Shila Ghazanfar

2 months ago | nature.com | Chuhan Wang |Adam Chan |Xiaohang Fu |Shila Ghazanfar |Ellis Patrick

AbstractSpatial transcriptomics has enabled the quantification of gene expression at spatial coordinates across a tissue, offering crucial insights into molecular underpinnings of diseases. In light of this, several methods predicting spatial gene expression from paired histology images have provided the opportunity to enhance the utility of obtainable and cost-effective haematoxylin-and-eosin-stained histology images.

Oct 9, 2024 | nature.com | Shila Ghazanfar

One of my first experiences of analysing biomedical data, following my training in applied statistics, was to perform differential gene expression analysis of microarray data. Nowadays, microarray technologies have been largely superseded by three major biotechnological advances: RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq) and spatial ‘omics’.

Aug 19, 2024 | nature.com | David Horsfall |Daniela Basurto-Lozada |Kenny Roberts |Martin Prete |Peng He |Josh Moore | +5 more

Multimodal tissue atlasing datasets pose two key challenges for online dissemination and equitable access. First, single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics data objects are often saved in non-unified sequencing and imaging file formats that perform poorly with web technologies. Second, existing software platforms do not readily support simultaneous browsing of multiple integrated data modalities.