nf-core/pixelator

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

Path to a local or remote directory that is the “current working directory” for relative paths defined in the input samplesheet

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

Email address for completion summary.

Save intermediate amplicon reads generated from the raw input reads.

Trim N bases from the front of the reads

Trim N bases from the tail of the reads

The maximum length of a read

The minimum length (bases) of a read

The maximum number of Ns allowed in a read

Minimum avg. quality a read must have (0 will disable the filter)

Remove duplicated reads (exact same sequence)

Remove PolyG sequences (length of 10 or more)

The number of mismatches allowed (in percentage) [default: 0.1; 0.0<=x<=0.9]

Save intermediate QC read files containing all reads that passed the filters.

Save intermediate QC read files containing all reads that failed the filters.

The number of mismatches allowed (as a fraction)

The minimum length of the barcode that must overlap when matching

Save intermediate QC read files containing all reads that contain valid antibody barcodes.

Save intermediate QC read files containing all reads that failed the filters.

A list of comma separated antibodies to discard

The algorithm to use for collapsing (adjacency will perform error correction using the number of mismatches given)

The number of mismatches allowed when collapsing (adjacency)

Discard molecules with with a count (reads) lower than this value

Save an intermediate parquet file containing collapsed read information.

Activate the multiplet recovery using leiden community detection

Save an intermediate CSV file containing the unfiltered graph edge list.

The minimum size (pixels) a component/cell can have (disabled by default)

The maximum size (pixels) a component/cell can have (disabled by default)

Enable the estimation of dynamic size filters using a log-rank approach.

Enable aggregate calling, information on potential aggregates will be added to the output data

Save the raw_component_metrics.csv file from the annotate stage.

Save the PXL dataset after the annotate stage.

Skip analysis step

Compute polarization scores matrix (clusters by markers)

Compute colocalization scores (marker by marker) for each component

Use the bipartite graph instead of the one-node projection when computing polarization, coabundance and colocalization scores

Which transformation to use for the antibody counts when calculating polarity scores.

Set the number of permutations use to compute the empirical z- and p-values for the polarity score

The minimum number of counts of a marker to calculate the polarity score in the component

Select the type of transformation to use on the node by antibody counts matrix when computing colocalization

Select the size of the neighborhood to use when computing colocalization metrics on each component

Set the number of permutations use to compute the empirical p-value for the colocalization score

The minimum number of counts in a region for it to be considered valid for computing colocalization

The minimum number of counts in a component for it to be considered valid for computing colocalization

Save the PXL dataset after the analysis stage.

Skip layout step

Skip adding marker counts to the layout.

Select a layout algorithm to use. This can be specified as a comma separated list to compute multiple layouts. Possible values are: fruchterman_reingold, fruchterman_reingold_3d, kamada_kawai, kamada_kawai_3d, pmds, pmds_3d

Skip report generation

Override the container image reference to use for all steps using the pixelator command.

Save all intermediate results.