Topological Data Analysis of Single-Cell Hi-C Contact Maps

  • Mathieu CarrièreEmail author
  • Raúl Rabadán
Conference paper
Part of the Abel Symposia book series (ABEL, volume 15)


Due to recent breakthroughs in high-throughput sequencing, it is now possible to use chromosome conformation capture (CCC) to understand the three dimensional conformation of DNA at the whole genome level, and to characterize it with the so-called contact maps. This is very useful since many biological processes are correlated with DNA folding, such as DNA transcription. However, the methods for the analysis of such conformations are still lacking mathematical guarantees and statistical power. To handle this issue, we propose to use the Mapper, which is a standard tool of Topological Data Analysis (TDA) that allows one to efficiently encode the inherent continuity and topology of underlying biological processes in data, in the form of a graph with various features such as branches and loops. In this article, we show how recent statistical techniques developed in TDA for the Mapper algorithm can be extended and leveraged to formally define and statistically quantify the presence of topological structures coming from biological phenomena, such as the cell cyle, in datasets of CCC contact maps.



This work has been funded by NIH grants (U54 CA193313 and U54 CA209997) and Chan Zuckerberg Initiative pilot grant.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Systems BiologyColumbia University Irving Medical CenterNew YorkUSA

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