12 papers · ranked by Valyu relevance
Kushal K. Dey, Matthew Stephens
Estimation of correlation matrices and correlations among variables is a ubiquitous problem in statistics. In many cases – especially when the number of observations is small relative to the number of variables – some kind of shrinkage or regularization is necessary to improve estimation accuracy. Here, we propose an…
Milton Pividori, Marylyn D. Ritchie, Diego H. Milone, Casey S. Greene
Correlation coefficients are widely used to identify patterns in data that may be of particular interest. In transcriptomics, genes with correlated expression often share functions or are part of disease-relevant biological processes. Here we introduce the Clustermatch Correlation Coefficient (CCC), an efficient…
Kirsten L. Peterson, Ruben Sanchez-Romero, Ravi D. Mill, Michael W. Cole
Functional connectivity (FC) has been invaluable for understanding the brain’s communication network, with strong potential for enhanced FC approaches to yield additional insights. Unlike with the fMRI field-standard method of pairwise correlation, theory suggests that partial correlation can estimate FC without…
Ruben Sanchez-Romero, Michael W. Cole
Cognition and behavior emerge from brain network interactions, suggesting that causal interactions should be central to the study of brain function. Yet approaches that characterize relationships among neural time series—functional connectivity (FC) methods—are dominated by methods that assess bivariate statistical…
Robert M Flight, Praneeth S Bhatt, Hunter NB Moseley
Almost all correlation measures currently available are unable to directly handle missing values. Typically, missing values are either ignored completely by removing them or are imputed and used in the calculation of the correlation coefficient. In both cases, the correlation value will be impacted based on a…
Qiqing Tao, Koichiro Tamura, Fabia Battistuzzi, Sudhir Kumar
Knowledge of the models of evolutionary rate variation in a phylogeny is of fundamental importance in molecular phylogenetics and systematics, not only to inform about the relationship among molecular, biological, and life history traits, but also for reliable estimation of divergence times among species and genes.…
Kenneth D. Harris
Many neurophysiological signals exhibit slow continuous trends over time. Because standard correlation analyses assume that all samples are independent, they can yield apparently significant “nonsense correlations” even for signals that are completely unrelated. Here we compare the performance of several methods for…
Dean A. Pospisil, Wyeth Bair
The Pearson correlation coefficient squared, r^2^, is often used in the analysis of neural data to estimate the relationship between neural tuning curves. Yet this metric is biased by trial-to-trial variability: as trial-to-trial variability increases, measured correlation decreases. Major lines of research are…
Soroosh Afyouni, Stephen M. Smith, Thomas E. Nichols
The dependence between pairs of time series is commonly quantified by Pearson’s correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher’s…
Gabriel Kennedy, Alejandro Ochoa
In studies of assortative mating, similarity between variables measured in parents is often quantified using correlation. The order of the parents within any given pair can be arbitrary in these applications, but common correlation estimators are not robust to reordering within pairs. These unordered variable pairs are…
Edoardo Saccenti, Margriet H. W. B. Hendriks, Age K. Smilde
Correlation coefficients are abundantly used in the life sciences. Their use can be limited to simple exploratory analysis or to construct association networks for visualization but they are also basic ingredients for sophisticated multivariate data analysis methods. It is therefore important to have reliable estimates…
Catherine S Cutts, Stephen J Eglen
Distant-dependent correlations in spontaneous retinal activity are thought to be instructive in the development of the retinotopic map and eye-specific segregation maps. Many studies which seek to investigate these correlations and their role in map formation record spontaneous retinal activity from different…