Cyto-Feature Engineering: A Pipeline for Flow Cytometry Analysis to Uncover Immune Populations and Associations with Disease
| العنوان: | Cyto-Feature Engineering: A Pipeline for Flow Cytometry Analysis to Uncover Immune Populations and Associations with Disease |
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| المؤلفون: | Marcela Henao-Tamayo, G. Brooke Anderson, Taru S. Dutt, Burton Karger, Mauricio Rojas, Andrés Obregón-Henao, Amy Fox |
| المصدر: | Scientific Reports, Vol 10, Iss 1, Pp 1-12 (2020) Scientific Reports |
| بيانات النشر: | Nature Publishing Group, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Feature engineering, Computer science, Pipeline (computing), Cytodiagnosis, Population, Cell, Immunology, lcsh:Medicine, Context (language use), Computational biology, Microbiology, Article, Flow cytometry, Immunophenotyping, Mice, medicine, Immunology and Allergy, Animals, Humans, Tuberculosis, education, lcsh:Science, Statistical hypothesis testing, education.field_of_study, Vaccines, Multidisciplinary, Blood Cells, medicine.diagnostic_test, Murine splenocytes, lcsh:R, Mycobacterium tuberculosis, Flow Cytometry, medicine.anatomical_structure, Phenotype, Infectious diseases, lcsh:Q, Disease Susceptibility, Biomarkers |
| الوصف: | Flow cytometers can now analyze up to 50 parameters per cell and millions of cells per sample; however, conventional methods to analyze data are subjective and time-consuming. To address these issues, we have developed a novel flow cytometry analysis pipeline to identify a plethora of cell populations efficiently. Coupled with feature engineering and immunological context, researchers can immediately extrapolate novel discoveries through easy-to-understand plots. The R-based pipeline uses Fluorescence Minus One (FMO) controls or distinct population differences to develop thresholds for positive/negative marker expression. The continuous data is transformed into binary data, capturing a positive/negative biological dichotomy often of interest in characterizing cells. Next, a filtering step refines the data from all identified cell phenotypes to populations of interest. The data can be partitioned by immune lineages and statistically correlated to other experimental measurements. The pipeline’s modularity allows customization of statistical testing, adoption of alternative initial gating steps, and incorporation of other datasets. Validation of this pipeline through manual gating of two datasets (murine splenocytes and human whole blood) confirmed its accuracy in identifying even rare subsets. Lastly, this pipeline can be applied in all disciplines utilizing flow cytometry regardless of cytometer or panel design. The code is available at https://github.com/aef1004/cyto-feature_engineering. |
| اللغة: | English |
| تدمد: | 2045-2322 |
| DOI: | 10.1038/s41598-020-64516-0 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e994455047f9747b3874139eb44019d2 http://link.springer.com/article/10.1038/s41598-020-64516-0 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....e994455047f9747b3874139eb44019d2 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-020-64516-0 |