In the original article, there was an error in the section Discussion, Paragraph Number 2as published. The following citations were missing (Murphy et al., 2005; Pinto et al., 2010, 2015; Williams et al., 2010; Witten and Tibshirani, 2010; Siu et al., 2015, 2017). The revised paragraph is included below:
“Many factors come into play when selecting an appropriate clustering algorithm for a study. Here, we considered the goal of the study (to resolve sometimes subtle age-related changes in molecular mechanism), the structure of the dataset (p ~ n to p >> n), and the output of the algorithm (is it just the clusters or is feature selection included). Sparse K-means clustering was selected because it fit all of those considerations. We know from previous studies of the molecular development of the human brain that there can be subtle differences between age groups (Murphy et al., 2005; Pinto et al., 2010, 2015; Williams et al., 2010; Siu et al., 2015, 2017), and yet even small changes in protein or gene expression will alter neural function. Therefore, we looked for algorithms designed for omics datasets where subtle changes in a subset of the genes or proteins would identify important characteristics of the data. The development of sparse K-means clustering by Witten and Tibshirani (2010) was partially inspired by the need to better cluster a breast cancer dataset. In that dataset, subtle differences in gene expression significantly impacted patient outcomes, but standard clustering approaches did not pick those up. In addition, sparse clustering was developed to address datasets, like ours and the breast cancer data where the structure is p ~ n to p >> n. Sparse K-means clustering is a good fit for those high dimensional structures because it minimizes the within-cluster sum of squares with a dissimilarity measure while maximizing the between-cluster sum of squares by iteratively reweighting the measures. Finally, and most importantly, sparse K-means clustering performs feature selection. The examples in this paper show the reweighted proteins and those distributions identifying how much each protein contributes to partitioning the samples into clusters. That matrix is sparse, with unimportant proteins having near-zero weights and important ones having non-zero weights. Those weights are essential for cluster analysis to help with making neurobiologically relevant interpretations of brain development from the cluster analysis.”
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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References
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MurphyK. M.BestonB. R.BoleyP. M.JonesD. G. (2005). Development of human visual cortex: a balance between excitatory and inhibitory plasticity mechanisms. Dev. Psychobiol.46, 209–221. 10.1002/dev.20053
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PintoJ. G. A.HornbyK. R.JonesD. G.MurphyK. M. (2010). Developmental changes in GABAergic mechanisms in human visual cortex across the lifespan. Front. Cell Neurosci.4:16. 10.3389/fncel.2010.00016
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PintoJ. G. A.JonesD. G.WilliamsC. K.MurphyK. M. (2015). Characterizing synaptic protein development in human visual cortex enables alignment of synaptic age with rat visual cortex. Front. Neural. Circuit9:3. 10.3389/fncir.2015.00003
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SiuC. R.BalsorJ. L.JonesD. G.MurphyK. M. (2015). Classic and Golli Myelin Basic Protein have distinct developmental trajectories in human visual cortex. Front. Neurosci-switz9:138. 10.3389/fnins.2015.00138
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SiuC. R.BesharaS. P.JonesD. G.MurphyK. M. (2017). Development of Glutamatergic Proteins in Human Visual Cortex across the Lifespan. J. Neurosci.37:6042. 10.1523/jneurosci.2304-16.2017
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WilliamsK.IrwinD. A.JonesD. G.MurphyK. M. (2010). Dramatic Loss of Ube3A Expression during Aging of the Mammalian Cortex. Front. Aging Neurosci.2:18. 10.3389/fnagi.2010.00018
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Summary
Keywords
human brain, development, clustering, synaptic proteins, transcriptomic data, high dimension and low sample size, sparsity-based algorithm
Citation
Balsor JL, Arbabi K, Singh D, Kwan R, Zaslavsky J, Jeyanesan E and Murphy KM (2022) Corrigendum: A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain. Front. Neurosci. 16:907479. doi: 10.3389/fnins.2022.907479
Received
29 March 2022
Accepted
30 March 2022
Published
19 April 2022
Approved by
Frontiers Editorial Office, Frontiers Media SA, Switzerland
Volume
16 - 2022
Updates
Copyright
© 2022 Balsor, Arbabi, Singh, Kwan, Zaslavsky, Jeyanesan and Murphy.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Kathryn M. Murphy kmurphy@mcmaster.ca
This article was submitted to Neurogenomics, a section of the journal Frontiers in Neuroscience
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.