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Marc Robinson-Rechavi

Jean Fan jean.fan is the invited speaker for the session on Machine learning algorithms for advancing spatial biology. Very active on Youtube and X, although not here.

jean.fanJean Fan | Personal sitePersonal website of Prof. Jean Fan
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Marc Robinson-Rechavi

Jean Fan : Single-cell methods assume a full transcriptome access, whereas spatial transcriptomics only access a skewed subset of genes, which impacts normalisation and downstream results biorxiv.org/content/10.1101/20

bioRxivGene count normalization in single-cell imaging-based spatially resolved transcriptomicsRecent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue anatomical regions or cell types, we find that normalization methods that use scaling factors derived from gene counts differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream differential gene expression and fold change analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed without normalization or when scaling factors are not derived from gene counts, such as with cell volume normalization. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data. ### Competing Interest Statement The authors have declared no competing interest.
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Marc Robinson-Rechavi

Jean Fan: "We can always machine learning to any data and it will always give us an answer" but we must be aware of the differences between data (single-cell resolution or not, full transcriptome or not…) to use the appropriate approach and obtain relevant results.

Marc Robinson-Rechavi

Impressive work by Jean Fan's lab : aligning different spatial transcriptomics results, not only from different experiments but different protocols, e.g. MERFISH - Visium. Combining with clustering and her lab's spot deconvolution allows to compare directly gene expression between these different experiments, biorxiv.org/content/10.1101/20

bioRxivAlignment of spatial transcriptomics data using diffeomorphic metric mappingSpatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at <https://github.com/JEFworks-Lab/STalign> and as supplementary software with additional documentation and tutorials available at <https://jef.works/STalign>. ### Competing Interest Statement MIM is a founder of AnatomyWorks. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies. The other authors declare that they have no competing financial interests.
Sep 12, 2023, 07:41 AM·Public·Web
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