flowPeaks

"flowPeaks" is an algorithm to address the challenges of unsupervised clustering in flow cytometry data, which traditionally relies on finite mixture models or histogram-based spatial exploration. These conventional approaches face limitations such as computational inefficiency, difficulty in identifying clusters with irregular shapes, and challenges in handling high-dimensional data due to exponential increases in computational time and memory requirements, alongside unreliable histogram estimation.

By ingeniously merging concepts from finite mixture models and histogram spatial exploration, flowPeaks offers a solution that is not only capable of directly processing high-dimensional data but also proficient in identifying clusters of irregular shapes. The algorithm begins by employing the K-means algorithm with a substantially large K value to divide the cell population into numerous small clusters. This step facilitates generating a smoothed density function using the finite mixture model approach. Subsequently, flowPeaks exhaustively searches all local peaks within the density function, assigning cells to clusters based on their proximity to these peaks.