Nested Stochastic Block Models applied to the analysis of single cell data

• Published in: BMC bioinformatics Vol. 22; no. 1; pp. 576 - 19
• Main Authors: Morelli, Leonardo, Giansanti, Valentina, Cittaro, Davide
• Format: Journal Article
• Language: English
• Published: London BioMed Central 30.11.2021; BioMed Central Ltd; BMC
• Subjects: Algorithms; Analysis; Bioinformatics; Biomedical and Life Sciences; Cell physiology; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Datasets; Learning models (Stochastic processes); Libraries; Life Sciences; Methods; Microarrays; Modularity; Molecular biology; Noise; Optimization; Probability; RNA sequencing; Schist; Single-Cell Analysis; Stochastic models; Italy
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-04489-7

Single cell profiling has been proven to be a powerful tool in molecular biology to understand the complex behaviours of heterogeneous system. The definition of the properties of single cells is the primary endpoint of such analysis, cells are typically clustered to underpin the common determinants that can be used to describe functional properties of the cell mixture under investigation. Several approaches have been proposed to identify cell clusters; while this is matter of active research, one popular approach is based on community detection in neighbourhood graphs by optimisation of modularity. In this paper we propose an alternative and principled solution to this problem, based on Stochastic Block Models. We show that such approach not only is suitable for identification of cell groups, it also provides a solid framework to perform other relevant tasks in single cell analysis, such as label transfer. To encourage the use of Stochastic Block Models, we developed a python library, schist , that is compatible with the popular scanpy framework.