EI Spatial Transcriptomics Analysis pipeline

master @ 180f243

Introduction

EarlhamInst/eista is a bioinformatics pipeline that performs analysis for single-cell spatial transcriptomics data (Vizgen data and 10x Xenium data). The pipeline is built using Nextflow. The pipeline was developed as a generalized, flexible, and scalable workflow for spatial transcriptomics analysis. It is primarily designed for Vizgen MERFISH data and 10x Xenium data. The pipeline can be applied from multiple starting points, either from raw image data or processed AnnData for the specified analysis phase.

The modules of the pipeline are listed as follows:

• Primary analysis
  • Vizgen Post-processing Tool which including following processes:
    • Cell segmentation - defines cell boundaries from images
    • Partition transcripts - determine which cell contains each detected transcript
    • Calculate cell metadata - calculate the geometric attributes of each cell
    • Sum signals - find the intensity of each mosaic image in each cell
    • Update vzg - Updates an existing .vzg file with new segmentation boundaries and expression matrix
  • MTX conversion - Converting the Vizgen/Xenium matrixes into Anndata objects
  • CONCAT counts - Concatenating input Anndata objects into one Anndata object and combine them into one Anndata object
• Secondary analysis
  • QC & cell filtering - cell filtering and QC on raw data and filtered data
  • Clustering analysis - single-cell clustering analysis
  • Merging/integration of samples
  • Spatial statistics analysis - Neighbor enrichment analysis, calculating centrality scores and Moran's I score
• Tertiary analysis
  • Cell type annotation
  • Differential expression analysis
  • Cell-cell communication analysis
  • Other downstream analyses (to be implemented)
• Pipeline reporting
  • Analysis report - Single-ell Analysis Report.
  • MultiQC - Aggregate report describing results and QC for tools registered in nf-core
  • Pipeline information - Report metrics generated during the workflow execution

1. Read QC (FastQC)
2. Present QC for raw reads (MultiQC)

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

Now, you can run the pipeline using:

nextflow run nf-core/eista \
   -profile  \
   --input samplesheet.csv \
   --outdir 

[!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.

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.

Credits

nf-core/eista was originally written by Huihai Wu.

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

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 #eista 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.