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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 tailored towards 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 tailored towards solving complex biological and medical questions.

Our team

Dr. Hananeh Aliee

Postdoc

Dr. Fabiola Curion

Postdoc

Dr. Ignacio Ibarra

Postdoc

Dr. Sara Jiménez

Postdoc

Dr. Marius Lange

Postdoc

Dr. Mohammad Lotfollahi

Postdoc

Dr. Niklas Schmacke

Postdoc

Dr. Hannah Spitzer

Postdoc

Dr. Luke Zappia

Postdoc

Isaac Virshup

Software engineer

Ciro Ramirez Suastegui

Research assistant

Mayar Ali

PhD candidate

Sören Becker

PhD candidate

Faye Chong

PhD candidate

Carlo De Donno

PhD candidate

Leander Dony

PhD candidate

Jan Engelmann

PhD candidate

Felix Fischer

PhD candidate

Robert Gutgesell

PhD candidate

Soroor Hediyeh-Zadeh

PhD candidate

Leon Hetzel

PhD candidate

Lukas Heumos

PhD candidate

Eva Holtkamp

PhD candidate

Nastassya Horlava

PhD candidate

Karin Hrovatin

PhD candidate

Kemal Inecik

PhD candidate

Yuge Ji

PhD candidate

Dominik Klein

PhD candidate

Louis Kümmerle

PhD candidate

Merel Kuijs

PhD candidate

Christopher Lance

PhD candidate

Katharina Limbeck

PhD candidate

Anastasia Litinetskaya

PhD candidate

Daniele Lucarelli

PhD candidate

Laura Martens

PhD candidate

Amir Ali Moinfar

PhD candidate

Michaela Müller

PhD candidate

Giovanni Palla

PhD candidate

Alessandro Palma

PhD candidate

Sabrina Richter

PhD candidate

Till Richter

PhD candidate

Sergei Rybakov

PhD candidate

Anna Schaar

PhD candidate

Lisa Sikkema

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

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

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Contact us

Prof. Dr. Fabian Theis

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