In the intricate world of mass cytometry data, clustering algorithms serve as our trusty guides, helping us navigate the complex cellular landscapes and identify distinct populations. Let’s embark on a journey through the diverse ecosystem of clustering techniques, from the venerable classics to the cutting-edge innovators.
Hierarchical Clustering: The Family Tree of Cells
Hierarchical clustering, one of the oldest techniques in the book, builds a tree-like structure of data points. It’s like creating a family tree for your cells, grouping them based on their similarities.
There are two main approaches:
- Agglomerative: Start with each cell as its own cluster and progressively merge the closest ones.
- Divisive: Begin with all cells in one cluster and recursively divide them.
While not always the fastest, hierarchical clustering provides an intuitive visualization of relationships between cell populations. It’s particularly useful for exploratory analysis and for understanding the overall structure of your data.
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I like FlowSOM, its algorithm felt very powerful... but its visualization tool? Not that much. So, there I was, caught between my love for FlowSOM's clustering prowess and my desperate need for eye-catching visuals. It was like dating someone brilliant but with a terrible sense of fashion. Enter my knight in shining R code - a colleague who helped me create a visualization package that could make even the dullest data strut its stuff. Suddenly, with just a few lines of code, I could have my cake and eat it too: FlowSOM's brains with Hollywood-worthy good looks. And just like that, I took my most significant step in R programming by developing Cytofast in R...
