Visualization of RNA Sequencing Data with PCA clustering and Heatmaps in RR Studio clean

As sequencing technologies continue to improve and assessment of the transcriptome with RNA-Sequencing becomes more commonplace, it is important that the proper methods are in place to analyze the large amounts of data.   R/R Studio contains many packages that are useful for both statistical analysis and visualization of large datasets. In this webinar, we will focus specifically on two of the more common methods of visualization, PCA clustering and heatmaps, and how quality, customized plots of each of these can be generated in R/R Studio.   Principal Component Analysis (PCA) clustering allows the investigator to quickly assess the overall similarity (or difference) in gene expression profiles among a group of samples.   It can also be useful to identify potential outlier samples.  Heatmaps can be used to observe expression of large groups of genes across all experimental samples, thus making it easier to identify potentially interesting patterns.  Prior experience with PCA clustering or heatmaps is not required for this webinar; however, familiarity with the basics of R/Studio is recommended.