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Helmholtz Munich | ©Fabian Theis
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ML in single cell genomics

Theis Lab

About us

We work on a variety of topics related to Machine Learning in the context of computational biology. We apply existing state-of-the-art Machine Learning algorithms and develop novel methods tailored towards solving complex biological and medical questions.

Single-cell technologies, such as single-cell RNA sequencing (scRNA-seq), have increased the resolution achieved in the study of cellular phenotypes, allowing measurements of thousands of different genes in thousands of individual cells. This has created an opportunity to begin understanding the dynamics of the prime biological processes undergone by cells, while requiring unique computational tools. In our lab, we develop novel and innovative computational methods for single-cell data analysis.

Imaging and spatial molecular profiling techniques allow us to assay morphological and molecular markers in situ. These experimental techniques provide a toolkit to investigate tissue biology at an unprecedented resolution. Computational tools are needed for the analysis of such data. We're developing analytical tools and data infrastructure for imaging and spatial molecular data, as well as modeling approaches to disentangle spatial components of cellular and tissue variation.

 

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. In our lab, we utilize single-cell computational methods to build and benchmark large single-cell tissue atlasses. We also collaborate with experimental biologists on the analysis of their single-cell data.

We work on a variety of topics related to Machine Learning in the context of computational biology. We apply existing state-of-the-art Machine Learning algorithms and develop novel methods tailored towards solving complex biological and medical questions.

Single-cell technologies, such as single-cell RNA sequencing (scRNA-seq), have increased the resolution achieved in the study of cellular phenotypes, allowing measurements of thousands of different genes in thousands of individual cells. This has created an opportunity to begin understanding the dynamics of the prime biological processes undergone by cells, while requiring unique computational tools. In our lab, we develop novel and innovative computational methods for single-cell data analysis.

Imaging and spatial molecular profiling techniques allow us to assay morphological and molecular markers in situ. These experimental techniques provide a toolkit to investigate tissue biology at an unprecedented resolution. Computational tools are needed for the analysis of such data. We're developing analytical tools and data infrastructure for imaging and spatial molecular data, as well as modeling approaches to disentangle spatial components of cellular and tissue variation.

 

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. In our lab, we utilize single-cell computational methods to build and benchmark large single-cell tissue atlasses. We also collaborate with experimental biologists on the analysis of their single-cell data.

Our team

Prof. Dr. Dr. Fabian Theis

Institute Director and Research Group Leader View profile

Dr. Dr. Alexander Wolf

Guest Scientist

Sophie Tritschler

PhD candidate

David Fischer

PhD candidate

Marius Lange

PhD candidate

Volker Bergen

PhD candidate

Mohammad Lotfollahi

PhD candidate

Subarna Palit

PhD candidate

Dr. Malte Lücken

Postdoc

Dr. Hananeh Aliee

Postdoc

Leander Dony

PhD candidate

Giovanni Palla

PhD candidate

Dr. Luke Zappia

Postdoc

Meshal Ansari

PhD candidate

Lisa Sikkema

PhD candidate

Carlo De Donno

PhD candidate

Dr. Ignacio Ibarra

Postdoc

Sabrina Richter

PhD candidate

Lukas Heumos

PhD candidate

Philipp Weiler

PhD candidate

Yuge Ji

PhD candidate

Dr. Fabiola Curion

Postdoc

Dr. Carlos Talavera-Lopez

Principal Investigator

Karin Hrovatin

PhD candidate

Nastassya Horlava

PhD candidate

Laura Martens

PhD candidate

Dr. Amit Frishberg

Postdoc

Till Richter

PhD candidate

Merel Kuijs

PhD candidate

Kemal Inecik

PhD candidate

Dominik Klein

PhD candidate

Soroor Hediyeh-Zadeh

PhD candidate

Anastasia Litnetskaya

PhD candidate

Felix Fischer

PhD candidate

Isaac Virshup

Software Engineer

Daniel Strobl

PhD candidate

Dr. Hannah Spitzer

Postdoc

Our projects

Single-Cell Methodologies

Single-cell technologies, such as single-cell RNA sequencing (scRNA-seq), have increased the resolution achieved in the study of cellular phenotypes, allowing measurements of thousands of different genes in thousands of individual cells. This has created an opportunity to begin understanding the dynamics of the prime biological processes undergone by cells, while requiring unique computational tools. In our lab, we develop novel and innovative computational methods for single-cell data analysis.

Spatial Transcriptomics and Imaging

Imaging and spatial molecular profiling techniques allow us to assay morphological and molecular markers in situ. These experimental techniques provide a toolkit to investigate tissue biology at an unprecedented resolution. Computational tools are needed for the analysis of such data. We're developing analytical tools and data infrastructure for imaging and spatial molecular data, as well as modeling approaches to disentangle spatial components of cellular and tissue variation.

Data Analysis and Benchmarking

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. In our lab, we utilize single-cell computational methods to build and benchmark large single-cell tissue atlasses. We also collaborate with experimental biologists on the analysis of their single-cell data.

Publications

Read more

2022 Letter to the Editor in Clinical and Translational Allergy

Slob, E.M.A. ; Faiz, A. ; van Nijnatten, J. ; Vijverberg, S.J.H. ; Longo, C. ; Kutlu, M. ; Chew, F.T. ; Sio, Y.Y. ; Herrera-Luis, E. ; Espuela-Ortiz, A. ; Perez-Garcia, J. ; Pino-Yanes, M. ; Burchard, E.G. ; Potocnik, U. ; Gorenjak, M. ; Palmer, C. ; Maroteau, C. ; Turner, S. ; Verhamme, K. ; Karimi, L. ; Mukhopadhyay, S. ; Timens, W. ; Hiemstra, P.S. ; Pijnenburg, M.W. ; Neighbors, M. ; Grimbaldeston, M.A. ; Tew, G.W. ; Brandsma, C.A. ; Berce, V. ; Aliee, H. ; Theis, F.J. ; Sin, D.D. ; Li, X. ; van den Berge, M. ; Maitland-van der Zee, A.H. ; Koppelman, G.H.

Association of bronchial steroid inducible methylation quantitative trait loci with asthma and chronic obstructive pulmonary disease treatment response.

Contact us

Prof. Dr. Fabian Theis

Director of the Computational Health Center, Director of the Institute for Computational Biology

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