Who is this gene and what does it do? A toolkit for munging transcriptomics data in python

Transcriptional regulation is extremely complicated. Unfortunately, so is working with transcriptional data. Genes can be referred to using a multitude of different identifiers and are assigned to an ever increasing number of categories. Gene expression data may be available in a variety of units (e.g, counts, RPKMs, TPMs). Batch effects dominate signal, but metadata may not be available. Most of the tools are written in R. Here, we introduce a library, genemunge, that makes it easier to work with transcriptional data in python. This includes translating between various types of gene names, accessing Gene Ontology (GO) information, obtaining expression levels of genes in healthy tissue, correcting for batch effects, and using prior knowledge to select sets of genes for further analysis. Code for genemunge is freely available on Github (http://github.com/unlearnai/genemunge).

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CTAD 2020: Unlearn will present abstracts to demonstrate how Digital Twins enable smaller, more efficient trials


Announcing The Unlearn Opportunities Internship Program


UCSF Rosenman Institute - The Health Technology Podcast #59: Digital Twins for Clinical Trials

Charles Fisher: Digital Twins for Clinical Trials
Creating Opportunities for Students of Data Science and Business from Underrepresented Groups in STEM
Unlearn will present data from two separate abstracts demonstrating how Digital Twins can power novel trial designs and accelerate timelines.