In the last decade, the number of proteins that can be measured simultaneously with cytometry techniques has increased from about ten to thirty and more. For these larger datasets, the traditional analysis method of repetitively selecting cell populations of interest in 2D scatter plots falls short. Not all possible 2D combinations can be examined and analysis results get biased towards the expected populations. Many cells are `gated out' and never analyzed, and rarely all markers are studied for a single cell. Additionally, as more and more cell populations can be detected, it becomes harder to identify which (combinations of) cell populations can be predictive for a clinical outcome.
In our lab, we developed a computational pipeline to aid in the analysis of cytometry data. We make use of automated quality control procedures to identify incorrect measurements and batch effects. With FlowSOM, we offer a comprehensive visualization of the data. FlowSOM makes use of a self-organizing map, making it computationally very scalable, and includes an additional meta-clustering step, allowing clusters in strongly varying sizes and shapes. The model can also be used to answer questions such as `What is the immunophenotypic difference between these two groups of patients?'
The European Laboratory Research & Innovation Group
Our Vision : To provide outstanding, leading edge knowledge to the life sciences community on an open access basis