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Department of Computing Sciences
Bocconi University

Via Guglielmo Röntgen 1

20136 Milan, Italy


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Analysis of single-cell omics data

12/30/2021 14:11


Analysis of single-cell omics data

Aggiornamento: 23 gen The analysis of single-cell omics data allows for studying the molecular processes that drive normal development and the onset o

Aggiornamento: 23 gen


The analysis of single-cell omics data allows for studying the molecular processes that drive normal development and the onset of different pathologies.


Single-cell omics data experiments, especially single-cell RNA sequencing (scRNA-Seq) data, are gaining ground thanks to their ability to capture the heterogeneities that are present among the cells of the same cell-type.

One the one hand, this emerging field of research is generating a massive amount of big data that have to be analysed. On the other hand, it continuously poses new biological questions that have to be addressed. As a consequence, new computational approaches should be proposed to effectively and efficiently face these novel problems.



“Overall, our study has provided a high resolution transcriptional and chromatin accessibility map of foetal HSPCs from the liver and bone marrow that will be essential for further exploration of HSC/MPPs in the context of blood pathologies and for the purpose of regenerative medicine.”  [Ranzoni et al., Cell Stem Cell 2020]

Standard computational pipelines used to analyse scRNA-Seq data exist, though there is room for improvements. For instance, finding an effective and efficient low-dimensional representation of the data can provide better identification of known or putatively novel cell-types. To address this problem, we designed a novel pipeline based on Autoencoders, along with a new tool named scAEspy, showing that effective low-dimensional representations allow for better separating the cell-types present in scRNA-Seq data.

More information about scAEspy is available on GitLab.


Different scRNA-Seq data analyses were conducted to study the development of human hematopoiesis. Specifically, 15 scRNA-Seq experiments and 3 scATAC-Seq experiments were integrated using the latest computational approaches. In addition, completely new strategies were applied to assess qualitative and quantitative differences between the haematopoietic cells collected from the liver and femur of the analysed human foetuses. This study provides a useful framework for future investigation of human developmental haematopoiesis in the context of blood pathologies and regenerative medicine.

More information about the performed analyses can be found on GitLab.


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