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To facilitate collaboration among cellular genomics researchers around Australia and to explore and solve challenges in analysing one of the next frontier technologies, namely spatial transcriptomics (ST). ST data contains both imaging pixel data and sequencing read data from the same tissue section, thus creating opportunities for new analysis approaches, which will be discussed at the Hackathon.



At the workshop, participants will form Hackathon teams to encourage team diversity regarding geographical locations, organisations, genders, and expertise. The Hackathon will mainly focus on interactive discussion and idea generation rather than real-time execution of data analysis programs. Attendants can produce algorithms and pseudo-codes to demonstrate how to implement the ideas, but no intensive computational runs are required. The teams should use their own laptops, and in rare cases, use VPN connection to access to HPC.

Prior to the workshop, participants are encouraged to attend the two technology talks on spatial transcriptomics in the morning session Sunday 21st July. For those interested in working with the data before the workshop, the data can be downloaded as instructed below.

At the end of the workshop, each team will deliver a flash presentation on their analysis ideas on spatial transcriptomics (3 minutes/team). There will be prizes for the winning teams.

Data set

Spatial transcriptomics data of prostate cancer tissues were generated by Lunderberg’s Lab, published in Nature Communications (Berglund et al, 2018). The dataset contains Slide-seq sequencing data for 12 tissue sections. Raw and processed count matrices and high-resolution images can be downloaded below. Raw Fastq files are available at European Genome–Phenome Archive (EGA) under the accession number EGAS0000100300.


  1. Raw count matrices:


    • 12 TSV files containing raw read count data of Slide-seq spots in 12 tissue sections (also contains 2 additional aligned TSV files).

  2. Coordinate-adjusted count matrices:​


    • 12 TSV files containing coordinate-adjusted count data that match H&E images

  3. H&E tissue images:​


    • 12 raw high-resolution images, kindly provided by the authors

Examples of discussion topics

  • QC and normalization of image data and sequencing data within a tissue section and between tissue sections

  • Identification of cell types and microenvironments

  • Identification of cell-cell interactions

  • Integration of imaging data and sequencing read count data

Challenge Entrants
Judging criteria
Description of datasets
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