page_title: Flow cytometry bioinformatics
section_title: Identifying cell populations
hierarchical_section_title: Flow cytometry bioinformatics / Identifying cell populations
caption_attribution_description: English: Results of the FlowCAP - Flow Cytometry: Critical Assessment of Population Identification Methods competition, comparing the performance of flow cytometry clustering algorithms.
context_page_description: Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing and analyzing flow cytometry data using extensive computational resources and tools.
  Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning.
  Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results.
  Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps.
context_section_description: The complexity of raw flow cytometry data (dozens of measurements for thousands to millions of cells) makes answering questions directly using statistical tests or supervised learning difficult. Thus, a critical step in the analysis of flow cytometric data is to reduce this complexity to something more tractable while establishing common features across samples. This usually involves identifying multidimensional regions that contain functionally and phenotypically homogeneous groups of cells. This is a form of cluster analysis.  There are a range of methods by which this can be achieved, detailed below.
caption: Two-dimensional scatter plots covering all three combinations of three chosen dimensions.  The colours show the comparison of consensus of eight independent manual gates (polygons) and automated gates (colored dots). The consensus of the manual gates and the algorithms were produced using the CLUE package.[25] Figure reproduced from.[26]
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