AliGater: a framework for the development of bioinformatic pipelines for large-scale, high-dimensional cytometry data

Ludvig Ekdahl; Antton Lamarca Arrizabalaga; Zain Ali; Caterina Cafaro; Aitzkoa Lopez de Lapuente Portilla; Björn Nilsson

doi:10.1093/bioadv/vbad103

AliGater: a framework for the development of bioinformatic pipelines for large-scale, high-dimensional cytometry data

Bioinform Adv. 2023 Aug 4;3(1):vbad103. doi: 10.1093/bioadv/vbad103. eCollection 2023.

Authors

Ludvig Ekdahl^{1

2}, Antton Lamarca Arrizabalaga^{1

2}, Zain Ali^{1

2}, Caterina Cafaro^{1

2}, Aitzkoa Lopez de Lapuente Portilla^{1

2}, Björn Nilsson^{1

2

3}

Affiliations

¹ Lund Stem Cell Center, Lund University, Lund 221 84, Sweden.
² Department of Laboratory Medicine, Lund University, Lund 221 84, Sweden.
³ Broad Institute, Cambridge, MA 02142, United States.

Abstract

Motivation: AliGater is an open-source framework to accelerate the development of bioinformatic pipelines for the analysis of large-scale, high-dimensional flow cytometry data. AliGater provides a Python package for automatic feature extraction workflows, as well as building blocks to construct analysis pipelines.

Results: We illustrate the use of AliGater in a high-resolution flow cytometry-based genome-wide association study on 46 immune cell populations in 14 288 individuals.

Availability and implementation: Source code and documentation at https://github.com/LudvigEk/aligater and https://aligater.readthedocs.io.