2017
DOI: 10.4236/ajps.2017.83035
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Improved Representation of Biological Information by Using Correlation as Distance Function for Heatmap Cluster Analysis

Abstract: Heatmap cluster figures are often used to represent data sets in the omic sciences. The default option of the frequently used R heatmap function is to cluster data according to Euclidean distance, which groups data mainly to their numerical value and not to its relative behaviour. The disadvantage of using the default clustering dendrograms of R is demonstrated. Instead, a script is provided that uses correlation as distance function, which better reveals biologically meaningful information. This optimized scr… Show more

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Cited by 19 publications
(12 citation statements)
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“…Several heatmap-bicluster figures were constructed, selecting the significant negative ions only ( S2 Fig ), all the significant positive ions ( Fig 1D ), or only the most intense significant positive ions ( S3 Fig ). In all cases, an optimized hierarchical clustering based on correlation as previously described was applied [ 49 ]. Those grayscale heatmaps depicted the relative intensity (ion abundance) under the different conditions (black indicates high, and white indicates low, as shown in Fig 1D and S2 and S3 Figs).…”
Section: Resultsmentioning
confidence: 99%
“…Several heatmap-bicluster figures were constructed, selecting the significant negative ions only ( S2 Fig ), all the significant positive ions ( Fig 1D ), or only the most intense significant positive ions ( S3 Fig ). In all cases, an optimized hierarchical clustering based on correlation as previously described was applied [ 49 ]. Those grayscale heatmaps depicted the relative intensity (ion abundance) under the different conditions (black indicates high, and white indicates low, as shown in Fig 1D and S2 and S3 Figs).…”
Section: Resultsmentioning
confidence: 99%
“…Data transformation using 'normalize' was used to compare and group different data. In addition, heatmap cluster Figures 1 and 2 were used to summarize the data from the experiment according to Euclidean distance [94]. The relationship between the parameters was determined with the Pearson correlation coefficient.…”
Section: Discussionmentioning
confidence: 99%
“…We then grouped bacterial taxa into clusters based on similarities in age-associated abundance trajectories. Pairwise distances between microbial taxa trajectories (i.e., the predicted values of the LOESS regression) were computed using correlation coefficients as a distance measure [150], and hierarchical clustering was performed using the complete method (using the function hclust from the stats R package). The optimal number of clusters was determined using the Elbow method (i.e., choosing a number of clusters so that adding another cluster does not highly improve the total within-cluster sum of squares) [151].…”
Section: Age-associated Changes In Microbial Taxonomic Compositionmentioning
confidence: 99%