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Fabian Theis

ML in Single-Cell Genomics

Theis Lab

We work on a variety of machine learning (ML) topics in the context of computational biology. We apply state-of-the-art ML algorithms and develop novel methods for solving complex biological and medical questions.

We work on a variety of machine learning (ML) topics in the context of computational biology. We apply state-of-the-art ML algorithms and develop novel methods for solving complex biological and medical questions.

Our team

Fabian Theis

Prof. Dr. Dr. Fabian Theis

Principal Investigator

Fabiola Curion

Postdoc

Leander Dony

Postdoc

Lennard Halle

Postdoc

Ignacio Ibarra

Postdoc

Sara Jiménez

Postdoc

Sergio Marco Salas

Postdoc

Niklas Schmacke

Postdoc

Larsen Vornholz

Postdoc

Philipp Angerer

Software engineer

Severin Dicks

Software engineer

Ilan Gold

Software engineer

Mohammed Abid Abrar

PhD candidate

Mayar Ali

PhD candidate

Mojtaba Bahrami

PhD candidate

Sören Becker

PhD candidate

Ali Oğuz Can

PhD candidate

Faye Chong

PhD candidate

Michael Dammann

PhD candidate

Francesca Drummer

PhD candidate

Jan Engelmann

PhD candidate

Felix Fischer

PhD candidate

Manuel Gander

PhD candidate

Robert Gutgesell

PhD candidate

Soroor Hediyeh-Zadeh

PhD candidate

Leon Hetzel

PhD candidate

Lukas Heumos

PhD candidate

Eva Holtkamp

PhD candidate

Kemal Inecik

PhD candidate

Yuge Ji

PhD candidate

Raphael Kfuri-Rubens

PhD candidate

Dominik Klein

PhD candidate

Merel Kuijs

PhD candidate

Louis Kümmerle

PhD candidate

Christopher Lance

PhD candidate

Katharina Limbeck

PhD candidate

Anastasia Litinetskaya

PhD candidate

Anna Litovskikh

PhD candidate

Daniele Lucarelli

PhD candidate

Laura Martens

PhD candidate

Mariia Minaeva

PhD candidate

Amir Ali Moinfar

PhD candidate

Ghaith Mqawass

PhD candidate

Michaela Müller

PhD candidate

Alessandro Palma

PhD candidate

Shrey Parikh

PhD candidate

Datta Prakki

PhD candidate

Sabrina Richter

PhD candidate

Till Richter

PhD candidate

Eljas Roellin

PhD candidate

Maiia Schulman

PhD candidate

Lisa Sikkema

PhD candidate

Perrine Simon

PhD candidate

Irena Sliskovic

PhD candidate

Daniel Strobl

PhD candidate

Artur Szalata

PhD candidate

Alejandro Tejada

PhD candidate

Tim Treis

PhD candidate

Weixu Wang

PhD candidate

Philipp Weiler

PhD candidate

Zihe Zheng

PhD candidate

Mohammed Zidane

PhD candidate

Lea Zimmerman

PhD candidate

Chelsea Bright

Research assistant

Fatemeh Hashemi

Research assistant

Vadim Nazarov

Visiting researcher

Lorenzo Consoli

Intern

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. These advances provide new insights into key biological processes and drive the need for innovative computational tools. In our lab, we develop cutting-edge 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 Atlassing

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 and data from both healthy and diseased individuals. In our lab, we utilize single-cell computational methods to build and analyze large single-cell tissue atlasses. Another important focus of our lab is data analysis. In collaboration with experimental biologists, we apply computational tools to answer pressing biological questions using single-cell or spatial data.

Publications

Read more

2024 Review in Cell

Bunne, C. ; Roohani, Y. ; Rosen, Y. ; Gupta, A. ; Zhang, X. ; Roed, M. ; Alexandrov, T. ; AlQuraishi, M. ; Brennan, P. ; Burkhardt, D.B. ; Califano, A. ; Cool, J. ; Dernburg, A.F. ; Ewing, K. ; Fox, E.B. ; Haury, M. ; Herr, A.E. ; Horvitz, E. ; Hsu, P.D. ; Jain, V. ; Johnson, G.R. ; Kalil, T. ; Kelley, D.R. ; Kelley, S.O. ; Kreshuk, A. ; Mitchison, T. ; Otte, S. ; Shendure, J. ; Sofroniew, N.J. ; Theis, F.J. ; Theodoris, C.V. ; Upadhyayula, S. ; Valer, M. ; Wang, B. ; Xing, E. ; Yeung-Levy, S. ; Zitnik, M. ; Karaletsos, T. ; Regev, A. ; Lundberg, E. ; Leskovec, J. ; Quake, S.R.

How to build the virtual cell with artificial intelligence: Priorities and opportunities.

Contact us

Fabian Theis

Prof. Dr. Dr. Fabian Theis

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