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).

Enter your email address to download paper.

Click the link to begin download.
Oops! Something went wrong while submitting the form.

Enter your email address to watch the webinar.

Click the link to watch webinar.
Oops! Something went wrong while submitting the form.
Webinars

Part 3: Innovation in Clinical Research: AI-based Drug Development Tools and the Regulatory Landscape‍

Webinars

Part 2: Faster, More Efficient Trials: Novel Trial Designs using Digital Twins‍

Webinars

Part 1: AI, Digital Twins, and the Future of Clinical Research‍

Learn about how Digital Twins are created and how they are incorporated into clinical trials to increase power, accelerate timelines, and enable patient level insights.
Watch an overview of specific use cases for Digital Twins and learn how novel trial designs with Digital Twins enable smaller trials that maintain their power.
Watch a panel discussion on the regulatory landscape where experts share perspectives on the future of AI-based drug development tools like Digital Twins.