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BMC Bioinformatics. 2019 May 28;20(1):274. doi: 10.1186/s12859-019-2787-4.

flowDiv: a new pipeline for analyzing flow cytometric diversity.

Author information

1
Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Natal, Brazil.
2
Departamento de Oceanografia e Limnologia, Universidade Federal do Rio Grande do Norte, Natal, Brazil.
3
Instituto Tecnológico de Chascomús (INTECH), Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
4
Departamento de Biologia, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil.
5
Departamento de Hidrobiologia, Universidade Federal de São Carlos, São Carlos, Brazil.
6
Instituto Tecnológico de Chascomús (INTECH), Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina. funrein@intech.gov.ar.

Abstract

BACKGROUND:

Flow cytometry (FCM) is one of the most commonly used technologies for analysis of numerous biological systems at the cellular level, from cancer cells to microbial communities. Its high potential and wide applicability led to the development of various analytical protocols, which are often not interchangeable between fields of expertise. Environmental science in particular faces difficulty in adapting to non-specific protocols, mainly because of the highly heterogeneous nature of environmental samples. This variety, although it is intrinsic to environmental studies, makes it difficult to adjust analytical protocols to maintain both mathematical formalism and comprehensible biological interpretations, principally for questions that rely on the evaluation of differences between cytograms, an approach also termed cytometric diversity. Despite the availability of promising bioinformatic tools conceived for or adapted to cytometric diversity, most of them still cannot deal with common technical issues such as the integration of differently acquired datasets, the optimal number of bins, and the effective correlation of bins to previously known cytometric populations.

RESULTS:

To address these and other questions, we have developed flowDiv, an R language pipeline for analysis of environmental flow cytometry data. Here, we present the rationale for flowDiv and apply the method to a real dataset from 31 freshwater lakes in Patagonia, Argentina, to reveal significant aspects of their cytometric diversities.

CONCLUSIONS:

flowDiv provides a rather intuitive way of proceeding with FCM analysis, as it combines formal mathematical solutions and biological rationales in an intuitive framework specifically designed to explore cytometric diversity.

KEYWORDS:

Cytometric diversity; Flow cytometry; R language

PMID:
31138128
PMCID:
PMC6540361
DOI:
10.1186/s12859-019-2787-4
[Indexed for MEDLINE]
Free PMC Article

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