AI Article Synopsis
- The growth of flow cytometry applications has made traditional manual data analysis tools inadequate for interpreting complex, high-dimensional datasets.
- Scientists now struggle with manually identifying cell populations in datasets that can reach up to 50 dimensions, similar to challenges faced in mass cytometry.
- The paper outlines a tailored automated analysis pipeline that addresses various steps in flow cytometry data analysis, and demonstrates its application using data from the International Mouse Phenotyping Consortium (IMPC).
Article Abstract
The rapid expansion of flow cytometry applications has outpaced the functionality of traditional manual analysis tools used to interpret flow cytometry data. Scientists are faced with the daunting prospect of manually identifying interesting cell populations in 50-dimensional datasets, equalling the complexity previously only reached in mass cytometry. Data can no longer be analyzed or interpreted fully by manual approaches. While automated gating has been the focus of intense efforts, there are many significant additional steps to the analytical pipeline (e.g., cleaning the raw files, event outlier detection, extracting immunophenotypes). We review the components of a customized automated analysis pipeline that can be generally applied to large scale flow cytometry data. We demonstrate these methodologies on data collected by the International Mouse Phenotyping Consortium (IMPC).
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5815930 | PMC |
| http://dx.doi.org/10.1016/j.ymeth.2017.12.015 | DOI Listing |
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