GigaSOM.jl: High-perfomance clustering and visualization of huge cytometry datasets

Kratochvil, M.; Hunewald, O.; Heirendt, L.; Verissimo, V.; Vondrasek, J.; Satagopam, V. P.; Schneider, R.; Trefois, C.; Ollert, M.

doi:10.1101/2020.08.03.234187

bioRxiv · 10.1101/2020.08.03.234187

GigaSOM.jl: High-perfomance clustering and visualization of huge cytometry datasets

Kratochvil, M.·Hunewald, O.·Heirendt, L.·Verissimo, V.·Vondrasek, J.·Satagopam, V. P.·Schneider, R.·Trefois, C.·Ollert, M.

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Abstract

BackgroundThe 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. ResultsWe 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. ConclusionsGigaSOM.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 pointsO_LIGigaSOM.jl improves the applicability of FlowSOM-style single-cell cytometry data analysis by increasing the acceptable dataset size to billions of single cells. C_LIO_LISignificant speedup over current methods is achieved by distributed processing and utilization of efficient algorithms. C_LIO_LIGigaSOM.jl package includes support for fast visualization of multidimensional data. C_LI

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Kratochvil, M., Hunewald, O., Heirendt, L., Verissimo, V., Vondrasek, J., Satagopam, V. P., Schneider, R., Trefois, C., Ollert, M.. 2020-08-04. GigaSOM.jl: High-perfomance clustering and visualization of huge cytometry datasets. https://doi.org/10.1101/2020.08.03.234187

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