We are researchers at LIBD that frequently use R and other tools. We blog about R packages we are interested in, how to do guides, and occasionally our own open-source software.
Since 2020 we have also been uploading videos to YouTube and our meeting schedule is public. We have a Google Group that is reserved to our members. If you have suggestions for future sesssions, please let us know!.
We would love feedback from the community and hope that our blog posts and videos will help researchers at LIBD and elsewhere. There are many packages and functions we don’t know about, plus the R community is always growing. This is part of our efforts to continue learning about R.
Check the LIBD careers page. You might also be interested in checking out the R/Bioconductor-powered Team Data Science LIBD team.
If you are commenting or participating in any way, please follow our code of conduct. Even if you are reading about us from R Bloggers, R Weekly, or elsewhere.
Our website is partially inspired by R-Ladies NYC. The R logo is licensed CC-BY-SA 4.0.
Posts with the rstats category can also be found at RBloggers and R Weekly.
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.
We recognize that our code of conduct is outdated. We now also follow the Bioconductor Project Code of Conduct, so please read that one first. Thank you!
The LIBD rstats club is committed to providing a welcoming and harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), or technology choices. We do not tolerate harassment of website participants in any form. Sexual language and imagery is not appropriate for any venue related to this website, including blog posts, talks, workshops, parties, Twitter and other online media. Website participants violating these rules may be sanctioned or expelled from the website at the discretion of the LIBD rstats club members.
This code of conduct applies to all participants, including LIBD rstats club members and applies to all modes of interaction, both in-person and online, including LieberInstitute GitHub project repos, the LIBD rstats club comments section, Slack channels and Twitter.
LIBD rstats club participants agree to:
If any participant engages in harassing behavior, the LIBD rstats club organizers may take any lawful action we deem appropriate, including but not limited to warning the offender or asking the offender to leave the website. (If you feel you have been unfairly accused of violating this code of conduct, you should contact the LIBD rstats club organizers with a concise description of your grievance.)
The above text has been modified from the rOpenSci 2018 unconference code of conduct, which in turn states that parts of the text are licensed CC BY-SA 4.0. Credit to SRCCON. Also inspired by the Ada Initiative’s “how to design a code of conduct for your community.”