A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain

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 a...

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Autores principales:	Justin L. Balsor, Keon Arbabi, Desmond Singh, Rachel Kwan, Jonathan Zaslavsky, Ewalina Jeyanesan, Kathryn M. Murphy
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	human brain development clustering synaptic proteins transcriptomic data high dimension and low sample size Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/e0519d8c50c44180963685b47f848b9a
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spelling	oai:doaj.org-article:e0519d8c50c44180963685b47f848b9a2021-11-16T07:42:14ZA Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain1662-453X10.3389/fnins.2021.668293https://doaj.org/article/e0519d8c50c44180963685b47f848b9a2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.668293/fullhttps://doaj.org/toc/1662-453XStudying 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.Justin L. BalsorKeon ArbabiDesmond SinghRachel KwanJonathan ZaslavskyEwalina JeyanesanKathryn M. MurphyKathryn M. MurphyFrontiers Media S.A.articlehuman braindevelopmentclusteringsynaptic proteinstranscriptomic datahigh dimension and low sample sizeNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	human brain development clustering synaptic proteins transcriptomic data high dimension and low sample size Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	human brain development clustering synaptic proteins transcriptomic data high dimension and low sample size Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Justin L. Balsor Keon Arbabi Desmond Singh Rachel Kwan Jonathan Zaslavsky Ewalina Jeyanesan Kathryn M. Murphy Kathryn M. Murphy A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain
description	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.
format	article
author	Justin L. Balsor Keon Arbabi Desmond Singh Rachel Kwan Jonathan Zaslavsky Ewalina Jeyanesan Kathryn M. Murphy Kathryn M. Murphy
author_facet	Justin L. Balsor Keon Arbabi Desmond Singh Rachel Kwan Jonathan Zaslavsky Ewalina Jeyanesan Kathryn M. Murphy Kathryn M. Murphy
author_sort	Justin L. Balsor
title	A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain
title_short	A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain
title_full	A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain
title_fullStr	A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain
title_full_unstemmed	A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain
title_sort	practical guide to sparse k-means clustering for studying molecular development of the human brain
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/e0519d8c50c44180963685b47f848b9a
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A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain

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