A quantitative comparison of cell type deconvolution methods for immuno-oncology

Supplementary Material

Gregor Sturm (1,2,*), Francesca Finotello (3), Florent Petitprez (4,5), Jitao David Zhang (6), Jan Baumbach (1), Wolf H. Fridman (4), Markus List (7), Tatsiana Aneichyk (2)

2018-11-14

1 Introduction

This document elaborates step-by-step how we derive our results, shows supplementary figures to support our study and serves as an open-source pipeline to reproduce our results.

The source code and instructions how to run the benchmark are available from GitHub: grst/immune_deconvolution_benchmark

1.1 Author’s affiliations

1. Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354 Freising, Germany
2. Pieris Pharmaceuticals GmbH, Lise-Meitner-Straße 30, 85354 Freising, Germany
3. Biocenter, Division for Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria
4. Cordeliers Research Centre, UMRS_1138, INSERM, University Paris-Descartes, Sorbonne University, 75006 Paris, France
5. Ligue Nationale contre le Cancer, Programme Cartes d’Identité des Tumeurs, 75013 Paris, France
6. Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
7. Big Data in BioMedicine, Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354 Freising, Germany

* corresponding author