Application of synthetic datasets in training and validation of analysis tools have led to
improvements in many decision-making tasks in a range of domains from computer vision to …

The development of flow cytometry data analysis computational tools in recent yearshas the
potential to reduce the variation arising from manual gating and improve the quality of cell …

Flow cytometry is widely used within the manufacturing of cell and gene therapies to
measure and characterise cells. Conventional manual data analysis relies heavily on …

We present a model‐based clustering method, SWIFT (Scalable Weighted Iterative Flow‐
clustering Technique), for digesting high‐dimensional large‐sized datasets obtained via …

Single-cell flow cytometry is a technology that measures the expression of several cellular
markers simultaneously for a large number of cells. Identification of homogeneous cell …

Motivation Identification of cell populations in flow cytometry is a critical part of the analysis
and lays the groundwork for many applications and research discovery. The current …

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data,
which involves storing, retrieving, organizing, and analyzing flow cytometry data using …

Flow cytometers have enabled researchers to measure 8 to 16 different cellular markers at
the single-cell level. Due to the encoded complexity in flow cytometry dataset across diverse …

Recent technological advancements have enabled the flow cytometric measurement of tens
of parameters on millions of cells. Conventional manual data analysis and bioinformatics …

Computational methods for identification of cell populations from polychromatic flow
cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the …