This award will be used to develop advanced methods for the analysis of single cell and spatial genomics and transcriptomics data, while providing life scientists a platform that can provide a one- stop solution that implements the best practices.

Ann Arbor, Michigan, January 30, 2024 

Advaita Bio was awarded $1.7 million from the National Institute of Health (NIH) to develop a new platform for the analysis of single-cell data. AdvaitaBio is the leader in the interpretation of high- throughput biomedical data including variant interpretation, pathway analysis, disease subtype discovery, single cell analysis, and integration across multiple data types. This research will develop advanced methods for the analysis of single cell and spatial genomics and transcriptomics data.

Advaita’s Life Scientist and Principal Investigator, Cristiana Iosef, DVM, PhD, explains, “The world of single cell analysis is currently a jungle. There are tens of packages that do various types of analysis. Researchers need to learn to code, then they need to deploy a myriad of different functions, and load the results of one package into the next one, using many different parameters every time. Life scientists have to become part-time programmers and quarter-time data scientists, just to run the software correctly. The goal of this project is to create a platform for the life scientists, entirely GUI- based, that can provide a one-stop solution that implements the best practices.” She continues: “No more wondering what methods to use at each step, no more time wasting to understand command- line parameters, and no more efforts to keep up to date with ever-changing new packages and releases. Advaita Bio’s single cell platform will allow the scientists to focus on the science, getting the best results from their data, while also saving huge amount of time and money.”

The project “A web-based platform for robust single-cell analysis, bulk data deconvolution and system- level analysis,” will be developed in collaboration with Tin Nguyen, PhD, an associate professor in Computer science and software engineering and director of Auburn University’s Bioinformatics Lab.

This software will provide new methodologies for single-cell analysis that far exceed current technologies with the ability to analyze massive data sets.

Sorin Draghici, PhD, Advaita Bio’s CEO, emphasizes other aspects of the value of of the platform: “The methods at the core of this project will allow life scientists to extract from a less expensive bulk RNASeq experiment about 80-90% of the knowledge they would get from the more expensive single cell experiment. For an experiment involving two groups and 10 samples per group, the typical lab would save more than $200K. In addition, scientists will be able to leverage hundreds of millions of dollars of existing bulk data to extract data and understand phenomena at cell-type level.”

Currently, nine of the top 10 pharmaceutical companies rely on Advaita’s state-of-the-art algorithms to solve complex problems. Advaita provides a suite of advanced analysis software to more than 13,000 registered users worldwide: iPathwayGuide, for functional interpretation of genes and proteins; iVariantGuide, for genetic variant analysis; iKnowledgeBase, a collection of knowledge and pre- analyzed data from various phenotypes, and iBioGuide, a search engine revealing connections between genes, pathways, SNPs, drugs, and more.

Single-cell software will be available for customers in Q1’24. For more information, contact customer.success@advaitabio.com.

Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R44GM152152. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.


About Advaita Bio:
Advaita Bioinformatics helps principal investigators, core facilities, and enterprise bioinformatics teams analyze and integrate omics data (transcriptomics, proteomics, methylomics, etc) and variant data (e.g. DNA-Seq) to find biomarkers, identify impacted pathways, and pinpoint putative mechanisms.
Currently, the data analysis process is slow, unreliable, expensive, and often requires multiple disjointed tools, which then provide irrelevant or incorrect results. While other solutions drop a haystack of results on you, Advaita’s advanced analysis leads the researchers straight to the needle.

The methods at the core of this project will allow life scientists to extract from a less expensive bulk RNASeq experiment about 80-90% of the knowledge they would get from the more expensive single cell experiment.