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

ABSTRACT:

Background

The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data with hundreds of millions of single-cell data points with >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 downsample 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

SUBMITTER: Kratochvil M 

PROVIDER: S-EPMC7672468 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature