Introduction Usage Parameters Output

Introduction

This document describes the output produced by the pipeline.

The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory.

Pipeline overview

The pipeline is built using Nextflow and processes data using the following steps:

Image mode

Output files
  • image/
    • xeniumranger/
      • resegment/
        • ${meta.id}/ Directory containing the output xenium bundle of Xenium
    • baysor/
      • preprocess/
        • *.csv filtered transcripts CSV (for Baysor 0.7.1 Parquet.jl compatibility)
      • run/
        • *segmentation.csv results of segmentation
        • *.json file with outlines of segmentation
        • segmentation_params.dump.toml file with full list of parameters used for the model
        • segmentation_log.log output file with metadata of running the workflow
        • segmentation_counts.loom loom file with metadata
        • segmentation_cell_stats.csv statistics of segmented cells
    • cellpose_cells/
      • *masks.tif labelled mask output from cellpose in tif format
      • *flows.tif cell flow output from cellpose
      • *seg.npy numpy array with cell segmentation data
    • stardist_nuclei/
      • *.{tiff,tif} labelled mask output from stardist in tif format
    • resolift/
      • *.tiff path to save the upscaled TIFF file

Coordinate mode

Output files
  • coordinate/
    • xenium_patch/
      • patches/patch_grid.json patch_grid.json metadata file
      • patches/patch_*/transcripts.parquet per-patch transcripts.parquet files (one per patch)
      • output/xr-cell-polygons.geojson stitched cell polygons
      • output/xr-transcript-metadata.csv transcript metadata
    • proseg/
      • preset/
        • cell-polygons.geojson.gz 2D polygons for each cell in GeoJSON format. These are flattened from 3D
        • expected-counts.csv.gz cell-by-gene count matrix
        • cell-metadata.csv.gz cell centroids, volume, and other information
        • transcript-metadata.csv.gz transcript ids, genes, revised positions, assignment probability
        • gene-metadata.csv.gz per-gene summary statistics
        • rates.csv.gz cell-by-gene Poisson rate parameters
        • cell-polygons-layers.geojson.gz a separate, non-overlapping cell polygon for each z-layer, preserving 3D segmentation
        • cell-hulls.geojson.gz convex hulls around assigned transcripts
      • proseg2baysor/
        • xr-cell-polygons.geojson 2D polygons for each cell in GeoJSON format. These are flattened from 3D
        • xr-transcript-metadata.csv transcript ids, genes, revised positions, assignment probability
    • segger/
      • create_dataset/
        • ${meta.id}/ directory to save the processed Segger dataset (in PyTorch Geometric format)
      • train/
        • ${meta.id}/ directory to save the trained model and checkpoints
      • predict/
        • ${meta.id}/ directory to save the segmentation results, including cell boundaries and associations
    • baysor/
      • run/
        • *segmentation.csv results of segmentation
        • *.json file with outlines of segmentation
        • segmentation_params.dump.toml file with full list of parameters used for the model
        • segmentation_log.log output file with metadata of running the workflow
        • segmentation_counts.loom loom file with metadata
        • segmentation_cell_stats.csv statistics of segmented cells

Segfree mode

Output files
  • segfree/
    • baysor/
      • preprocess/
        • *.csv filtered transcripts CSV (for Baysor 0.7.1 Parquet.jl compatibility)
      • segfree/
        • ncvs.loom loom file with neighborhood results
        • ncvs_segfree_log.log Log file with summary statistics
    • ficture/
      • preprocess/
        • processed_transcripts.tsv.gz transcirpt file used for FICTURE
        • coordinate_minmax.tsv listing the min and max of the coordinates used for FICTURE
        • feature.clean.tsv.gz another file contains the (unique) names of genes that should be used for FICUTRE
      • ${meta.id}/results/ files containing the results of FICTURE

QC mode

Output files
  • opt/
    • flip/
      • *.fa the forward oriented fasta file
    • track/
      • *.tsv TSV file containing the gene and transcript information to which each probe aligns
    • stat/
      • *.tsv TSV file containing the summary stats
  • multiqc/
    • multiqc_report.html: a standalone HTML file that can be viewed in your web browser.
    • multiqc_data/: directory containing parsed statistics from the different tools used in the pipeline.
    • multiqc_plots/: directory containing static images from the report in various formats.

Preview mode

Output files
  • preview/
    • baysor/
      • preview/
        • preview.html segmentation preview

Additional Functionality

SpatialData

The pipeline create spatialdata objects (data bundles) on various stages (see metromap in the README)

Output files
  • spatialdata/
    • write/${meta.id}/spatialdata/ spatialdata bundle of the raw data
    • meta/${meta.id}/spatialdata_spatialxe_final/ spatialdata bundle of the final data with metadata
      • sdata['raw_table'].uns['spatialdata_attrs'] provenance metadata
      • sdata['raw_table'].uns['experiment_xenium'] experimental metadata
      • sdata['raw_table'].uns['gene_panel'] gene panel metadata

Xenium Ranger Import Segmentation)

This step is needed to import segemntations from different methods into the xenium bundle and is called at different stages of the pipeline.

Output files
  • xeniumranger/
    • import_segementation/
      • ${meta.id}/ directory containing the output xenium bundle of Xenium

MultiQC

Output files
  • multiqc/
    • multiqc_report.html: a standalone HTML file that can be viewed in your web browser.
    • multiqc_data/: directory containing parsed statistics from the different tools used in the pipeline.
    • multiqc_plots/: directory containing static images from the report in various formats.

MultiQC is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.

The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see http://multiqc.info.

Pipeline information

Output files
  • pipeline_info/
    • Reports generated by Nextflow: execution_report.html, execution_timeline.html, execution_trace.txt and pipeline_dag.dot/pipeline_dag.svg.
    • Reports generated by the pipeline: pipeline_report.html, pipeline_report.txt and software_versions.yml. The pipeline_report* files will only be present if the --email / --email_on_fail parameter’s are used when running the pipeline.
    • Reformatted samplesheet files used as input to the pipeline: samplesheet.valid.csv.
    • Parameters used by the pipeline run: params.json.

Nextflow provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.
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