluding flags in SAM/BAM: skip reads that have ANY of these flags. See SAM format specification for details.

Minimum read length (after clipping) for a read to be included.

Minimum mapping quality for a read to be included.

Maximum read depth at a position per input file. Set to 0 for highest possible value.

If true, do not skip anomalous read pairs (i.e., count orphan reads).

The tag for donor genotype in VCF file. Options: GT, GP, PL.

If true, do not check for doublets during demultiplexing.

Number of random initializations when GT needs to be learned.

Number of extra donors in pre-cluster, when GT needs to be learned.

Method for searching from extra donors. ‘size’: n_cell per donor; ‘distance’: GT distance between donors.

If true, treat donor GT as prior only and learn genotypes from data.

If true, turn on SNP specific allelic ratio (ASE mode).

If true, turn off plotting GT distance.

Random seed for initialization.

Range of cells to process, e.g., ‘0-10000’. Default is ‘all’.

If true, detect ambient RNAs in each cell (experimental feature).

Tag representing readgroup or cell barcodes to partition the BAM file into multiple groups. For 10x Genomics, use CB.

Tag representing UMIs. For 10x Genomics, use UB.

Maximum base quality (higher BQ will be capped).

Minimum base quality to consider (lower BQ will be skipped).

Minimum mapping quality to consider (lower MQ will be ignored).

Minimum distance to the tail (lower will be ignored).

SAM/BAM FLAGs to be excluded.

Minimum number of total reads for a droplet/cell to be considered.

Minimum number of unique reads (determined by UMI/SNP pair) for a droplet/cell to be considered.

Minimum number of SNPs with coverage for a droplet/cell to be considered.

FORMAT field to extract the genotype, likelihood, or posterior from.

Offset of genotype error rate. [error] = [offset] + [1-offset][coeff][1-r2]

Slope of genotype error rate. [error] = [offset] + [1-offset][coeff][1-r2]

INFO field name representing R2 value. Used for representing imputation quality.

Minimum minor allele frequency.

Minimum call rate.

Grid of alpha to search for.

Prior probability of doublet.

Prior probability of doublet.

Genotype error parameter per cluster.

Bayes Factor Threshold used in the initial clustering.

Fraction of droplets to be clustered in the very first round of initial clustering procedure.

Iteration for initial cluster assignment (set to zero to skip the iterations).

Keep missing cluster assignment as missing in the initial iteration.

Randomize the singlet scores to test its effect.

Seed for random number (use clocks if not set).

Ploidy, must be 1 or 2.

Min alt to use locus.

Min ref to use locus.

Max loci per cell, affects speed.

Number of restarts in clustering, when there are > 12 clusters we recommend increasing this to avoid local minima.

Common variant loci or known variant loci vcf, must be vs same reference fasta.

Known variants per clone in population vcf mode, must be .vcf right now we dont accept gzip or bcf sorry.

Which samples in population vcf from known genotypes option represent the donors in your sample. Provide space-separated sample names for multiple donors.

Don’t remap with minimap2 (not recommended unless in conjunction with —common_variants).

Set to True to ignore data error assertions.

Custom MultiQC yaml file containing HTML including a methods description.

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).