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).
By Nick Eagles As part of recent LIBD work with spatial gene expression, I recently was recommended the tool Space Ranger, which provides software pipelines walking Visium spatial RNA-seq samples through the steps we ultimately need to explore gene expression coupled with spatial information. In this blog post, I’ll explain how to start using Space Ranger at JHPCE, focusing heavily on the set-up details relevant to this cluster in particular.
HAPPY HOLIDAYS!!!🎉⛄🎆🍾❄ In the spirit of the coming new year and new beginnings, we created a tutorial for getting started or restarted with R. If you are new to R or have dabbled in R but haven’t used it much recently, then this post is for you. We will focus on data classes and types, as well as data wrangling, and we will provide basic statistics and basic plotting examples using real data.
By Amy Peterson Studying genetic differential expression using postmortem human brain tissue requires an understanding of the effect brain tissue degradation has on genetic expression. Particularly when brain tissue degradation confounds1 the differences in gene expression levels between subject groups. This problem of confounding necessitates measures from a control dataset of postmortem tissue from individuals who do not have the outcome of interest. Doing so provides a comparative measure of the impact of tissue degradation on expression that can then be used in a case-control study to examine the impact of the outcome of interest on genetic expression.
Every six months the Bioconductor project releases it’s new version of packages. This allows developers a time window to try out new methods and test them rigorously before releasing them to the community at large. It also means that this is an exciting time 🎉. With every release there are dozens1 of new software packages. Bioconductor version 3.8 was just released on Halloween: October 31st, 2018. Thus, this is the perfect time to browse through their descriptions and find out what’s new that can be of use to your research.
To carry on our momentum from a few weeks ago from our useR!2018 remote notes blog post, this time we will be summarizing the Demystifying Data Sience 2018 conference for which you can register for free. We are just following David Robinson’s advice to blog all the time! Conference overview We got interested in this conference1 thanks to tweets like these ones that highlight that: data scientists are young! specialists are more in demand!