Dr Shila Ghazanfar

Single-molecule, tissue and whole organ resolutions: data analytical approaches for high resolution spatial transcriptomics data

Dissociated transcriptional profiling of single cells has advanced our knowledge of the molecular basis of development. Novel spatial transcriptomics technologies, such as seqFISH, enable sensitive detection of RNA molecules in their native spatial context. However, this represents an analytical challenge as biologically relevant information may need to be extracted at the order of individual molecules, cells or tissues across entire organisms. This talk will describe the current state of play for analysis of such spatial omics datasets, and explore some emerging approaches for addressing these analytical challenges. A key example is the problem of mosaic data integration stemming from extraction of spatial -omics features in conjunction with data from dissociated single cell -omics technologies.

WORKSHOP: Unlocking single cell spatial omics analyses with scdney

In this workshop, we will focus on the newest techniques for understanding spatial genomics and spatial proteomics datasets. We will use some example data available via Bioconductor but encourage participants to bring questions for their own data analyses. Topics include detecting co-localisation of different cell types using mixed effect models (spicyR), identifying differential covarying genes in space (scHOT), and extracting spatial feature representations for precision medicine (scFeatures).

Key words: Spatial transcriptomics, high parameter imaging, scdney, higher order testing, feature engineering, precision medicine.

Requirements: You will need to bring your own laptop. Please make sure it has the latest version of R installed and the latest version of RStudio Desktop. Some existing knowledge of R is assumed.
Please install the following software via Bioconductor and Github:

  • Analytical packages: scdney (this includes installation of spicyR, scHOT and scFeatures)
  • Data packages: STexampleData, imcdatasets

Relevance: This is relevant to anyone who is interested in spatial genomics data analysis and wants to learn commonly used tools for such analyses in the R – Bioconductor environment.

Dr Shila Ghazanfar

ARC Discovery Early Career Researcher, The University of Sydney

Dr Shila Ghazanfar completed her undergraduate and PhD studies in statistics and statistical bioinformatics at The University of Sydney, before completing a Royal Society Newton International Fellowship at The University of Cambridge under the mentorship of Dr John Marioni in computational biology. Currently, she is an Australian Research Council DECRA Fellow at the University of Sydney, and has recently become one of the first grantees of the Chan Zuckerberg Initiative Data Insights program.
Dr Ghazanfar’s interests are in developing statistical bioinformatic and biomedical data science approaches for the meaningful integration of complex and high dimensional biological datasets. She is an expert in statistical and computational analysis of spatial transcriptomics and single cell RNA-seq data.