Press Release

Machine Learning / Computational Biology

Clear sight in the data fog with PAGA

Experimental molecular assays with single-cell resolution generate big and complex data. Researchers at Helmholtz Zentrum München and the Technical University of Munich are now presenting their computer algorithm PAGA*. They create data-driven, easily interpretable maps that reveal cellular processes and fates in complex contexts. Their paper has been published in Genome Biology.

PAGA based embedding of the developmental trajectories in the Zebrafish embryo, colored by timepoint. © Original data from Wagner et al., Science (2018).

The fate of individual cells in the body is relevant in many ways. Researchers want to study developmental processes and understand how diseases progress. “Experiments generate large data sets, which is to say big data,” explains Professor Fabian Theis, Director of the Institute of Computational Biology (ICB) at Helmholtz Zentrum München and professor of Mathematical Modelling of Biological Systems at the Technical University of Munich (TUM). Researchers gather information not only about cells per se** but also about their interactions with other cells and other tissue types. “Previously, however, it was not possible to model complex processes at the cellular level in a clear and comprehensible manner.”

PAGA interprets big data

So far, researchers have taken two approaches to data analysis. Either they searched for cells with similar properties and grouped them (clustering), or they described the timing of cells along their developmental pathways (trajectory inference). “If you look at the data through these very different lenses, divergent and unclear interpretations inevitably arise,” adds Alex Wolf, who until recently headed a machine learning team at the ICB. “PAGA does everything that clustering and trajectory inference can do in a single analysis, with a single method and with a single consistent modeling approach.” Depending on the desired resolution, the tool groups cells by type (such as skin cells) and biological state (such as cells in undergoing mitosis) and reveals transitions between cell types and states.

Use in research

In recent months, several articles have been published that show the possibilities PAGA opens up. Mireya Plass of the Max Delbrück Center of Molecular Medicine within the Helmholtz Association together with Wolf and colleagues reconstructed the first cellular lineage tree of an adult animal*** – an achievement the journal Science hailed as one of the foremost scientific breakthroughs of 2018****. Recently, a team headed by Blanca Pijuan-Sala of Cambridge University used PAGA to reconstruct the developmental processes of a mouse embryo*****. Other papers show that PAGA delivers important results in a clinical context. Using PAGA to determine the lineages of intestinal cells, researchers at the Broad Institute of MIT and Harvard gained an understanding of the different cellular contributions to chronic inflammatory bowel disease******. Theis also sees great future potential in the tool: “Basically, any biological phenomenon that can be attributed to a cellular process can be analyzed with PAGA as soon as the data are available.”

Further information

Original publication:
Wolf FA et al (2019): PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells, doi: 10.1186/s13059-019-1663-x, Link: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1663-x

* PAGA is short for partition-based graph abstraction (PAGA), a method that reconciles various approaches such as cell grouping by properties and development over time.

** This includes the transcriptome (the sum total of all active genes), the proteome (the sum total of all proteins formed), morphology (the appearance of a cell) and the epigenome (the sum total of all changes to DNA and histones, special protein in the cell nucleus). 

*** Plass M et al (2018): Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics, doi: 10.1126/science.aaq1723

**** Link: https://vis.sciencemag.org/breakthrough2018/

***** Pijuan-Sala B et al (2019): A single-cell molecular map of mouse gastrulation and early organogenesis, doi: 10.1038/s41586-019-0933-9

****** Smillie CS et al, Rewiring of the cellular and inter-cellular landscape of the human colon during ulcerative colitis, doi: 10.1101/455451

As German Research Center for Environmental Health, Helmholtz Zentrum München pursues the goal of developing personalized medical approaches for the prevention and therapy of major common diseases such as diabetes mellitus, allergies and lung diseases. To achieve this, it investigates the interaction of genetics, environmental factors and lifestyle. The Helmholtz Zentrum München has about 2,500 staff members and is headquartered in Neuherberg in the north of Munich. Helmholtz Zentrum München is a member of the Helmholtz Association, a community of 19 scientific-technical and medical-biological research centers with a total of about 37,000 staff members. 

The Institute of Computational Biology (ICB) develops and applies methods for the model-based description of biological systems, using a data-driven approach by integrating information on multiple scales ranging from single-cell time series to large-scale omics. Given the fast technological advances in molecular biology, the aim is to provide and collaboratively apply innovative tools with experimental groups in order to jointly advance the understanding and treatment of common human diseases. 

The Technical University of Munich (TUM) is one of Europe’s leading research universities, with around 550 professors, 41,000 students, and 10,000 academic and non-academic staff. Its focus areas are the engineering sciences, natural sciences, life sciences and medicine, combined with economic and social sciences. TUM acts as an entrepreneurial university that promotes talents and creates value for society. In that it profits from having strong partners in science and industry. It is represented worldwide with the TUM Asia campus in Singapore as well as offices in Beijing, Brussels, Cairo, Mumbai, San Francisco, and São Paulo. Nobel Prize winners and inventors such as Rudolf Diesel, Carl von Linde, and Rudolf Mößbauer have done research at TUM. In 2006 and 2012 it won recognition as a German "Excellence University." In international rankings, TUM regularly places among the best universities in Germany.

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