By Sang Ho Kwon. Recent advancements in spatially-resolved transcriptomics (SRT) technologies have ushered in a new era of possibilities for biological research. These technologies offer the unique ability to map biomolecular information within the native tissue architecture. Preserving the spatial resolution of genome-wide gene expression allows researchers to obtain a more holistic view of the tissue microenvironment, particularly the underlying molecular and cellular dynamics in a spatial-anatomical context, which is useful to understand the composition, states, and function of individual cell types, as well as their interactions with one another in a defined microenvironment.

By Nick Eagles Over the past few years, I’ve had the opportunity to work with a lot of whole-genome bisulfite-sequencing (WGBS) datasets. They provide a powerful opportunity to look at DNA methylation on a complete scale, in contrast to microarrays which target a narrower set of important CpG sites across the genome. But for this same reason, the data is often unwieldy, and can feel difficult to tackle even with access to powerful computational resources.

By Nick Eagles We’ve recently been interested in exploring the (largely python-based) tools others have published to process spatial transcriptomics data for various end goals. A common goal is to integrate data from platforms like Visium, which provides some information about how gene expression is spatially organized, with other approaches with potentially better spatial resolution or gene throughput. In particular, we came across a paper by Biancalani, Scalia et al.

By Arta Seyedian Medical Cost Personal Datasets Insurance Forecast by using Linear Regression Link to Kaggle Page Link to GitHub Source Around the end of October 2020, I attended the Open Data Science Conference primarily for the workshops and training sessions that were offered. The first workshop I attended was a demonstration by Jared Lander on how to implement machine learning methods in R using a new package named tidymodels.

By Brenda Pardo A month ago, I started an enriching adventure by joining Leonardo Collado-Torres’ team at Lieber Institute for Brain Development. Since then, I have been working on modifying spatialLIBD, a package to interactively visualize the LIBD human dorsolateral pre-frontal cortex (DLPFC) spatial transcriptomics data (Maynard, Collado-Torres, Weber, Uytingco, et al., 2020). The performed modifications allow spatialLIBD to use objects of the VisiumExperiment class, which is designed to specifically store spatial transcriptomics data (Righelli and Risso, 2020).