Normalization And Statistical Analysis Of Multiplexed Bead-based Immunoassay Data Using Mixed-effects Modeling*. - Info and Reading Options
By Clarke, David C., Morris, Melody K. and Lauffenburger, Douglas A.
"Normalization And Statistical Analysis Of Multiplexed Bead-based Immunoassay Data Using Mixed-effects Modeling*." and the language of the book is English.
“Normalization And Statistical Analysis Of Multiplexed Bead-based Immunoassay Data Using Mixed-effects Modeling*.” Metadata:
- Title: ➤ Normalization And Statistical Analysis Of Multiplexed Bead-based Immunoassay Data Using Mixed-effects Modeling*.
- Authors: Clarke, David C.Morris, Melody K.Lauffenburger, Douglas A.
- Language: English
Edition Identifiers:
- Internet Archive ID: pubmed-PMC3536905
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The Internet Archive:
This article is from <a href="//archive.org/search.php?query=journaltitle%3A%28Molecular%20&%20Cellular%20Proteomics%20:%20MCP%29" rel="nofollow">Molecular & Cellular Proteomics : MCP</a>, <a href="//archive.org/search.php?query=journaltitle%3A%28Molecular%20&%20Cellular%20Proteomics%20:%20MCP%29%20AND%20volume%3A%2812%29" rel="nofollow">volume 12</a>.<h2>Abstract</h2>Multiplexed bead-based flow cytometric immunoassays are a powerful experimental tool for investigating cellular communication networks, yet their widespread adoption is limited in part by challenges in robust quantitative analysis of the measurements. Here we report our application of mixed-effects modeling for the normalization and statistical analysis of bead-based immunoassay data. Our data set consisted of bead-based immunoassay measurements of 16 phospho-proteins in lysates of HepG2 cells treated with ligands that regulate acute-phase protein secretion. Mixed-effects modeling provided estimates for the effects of both the technical and biological sources of variance, and normalization was achieved by subtracting the technical effects from the measured values. This approach allowed us to detect ligand effects on signaling with greater precision and sensitivity and to more accurately characterize the HepG2 cell signaling network using constrained fuzzy logic. Mixed-effects modeling analysis of our data was vital for ascertaining that IL-1α and TGF-α treatment increased the activities of more pathways than IL-6 and TNF-α and that TGF-α and TNF-α increased p38 MAPK and c-Jun N-terminal kinase (JNK) phospho-protein levels in a synergistic manner. Moreover, we used mixed-effects modeling-based technical effect estimates to reveal the substantial variance contributed by batch effects along with the absence of loading order and assay plate position effects. We conclude that mixed-effects modeling enabled additional insights to be gained from our data than would otherwise be possible and we discuss how this methodology can play an important role in enhancing the value of experiments employing multiplexed bead-based immunoassays.
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