Seminar: Data exploration of single-cell landscapes

The growing field of single-cell sequencing is enabling DNA and RNA analysis to reach a deeper, finer level than what is traditionally obtained in bulk tissue sequencing. Cell-type and cell-state specific genomic signals, once buried and diluted by averaging the measures over a whole cell population, are now ready to be discovered; the hard assumption of a “average” cell representing the cell population of a given sample is rapidly fading in favor of a more realistic representation of the complex mosaic of diverse cells and diverse cell states. As these novel techniques for data extraction are slowly defining new standards, a plethora of data analysis tools and algorithms are being published and utilized. The techniques and parameters chosen for data analysis are as important as the ones chosen for wet lab protocols. Yet, especially in single-cell biology, scientists are tempted to apply off-the-shelf algorithms for data interpretation, without the same rigor they apply to lab procedures. In this seminar we will discuss the tricks of the trade of single-cell data analysis, including hands-on examples and caveats from bleeding edge applications. We will also discuss ideas and potential developments for machine learning applications in single-cell data.