RT Journal Article
SR Electronic
T1 GigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.08.03.234187
DO 10.1101/2020.08.03.234187
A1 Miroslav Kratochvíl
A1 Oliver Hunewald
A1 Laurent Heirendt
A1 Vasco Verissimo
A1 Jiří Vondrášek
A1 Venkata P. Satagopam
A1 Reinhard Schneider
A1 Christophe Trefois
A1 Markus Ollert
YR 2020
UL http://biorxiv.org/content/early/2020/08/04/2020.08.03.234187.abstract
AB Background The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow to easily generate data with hundreds of millions of single-cell data points with more than 40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to down-sample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena.Results We present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality-reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community, and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study.Conclusions GigaSOM.jl facilitates utilization of the commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from an massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies.Key pointsGigaSOM.jl improves the applicability of FlowSOM-style single-cell cytometry data analysis by increasing the acceptable dataset size to billions of single cells.Significant speedup over current methods is achieved by distributed processing and utilization of efficient algorithms.GigaSOM.jl package includes support for fast visualization of multidimensional data.Competing Interest StatementThe authors have declared no competing interest.