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Balsor JL, Arbabi K, Singh D, Kwan R, Zaslavsky J, Jeyanesan E, Murphy KM. A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain. Front Neurosci 2021; 15:668293. [PMID: 34867140 PMCID: PMC8636820 DOI: 10.3389/fnins.2021.668293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 09/30/2021] [Indexed: 12/29/2022] Open
Abstract
Studying the molecular development of the human brain presents unique challenges for selecting a data analysis approach. The rare and valuable nature of human postmortem brain tissue, especially for developmental studies, means the sample sizes are small (n), but the use of high throughput genomic and proteomic methods measure the expression levels for hundreds or thousands of variables [e.g., genes or proteins (p)] for each sample. This leads to a data structure that is high dimensional (p ≫ n) and introduces the curse of dimensionality, which poses a challenge for traditional statistical approaches. In contrast, high dimensional analyses, especially cluster analyses developed for sparse data, have worked well for analyzing genomic datasets where p ≫ n. Here we explore applying a lasso-based clustering method developed for high dimensional genomic data with small sample sizes. Using protein and gene data from the developing human visual cortex, we compared clustering methods. We identified an application of sparse k-means clustering [robust sparse k-means clustering (RSKC)] that partitioned samples into age-related clusters that reflect lifespan stages from birth to aging. RSKC adaptively selects a subset of the genes or proteins contributing to partitioning samples into age-related clusters that progress across the lifespan. This approach addresses a problem in current studies that could not identify multiple postnatal clusters. Moreover, clusters encompassed a range of ages like a series of overlapping waves illustrating that chronological- and brain-age have a complex relationship. In addition, a recently developed workflow to create plasticity phenotypes (Balsor et al., 2020) was applied to the clusters and revealed neurobiologically relevant features that identified how the human visual cortex changes across the lifespan. These methods can help address the growing demand for multimodal integration, from molecular machinery to brain imaging signals, to understand the human brain’s development.
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Affiliation(s)
- Justin L Balsor
- McMaster Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Keon Arbabi
- McMaster Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Desmond Singh
- Department of Psychology, Neuroscience and Behavior, McMaster University, Hamilton, ON, Canada
| | - Rachel Kwan
- Department of Psychology, Neuroscience and Behavior, McMaster University, Hamilton, ON, Canada
| | - Jonathan Zaslavsky
- Department of Psychology, Neuroscience and Behavior, McMaster University, Hamilton, ON, Canada
| | - Ewalina Jeyanesan
- McMaster Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Kathryn M Murphy
- McMaster Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada.,Department of Psychology, Neuroscience and Behavior, McMaster University, Hamilton, ON, Canada
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Balsor JL, Ahuja D, Jones DG, Murphy KM. A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex. Front Cell Neurosci 2020; 14:245. [PMID: 33192303 PMCID: PMC7482673 DOI: 10.3389/fncel.2020.00245] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 07/15/2020] [Indexed: 11/20/2022] Open
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 aspects of neural development and individual variations that give rise to a richer picture of brain disorders. We have developed an approach for studying V1 plasticity that takes advantage of the known functions of many synaptic proteins for regulating visual plasticity. We use that knowledge to rebrand protein measurements into plasticity features and combine those into a plasticity phenotype. Here, we provide a primer for analyzing experience-dependent plasticity in V1 using example R code to identify high-dimensional changes in a group of proteins. We describe using PCA to classify high-dimensional plasticity features and use them to construct a plasticity phenotype. In the examples, we show how to use this analytical framework to study and compare experience-dependent development and plasticity of V1 and apply the plasticity phenotype to translational research questions. We include an R package “PlasticityPhenotypes” that aggregates the coding packages and custom code written in RStudio to construct and analyze plasticity phenotypes.
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Affiliation(s)
- Justin L Balsor
- McMaster Integrative Neuroscience Discovery and Study (MiNDS) Program, McMaster University, Hamilton, ON, Canada
| | - Dezi Ahuja
- Department of Psychology, Neuroscience & Behavior, McMaster University, Hamilton, ON, Canada
| | | | - Kathryn M Murphy
- McMaster Integrative Neuroscience Discovery and Study (MiNDS) Program, McMaster University, Hamilton, ON, Canada.,Department of Psychology, Neuroscience & Behavior, McMaster University, Hamilton, ON, Canada
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Zitman F, Richter-Levin G. Age and sex-dependent differences in activity, plasticity and response to stress in the dentate gyrus. Neuroscience 2013; 249:21-30. [DOI: 10.1016/j.neuroscience.2013.05.030] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2013] [Revised: 05/13/2013] [Accepted: 05/17/2013] [Indexed: 12/25/2022]
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Activity-dependent competition regulated by nonlinear interspike interaction in STDP: a model for visual cortical plasticity. ARTIFICIAL LIFE AND ROBOTICS 2012. [DOI: 10.1007/s10015-012-0029-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Manninen T, Hituri K, Kotaleski JH, Blackwell KT, Linne ML. Postsynaptic signal transduction models for long-term potentiation and depression. Front Comput Neurosci 2010; 4:152. [PMID: 21188161 PMCID: PMC3006457 DOI: 10.3389/fncom.2010.00152] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2010] [Accepted: 11/22/2010] [Indexed: 01/01/2023] Open
Abstract
More than a hundred biochemical species, activated by neurotransmitters binding to transmembrane receptors, are important in long-term potentiation (LTP) and long-term depression (LTD). To investigate which species and interactions are critical for synaptic plasticity, many computational postsynaptic signal transduction models have been developed. The models range from simple models with a single reversible reaction to detailed models with several hundred kinetic reactions. In this study, more than a hundred models are reviewed, and their features are compared and contrasted so that similarities and differences are more readily apparent. The models are classified according to the type of synaptic plasticity that is modeled (LTP or LTD) and whether they include diffusion or electrophysiological phenomena. Other characteristics that discriminate the models include the phase of synaptic plasticity modeled (induction, expression, or maintenance) and the simulation method used (deterministic or stochastic). We find that models are becoming increasingly sophisticated, by including stochastic properties, integrating with electrophysiological properties of entire neurons, or incorporating diffusion of signaling molecules. Simpler models continue to be developed because they are computationally efficient and allow theoretical analysis. The more complex models permit investigation of mechanisms underlying specific properties and experimental verification of model predictions. Nonetheless, it is difficult to fully comprehend the evolution of these models because (1) several models are not described in detail in the publications, (2) only a few models are provided in existing model databases, and (3) comparison to previous models is lacking. We conclude that the value of these models for understanding molecular mechanisms of synaptic plasticity is increasing and will be enhanced further with more complete descriptions and sharing of the published models.
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Affiliation(s)
- Tiina Manninen
- Department of Signal Processing, Tampere University of Technology Tampere, Finland
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A variety of competitive properties arising from STDP incorporating metaplastic regulation. ARTIFICIAL LIFE AND ROBOTICS 2010. [DOI: 10.1007/s10015-010-0791-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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