The process of data analysis is overly time-consuming and driven by manual human effort. This creates bottlenecks for scientific progress and risk of missing signals within data. Innovations in computational biology are continuously emerging to address these issues, but their accessibility is low and their implementation within organizations is lacking.
Our team works with organizations to solve these problems by developing tools or pipelines with functionality including autogating, high-dimensional analysis algorithms, signal normalization, batch processing, differential analysis, and scripts for connecting different analysis software and information systems such as electronic lab notebooks and clinical databases. Applications include biomarker discovery, immune monitoring, and high-throughput screening. Our specialty is single cell data with attention to flow and mass cytometry, RNA-seq, and imaging.
Other services include education and training designed to quickly arm scientists with a key theoretical framework for understanding and approaching high-dimensional analysis.