Category: Data Analysis and Informatics
Single cell sequencing will increase our ability to identify 3 orders of magnitude more phenotypes and expression patterns responsible for cellular development, communication, the initiation, persistence, or resolution of disease, and the actual delivery of precision medicine. However, current analysis of single cell data using R, Bowtie, BED-tools etc., is hampered by the disjointed nature of relevant tools, incompatible file types, disparate normalization processes, and command line interfaces. Worse, analysis of single cell data often requires a resident bioinformatician, not necessarily an expert in the biological process being tested, to derive critical insights. Demolishing these roadblocks requires consolidation of pertinent tools, standardization of file types, flexibility to modulate normalization methods, and elimination of the technical “middle-man”, all in an intuitive and concise environment.
To enhance and broaden the single cell analysis portal and empower the individuals driving the research, we have developed a new data standard for differential gene expression sharing as well as a new analysis paradigm for gene expression analysis, which together enable bench scientists to derive single cell insight. Herein, we used a publicly available data set from a study of healthy human pancreatic islet cells. We show using both directed or unsupervised analyses that a two-mode analysis (cells to genes) or bottom-up (genes to cells) approaches, reducing bias and providing a new paradigm for single-cell interrogation. Using t-SNE and PCA, we identify new cell populations and gene clusters that have not previously been described in the literature. In addition, we show a reduction in analysis time from >4 hours to less than 30 seconds. Furthermore, we show that layers of depth beyond traditional immunological phenotyping are possible through this approach and show multiple subsets of pancreatic islet cells extant in 4 patient samples. Thus, we present an innovative, rapid, intuitive analysis paradigm to harness the power of single cell sequencing.
Andreas Panopoulos– Application Scientist, Flowjo, Llc, Ashland, OR
Andreas, who goes by the nickname "Jack" is currently an application scientist for FlowJo, LLC. His education credentials include the following: a BS in microbiology from Cal Poly Pomona, and a Ph. D in molecular microbiology and immunology from the University of Southern California. Prior to coming to FlowJo, LLC., he completed a post-doctoral position at the Sanford-Burnham Institute for Medical research in La Jolla, CA. Jack has a keen interest single-cell science, immunology and gene therapy. Outside of his scientific pursuits he's an avid scuba diver, spear-fisher and beach volleyball enthusiast.