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Fig. 4 | BMC Bioinformatics

Fig. 4

From: BEAVR: a browser-based tool for the exploration and visualization of RNA-seq data

Fig. 4

Illustrating variance across samples using principle component analysis (PCA) and sample clustering. a PCA is a useful tool to determine the variance within and across different experimental groups and replicates. The PCA output from the PCA tab is shown for the Sehrawat et al. dataset. High variance (98%), as expected, is observed between the two experimental groups (DMSO- vs SP2509-treated) whereas low variance (1%) is observed between replicates within each group. b Hierarchical sample clustering is also a useful tool to determine variances. The output from the Sample Clustering tab is shown for the Sehrawat et al. dataset. Pearson correlation was selected as the distance measurement method in the sidebar. Similar to the PCA plot, the clustered heatmap shows that replicates in each experimental group (DMSO- or SP2509-treated) cluster strongly together, indicating low variance between biological replicates

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