Parameter optimization for stable clustering using FlowSOM: a case study from CyTOF.

ABSTRACT: High-dimensional cell phenotyping is a powerful tool to study molecular and cellular changes in health and diseases. CyTOF enables high-dimensional cell phenotyping using tens of surface and intra-cellular markers. To utilize the full potential of CyTOF, we need advanced clustering and machine learning methodologies to enable automated gating of the complex data. Here we show that critical modifications to a machine learning based FlowSOM package and precise parameter optimization can enable us to reliably analyze the complex CyTOF data. We show the impact of key parameters on clustering outcomes while addressing bugs within the publicly available package. We modified the FlowSOM pipeline to fix the bugs, enable scalability to handle large datasets and perform parameter optimization. We fu

SUBMITTER: Tao W 

PROVIDER: S-EPMC11497637 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature