Introduction

nf-core/spatialaxe is a bioinformatics best-practice processing and quality control pipeline for Xenium (and soon Atera) data. The current plan for the pipeline implementation is shown in the metromap below. The pipeline is under active developement and changes might occure frequently.

Note

We are currently extending the pipeline for the 10x Atera system.

Tools supported

The pipeline supports the following tools:

• Segmenation methods:
  • Baysor
  • Cellpose
  • Xenium ranger (XR)
  • StarDist
• Segmentation free methods:
  • Ficture
  • Baysor
• Transcript assignment methods:
  • Segger
  • Proseg
• Utility methods:
  • SpatialData
  • Baysor
• QC methods:
  • MultiQC Xenium Extra Plugin
  • OPT

Usage

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Note

The pipeline does not support conda currently. We are working on it.

Quick Start

samplesheet.csv:

sample,bundle,image
test_sample,/path/to/xenium-bundle,/path/to/morphology.ome.tif

Now, you can run the pipeline using:

Run image-based segmentation mode

CELLPOSE -> BAYSOR -> XR-IMPORT_SEGMENTATION -> SPATIALDATA -> QC

nextflow run nf-core/spatialaxe \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR> \
   --mode <MODE>

Run coordinate-based segmentation mode

PROSEG -> PROSEG2BAYSOR -> XR-IMPORT_SEGMENTATION -> SPATIALDATA -> QC

nextflow run nf-core/spatialaxe \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR> \
   --mode coordinate

Run segfree mode

BAYSOR_SEGFREE

nextflow run nf-core/spatialaxe \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR> \
   --mode segfree

Run preview mode

BAYSOR_PREVIEW

nextflow run nf-core/spatialaxe \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR> \
   --mode preview

Run just the quality control

nextflow run nf-core/spatialaxe \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR> \
   --mode qc

Additional information

Warning

Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Runtime and resource estimations

Tool	Compute	Runtime (min / med / max)	Peak RSS (min / med / max)
Cellpose	GPU	1m / 4m / 1.4h	10 GB / 26 GB / 554 GB
Cellpose	CPU	1.3h / 2.3h / 6.5h	161 GB / 426 GB / 1115 GB
StarDist	GPU	1m / 4m / 7m	5 GB / 12 GB / 18 GB
StarDist	CPU	5m / 6m / 7m	18 GB / 18 GB / 18 GB
Segger (create_dataset)	GPU	2m / 9m / 31m	1.7 GB / 14 GB / 50 GB
Segger (create_dataset)	CPU	13m / 21m / 46m	13 GB / 19 GB / 49 GB
Segger (train)	GPU	10m / 43m / 2.9h	30 GB / 33 GB / 60 GB
Segger (predict)	GPU	2m / 16m / 59m	10 GB / 25 GB / 87 GB
Baysor (whole-image)	CPU	2m / 30m / 17h	6 GB / 10 GB / 650 GB
Baysor (tiled)	CPU	1m / 18m / 13h	0.2 GB / 34 GB / 530 GB
Proseg	CPU	1m / 18m / 6.8h	279 MB / 3.8 GB / 136 GB
XeniumRanger (resegment)	CPU	18m / 39m / 3.7h	28 GB / 54 GB / 60 GB
XeniumRanger (import_seg)	CPU	2m / 7m / 2.7h	2.6 GB / 11 GB / 51 GB
Ficture (preprocess)	CPU	3m / 4m / 13m	331 MB / 357 MB / 21 GB

• Cellpose GPU vs CPU: 35x faster on GPU (4m median vs 2.3h), 16x less memory (26 GB vs 426 GB)
• Segger: Only tool that truly requires GPU for all 3 steps (create_dataset, train, predict)
• StarDist: Very fast on CPU, GPU is not necessary to run its default model

Credits

nf-core/spatialaxe is mainly developed by Sameesh Kher, Dongze He, and Florian Heyl.

We thank the following people for their extensive assistance in the development of this pipeline:

• Tobias Krause
• Krešimir Beštak (kbestak)
• Matthias Hörtenhuber (mashehu)
• Maxime Garcia (maxulysse)
• Kübra Narcı (kubranarci)

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #spatialaxe channel (you can join with this invite).

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.