Datasets: seqFISH

Lukas M. Weber

Leader(s)

Difficulty & duration

Tier 1 (dataset)

  • difficulty: intermediate
  • duration: 1-2 days

Tier 2 (workflow)

  • difficulty: advanced
  • duration: 1-2 weeks

Abstract

Our current aims are:

  • Finalize SpatialExperiment class structure by creating and testing example datasets from several technological platforms (to be collected in STdata package)
  • Adapt visualization functions for data from each platform (to be collected in ggspavis package)
  • Create a short analysis workflow using one of the example datasets from each platform

This will allow us to build an integrated Bioconductor-based infrastructure for analyzing spatially resolved transcriptomics data. Ultimately, this will all be showcased in our OSTA online book.

So far, we have mainly worked with data from the 10x Genomics Visium platform. The STdata package (under development) currently contains two example datasets from the Visium platform (human DLPFC and mouse coronal).

In this set of “Dataset” challenges, we will aim to select and prepare several additional datasets for demonstration and testing purposes.

seqFISH and seqFISH+ (Eng et al. 2019) are molecule-based spatially resolved transcriptomics platforms. These platforms allow measuring expression of hundreds to thousands of genes, with sub-cellular resolution. For an example of a recent paper using this technology, see Lohoff and Ghazanfar et al. 2020.

Compared to Visium, the sub-cellular resolution of this technology allows capturing very different types of information, which may also require some slight modifications to the SpatialExperiment class structure (see also “SpatialExperiment: molecule-based data” challenge).

Targets

This challenge consists of two tiers:

Tier 1 (dataset)

The Tier 1 challenge consists of:

  • selecting a suitable publicly accessible seqFISH or seqFISH+ dataset
  • formatting it into a SpatialExperiment object
  • creating a reproducible script to create the object from the raw data files, saving the object, and adding these to the STdata package

As a starting point, we can use the make-data.R scripts from the existing objects in the STdata package:

Note there is some overlap with the “SpatialExperiment: molecule-based data” challenge, where we aim to ensure that the SpatialExperiment class can be used to correctly store seqFISH (or other molecule-based) data, using an example dataset as a test case.

Tier 2 (workflow)

If there is interest and capacity, we could then continue with the Tier 2 challenge:

  • write a complete short analysis workflow, using the SpatialExperiment object created in the Tier 1 challenge
  • format the analysis workflow as an RMarkdown file, which could later be integrated into the workflow chapters of our OSTA online book