Taking feedback from customers is the best way to improve any product, a practice we take seriously at Elucidata. Recently, while chatting with one of our customers, we realized that across the drug discovery process, researchers use public data to build and validate hypotheses.
Researchers fetch data from multiple public data sources such as TCGA, UK BioBank, cBioportal, etc., & perform the meta-analysis. More often than not, they analyze experimental data differently than what has been published.
One critical feedback we received from the researchers that use Polly, especially the Bioinformaticians working on different omics datasets, was the repeated need to group samples from multiple OmixAtlases such that it becomes easy to analyze data from different datasets/across repositories.
So voila! Here we are with the incredible ‘Cohorting’ feature for Polly that helps group data.
Put simply, you can now group datasets or samples based on metadata of interest on Polly.
Cohort studies are a powerful tool for any multi-omics comparative analysis. For example, researchers can look at baseline multi-omics data for people without a particular disease and examine the factors that differed between those who developed a condition and those who did not.
Moreover, you can build groups aka cohorts, by combining datasets that can help you identify patterns/gene expression imbalances and so much more. The possibility of detailed analysis across multiple datasets will allow users to reach actionable insights so much faster!
A user can use the cohorting feature to do one or more of the following:
Here’s a video to give you a glimpse of how cohorting using these features looks on Polly Python!
We’ve also included these exciting features to our To-Do to enrich user experience:
Last, but not the least- few more that we would love for our users to have in the future:
Reach out to your CSM, if there’s a particular feature that’s important to you and you’d like us to prioritize.
Yes, Absolutely! Here’s a link to a demo; If you need more help, just reach out to your Customer Success Manager, who’d be more than happy to set up a call to take you through the feature one on one. Or just click here to book a meeting.
Get the latest insights on Biomolecular data and ML