Single-cell technologies have positioned themselves at the forefront of biomedical research. These platforms allow researchers to bypass the uncertainties of bulk data and instead interrogate biological systems at a level of detail that was previously unreachable. by Ruairi J MacKenzie
Marilyn Matz, CEO of Paradigm4, and Zachary Pitluk, Vice President of Life Sciences at Paradigm4, explain why scalable data science platforms are key to supporting integrated analysis of single-cell genomic data sets.
Single-cell technologies have positioned themselves at the forefront of biomedical research. These platforms allow researchers to bypass the uncertainties of bulk data and instead interrogate biological systems at a level of detail that was previously unreachable. But the value of these systems risks being impaired by bottlenecks in data analysis
Metabolomics—often referred to as the youngest of the omics—provides key insight into phenotype. However, bulk metabolomics requires the homogenization of the sample and is thus unable to discern metabolic differences at a cellular level.
