2020
DOI: 10.3389/fncel.2020.00245
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A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex

Abstract: Many neural mechanisms regulate experience-dependent plasticity in the visual cortex (V1), and new techniques for quantifying large numbers of proteins or genes are transforming how plasticity is studied into the era of big data. With those large data sets comes the challenge of extracting biologically meaningful results about visual plasticity from data-driven analytical methods designed for high-dimensional data. In other areas of neuroscience, high-information content methodologies are revealing more subtle… Show more

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Cited by 2 publications
(9 citation statements)
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“…Thus, this visualization shows the mean expression for the 23 proteins in the 6 clusters, but it is still challenging to derive what differentiates the clusters. To address this, we applied our recently developed workflow (Balsor et al, 2019(Balsor et al, , 2020) that includes dimension reduction, identification of features and the construct of a plasticity phenotype visualization to characterize the development of the human visual cortex. This workflow is described in detail in a previous publication (Balsor et al, 2020).…”
Section: Application Of Robust Sparse K-means Clustering Clusters To Study Human Visual Cortex Developmentmentioning
confidence: 99%
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“…Thus, this visualization shows the mean expression for the 23 proteins in the 6 clusters, but it is still challenging to derive what differentiates the clusters. To address this, we applied our recently developed workflow (Balsor et al, 2019(Balsor et al, , 2020) that includes dimension reduction, identification of features and the construct of a plasticity phenotype visualization to characterize the development of the human visual cortex. This workflow is described in detail in a previous publication (Balsor et al, 2020).…”
Section: Application Of Robust Sparse K-means Clustering Clusters To Study Human Visual Cortex Developmentmentioning
confidence: 99%
“…The range of trajectories highlights the need for high-dimensional analyses to capture the complexity of this development. To help describe when the expression level of a protein in a cluster was above or below the overall mean, we implemented the over-representation analysis (ORA_phenotype function) described previously ( Balsor et al, 2020 ; Figure 8B ). Briefly, for each protein, a normal distribution was simulated using the mean and standard deviation of the expression values for all samples.…”
Section: Application Of Robust Sparse K -Means Clustering Clusters To Study Human Visual Cortex Developmentmentioning
confidence: 99%
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“…They can also be used for differential gene expression analysis to highlight which features are enriched during different stages. The top weighted proteins or genes are also useful for creating a phenotype that characterize higher dimensional changes occuring different stages of the lifespan (Balsor et al, 2020) .…”
Section: Proclus : the Proclus Clustering Methods Was Implemented In Rmentioning
confidence: 99%