spatialaxe

Display the full detailed help message.

Custom MultiQC yaml file containing HTML including a methods description.

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

Image alignment file containing similarity transform matrix. (e.g., the _imagealignment.csv file exported from Xenium Explorer)

Model to use for running or starting training.

Email address for completion summary.

Path to gene panel JSON file to use for relabeling transcripts with the correct gene.

Gene synonyms that may have been counted as off-targets but simply differ in name.

Path to comma-separated file containing information about the Xenium experiment. (eg; meta,path-to-xenium-bundle,path-to-morphology.ome.tif))

Segmentation method to run.

Mode in which the pipeline is to be run. Either image-based segmentation, coordinate-based segmentation, segmentation-free analysis or data preview.

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

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

Fasta file for the probe sequences used in the xenium experiment.

Path to qupath segmentation file in GeoJSON format.

Path to the directory containing genomic features (.gff) and fasta (.fa) files used as reference annotations.

Prior segmentation mask from other segmentation methods.

StarDist pretrained model for cell segmentation (e.g., '2D_versatile_fluo', '2D_versatile_he').

StarDist tiling for large images (e.g., '4 4'). Reduces memory usage.

StarDist non-maximum suppression threshold. Lower values reduce overlapping detections.

StarDist pretrained model for nuclei segmentation.

StarDist object probability threshold. Lower values detect more objects.

Base path / URL for data used in the test profiles.

Number of samples to process per batch during prediction

Number of samples to process per training batch

Path to Baysor config TOML file (optional).

Prior segmentation type for Baysor. 'cells' uses Xenium bundle cell_id column; 'cellpose' uses Cellpose mask as image prior.

Baysor prior-segmentation-confidence (0-1).

Baysor --scale parameter for non-tiled runs.

Enable tiled Baysor segmentation (divide transcripts into patches, run Baysor per patch, stitch results).

Balance transcripts across tiles by merging sparse tiles.

Tile width in microns for Baysor tiling.

Minimum molecules per cell (--min-molecules-per-cell) for tiled Baysor.

Post-stitch cell filtering threshold: minimum transcripts per cell.

Overlap between Baysor patches in microns.

Baysor --scale for tiled runs (larger to compensate for EM on smaller tiles).

Specify the name of the boundary stain to use or disable. Supported for cell segmentation staining workflow output bundles. Possible options are: \"ATP1A1/CD45/E-Cadherin\" (default) or \"disable\"

Process only one sample at a time from a multi-sample samplesheet.

Number of sample(s) to process at a time from a multi-sample samplesheet. Works if buffered_samples is true.

Whether to use connected components for grouping transcripts without direct nucleus association

Whether to only run nucleus segmentation.

Pre-downscale morphology image to avoid Cellpose OOM on large images.

AWS Batch queue for Cellpose (single large GPU).

Number of tiles along the x axis for cell-type separability.

Number of tiles along the y axis for cell-type separability.

Minimum intensity in photoelectrons (pe) to filter nuclei. Default: 100. (appropriate range of values is 0 to 99th percentile of image stack or 1000, whichever is larger)

Number of devices (GPUs) to use during training

Nuclei boundary expansion distance in µm. Default: 5 (Min: 0, Max: 15 if either boundary-stain or interior-stain are enabled and 100 if nucleus-expansion only)

List of features to be passed to the ficture method. (eg: TP53,OCIAD1,BCAS3,SOX)

Whether to filter the transcripts.parquet file before running Baysor segmentation.

Input data platform. Used by proseg, segger, and spatialdata modules.

AWS Batch queue for GPU tasks (e.g., Segger, ProSeg).

List of image-based segmentation methods.

Specify the name of the interior stain to use or disable. Supported for cell segmentation staining workflow output bundles. Possible options are: \"18S\" (default) or \"disable\"

Number of training epochs

only keep transcripts whose x-coordinate is less than specified limit, if no limit is specified, the default value will retain all transcripts since Xenium slide is <24000 microns in x and y (default: 24000.0)

only keep transcripts whose y-coordinate is less than specified limit, if no limit is specified, the default value will retain all transcripts since Xenium slide is <24000 microns in x and y (default: 24000.0)

Minimum Q-Score to pass filtering.

only keep transcripts whose x-coordinate is greater than specified limit, if no limit is specified, the default minimum value will be 0.0

only keep transcripts whose y-coordinate is greater than specified limit, if no limit is specified, the default minimum value will be 0.0

Regex used to identify or match negative control samples in a dataset.

Whether to only run nucleus segmentation.

Whether to run the off-target probe tracking.

IQR multiplier for empirical cell size filtering during stitching.

Post-stitch cell size filtering method. Options: 'empirical' (IQR-based), 'distribution' (z-score), 'both', or null to disable.

Z-score threshold for distribution-based cell size filtering during stitching.

Grid layout for tiling (rows x cols), e.g. '3x3', '4x4'.

Overlap between adjacent patches in microns.

Whether to relabel genes with gene_panel.json file. True when gene_panel is provided.

Whether to run the qc layer in the pipeline.

List of segmentation-free methods.

Device used for training. (e.g., cuda for GPU or cpu)

Method for KNN computation. (e.g., cuda for GPU-based computation)

Path to a pre-trained Segger model checkpoint.

Number of data-loader workers for Segger.

Whether to run refinement on the image-based segmentation methods. Runs coordinate-based methods after the initial image-based segmentation run.

Whether to enhance the morphology.ome.tif file.

Height of the tiles in pixels

Width of the tiles in pixels

Enable tiled segmentation for large datasets. Divides transcripts into overlapping patches, runs segmentation in parallel per patch, then stitches results.

List of transcript-based segmentation methods.

Enable GPU acceleration (set automatically by the gpu profile).

Whether to run vanilla xeniumranger workflow.