Statistical models discriminating between complex samples measured with microfluidic receptor-cell arrays

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Abstract

Data analysis for flow-based in-vitro receptomics array, like a tongue-on-a-chip, is complicated by the relatively large variability within and between arrays, transfected DNA types, spots, and cells within spots. Simply averaging responses of spots of the same type would lead to high variances and low statistical power. This paper presents an approach based on linear mixed models, allowing a quantitative and robust comparison of complex samples and indicating which receptors are responsible for any differences. These models are easily extended to take into account additional effects such as the build-up of cell stress and to combine data from replicated experiments. The increased analytical power this brings to receptomics research is discussed.
Original languageEnglish
Article numbere0214878
JournalPLoS ONE
Volume14
Issue number4
DOIs
Publication statusPublished - 8 Apr 2019

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Microfluidics
Statistical Models
statistical models
receptors
Oligonucleotide Array Sequence Analysis
tongue
Tongue
Linear Models
data analysis
cells
sampling
DNA
Research
Experiments
In Vitro Techniques

Cite this

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title = "Statistical models discriminating between complex samples measured with microfluidic receptor-cell arrays",
abstract = "Data analysis for flow-based in-vitro receptomics array, like a tongue-on-a-chip, is complicated by the relatively large variability within and between arrays, transfected DNA types, spots, and cells within spots. Simply averaging responses of spots of the same type would lead to high variances and low statistical power. This paper presents an approach based on linear mixed models, allowing a quantitative and robust comparison of complex samples and indicating which receptors are responsible for any differences. These models are easily extended to take into account additional effects such as the build-up of cell stress and to combine data from replicated experiments. The increased analytical power this brings to receptomics research is discussed.",
author = "H.R.M.J. Wehrens and M. Roelse and M.G.L. Henquet and {van Lenthe}, M.S. and Paul Goedhart and M.A. Jongsma",
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AU - Wehrens, H.R.M.J.

AU - Roelse, M.

AU - Henquet, M.G.L.

AU - van Lenthe, M.S.

AU - Goedhart, Paul

AU - Jongsma, M.A.

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AB - Data analysis for flow-based in-vitro receptomics array, like a tongue-on-a-chip, is complicated by the relatively large variability within and between arrays, transfected DNA types, spots, and cells within spots. Simply averaging responses of spots of the same type would lead to high variances and low statistical power. This paper presents an approach based on linear mixed models, allowing a quantitative and robust comparison of complex samples and indicating which receptors are responsible for any differences. These models are easily extended to take into account additional effects such as the build-up of cell stress and to combine data from replicated experiments. The increased analytical power this brings to receptomics research is discussed.

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