Pattern recognition in flow cytometry

Boddy, Lynne; Wilkins, M. F.; Morris, Colin W.

doi:10.1002/1097-0320(20010701)44:3<195::aid-cyto1112>3.0.co;2-h

Cited by 50 publications

(38 citation statements)

References 63 publications

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“…First, they require statistical clustering or artificial neural networks to construct functional groups (Boddy et al, 2001), and these final categories may not have a general ecological interpretation. Each different method suffers from arbitrary decision steps that may critically affect the outcomes in terms of BD metrics, through identity and abundance of resulting formed groups (Figure 2) (Petchey and Gaston, 2006).…”

Section: Application Of Automated Sfc In the Field: Current Use And Mmentioning

confidence: 99%

Opportunities and challenges in deriving phytoplankton diversity measures from individual trait-based data obtained by scanning flow-cytometry

2014

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In the context of understanding and predicting the effects of human-induced environmental change (EC) on biodiversity (BD), and the consequences of BD change for ecosystem functioning (EF), microbial ecologists face the challenge of linking individual level variability in functional traits to larger-scale ecosystem processes. Since lower level BD at genetic, individual, and population levels largely determines the functionality and resilience of natural populations and communities, individual level measures promise to link EC-induced physiological, ecological, and evolutionary responses to EF. Intraspecific trait differences, while representing among the least-understood aspects of natural microbial communities, have recently become easier to measure due to new technology. For example, recent advance in scanning flow-cytometry (SCF), automation of phytoplankton sampling and integration with environmental sensors allow to measure morphological and physiological traits of individual algae with high spatial and temporal resolution. Here we present emerging features of automated SFC data from natural phytoplankton communities and the opportunities that they provide for understanding the functioning of complex aquatic microbial communities. We highlight some current limitations and future needs, particularly focusing on the large amount of individual level data that, for the purpose of understanding the EC-BD-EF link, need to be translated into meaningful BD indices. We review the available functional diversity (FD) indices that, despite having been designed for mean trait values at the species level, can be adapted to individual-based trait data and provide links to ecological theory. We conclude that, considering some computational, mathematical and ecological issues, a set of multi-dimensional indices that address richness, evenness and divergence in overall community trait space represent the most promising BD metrics to study EC-BD-EF using individual level data.

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Section: Application Of Automated Sfc In the Field: Current Use And Mmentioning

confidence: 99%

Opportunities and challenges in deriving phytoplankton diversity measures from individual trait-based data obtained by scanning flow-cytometry

2014

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“…However, visualizing these data becomes more and more complex and requires multiple sequential analyses to provide information about each cell subset. Therefore, automated classification systems are being developed (109)(110)(111)(112). Additionally, optimization of storage conditions to preserve CSF cells should result in higher cell yields and thereby increase the detection rate of flow cytometry in CSF samples with low cellularity.…”

Section: Perspectivesmentioning

confidence: 99%

Flow cytometric characterization of cerebrospinal fluid cells

Graaf

Jongste

Kraan

et al. 2011

Cytometry Part B Clinical

120

115

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Flow cytometry facilitates the detection of a large spectrum of cellular characteristics on a per cell basis, determination of absolute cell numbers and detection of rare events with high sensitivity and specificity. White blood cell (WBC) counts in cerebrospinal fluid (CSF) are important for the diagnosis of many neurological disorders. WBC counting and differential can be performed by microscopy, hematology analyzers, or flow cytometry. Flow cytometry of CSF is increasingly being considered as the method of choice in patients suspected of leptomeningeal localization of hematological malignancies. Additionally, in several neuroinflammatory diseases such as multiple sclerosis and paraneoplastic neurological syndromes, flow cytometry is commonly performed to obtain insight into the immunopathogenesis of these diseases. Technically, the low cellularity of CSF samples, combined with the rapidly declining WBC viability, makes CSF flow cytometry challenging. Comparison of flow cytometry with microscopic and molecular techniques shows that each technique has its own advantages and is ideally combined. We expect that increasing the number of flow cytometric parameters that can be simultaneously studied within one sample, will further refine the information on CSF cell subsets in low-cellular CSF samples and enable to define cell populations more accurately. V C 2011 International Clinical Cytometry Society

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“…The output layer was structured to have two outputs that could each vary between 0 and 1. Output vectors for the training and validation set were defined to be [1,0] for the first dye and [0,1] for the second dye. The training algorithms used were a resilient back-propagation (TrainRP), a scaled conjugate gradient algorithm (TrainSCG), and the Levenberg-Marquardt algorithm (TrainLM).…”

Section: Methodsmentioning

confidence: 99%

Implementation of Neural Networks for the Identification of Single Molecules

et al. 2004

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The effectiveness of neural networks and the optimization of parameters for implementing neural networks were evaluated for use in the identification of single molecules according to their fluorescence lifetime. The best network architecture and training parameters were determined for both ideal and nonideal single-molecule fluorescence data. The effectiveness of the neural network is compared to that of the maximum likelihood estimator on the basis of its ability to correctly identify single molecules. For ideal single-molecule data, it was found that the neural networks and the maximum likelihood estimator perform approximately equally well. For nonideal single-molecule fluorescence data, neural networks were able to correctly identify a larger percentage of single-molecule events than the MLE method.

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Pattern recognition in flow cytometry

Cited by 50 publications

References 63 publications

Opportunities and challenges in deriving phytoplankton diversity measures from individual trait-based data obtained by scanning flow-cytometry

Opportunities and challenges in deriving phytoplankton diversity measures from individual trait-based data obtained by scanning flow-cytometry

Flow cytometric characterization of cerebrospinal fluid cells

Implementation of Neural Networks for the Identification of Single Molecules

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