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

Abstract 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 dete...

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Autores principales:	Leonardo Morelli, Valentina Giansanti, Davide Cittaro
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9d19a496d2194c8d828c98bed88f7c3a
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spelling	oai:doaj.org-article:9d19a496d2194c8d828c98bed88f7c3a2021-12-05T12:08:42ZNested Stochastic Block Models applied to the analysis of single cell data10.1186/s12859-021-04489-71471-2105https://doaj.org/article/9d19a496d2194c8d828c98bed88f7c3a2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04489-7https://doaj.org/toc/1471-2105Abstract 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.Leonardo MorelliValentina GiansantiDavide CittaroBMCarticleComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Leonardo Morelli Valentina Giansanti Davide Cittaro Nested Stochastic Block Models applied to the analysis of single cell data
description	Abstract 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.
format	article
author	Leonardo Morelli Valentina Giansanti Davide Cittaro
author_facet	Leonardo Morelli Valentina Giansanti Davide Cittaro
author_sort	Leonardo Morelli
title	Nested Stochastic Block Models applied to the analysis of single cell data
title_short	Nested Stochastic Block Models applied to the analysis of single cell data
title_full	Nested Stochastic Block Models applied to the analysis of single cell data
title_fullStr	Nested Stochastic Block Models applied to the analysis of single cell data
title_full_unstemmed	Nested Stochastic Block Models applied to the analysis of single cell data
title_sort	nested stochastic block models applied to the analysis of single cell data
publisher	BMC
publishDate	2021
url	https://doaj.org/article/9d19a496d2194c8d828c98bed88f7c3a
work_keys_str_mv	AT leonardomorelli nestedstochasticblockmodelsappliedtotheanalysisofsinglecelldata AT valentinagiansanti nestedstochasticblockmodelsappliedtotheanalysisofsinglecelldata AT davidecittaro nestedstochasticblockmodelsappliedtotheanalysisofsinglecelldata
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Nested Stochastic Block Models applied to the analysis of single cell data

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