Even when appearing perfectly homogeneous on a morphological basis, tissues can be substantially heterogeneous in single-cell molecular expression. As such heterogeneities might govern the regulation of cell fate, one is interested in quantifying them in a given tissue. In this project, we infer single-cell regulatory states from expression measurements taken from small groups of cells. This averaging-and-deconvolution approach allows to quantify single-cell regulatory heterogeneities while avoiding the measurement noise of global single-cell techniques.
This webtool allows you to generate synthetic data from our stochastic profiling models as well as analyze your own data files. It serves as an interface to the R package stochprofML. For more details, please visit the Stochastic Profiling Webpage. For questions and suggestions, please contact Christiane Fuchs.