BaseMount: Directly Linking NGS Data to R Packages for RNA-Seq Differential Expression Analyses
With the recent launch of BaseMount, access to your NGS data has never been so convenient. This early access release is available for all Linux-based operating systems and utilizes a command line interface (CLI) to access personal Projects, Samples, Runs, and AppResults within your BaseSpace account. Below are some simple steps to effectively transform your RNA-Seq data straight from our very own RNA Express app into a Normalized Count Plot, MA-plot, and Principal Component Analysis (PCA) plot. We are going to be using the popular Bioconductor DESeq2 package to construct the plots and the example is a differential expression analysis comparing two tissue samples: Human Brain Reference RNA (HBRR) and Universal Human Reference RNA (UHRR). Follow these steps below to get started:
- Download BaseMount, create a ‘BaseSpace’ directory, and initialize the connection with BaseSpace.
- Proceed to import the counts table from BaseMount and provide the metadata (personal file).
- Download and import the DESeq2 package from Bioconductor.
- Use the DESeq function to perform standard differential expression analysis.
*The following examples use BaseSpace public data HiSeq 4000: RNA-Seq 64-plex (MAQC HBRR and UHRR) and the design argument is based on the metadata.
Figure (1): Comparison of normalized counts for SDF4 gene in HBRR vs. UHRR. Visualize differential expression for specific genes with this plot. In this example, the gene with the smallest p-adjusted value (SDF4) was plotted.
Figure (2): MA-plot of UHRR vs. HBRR. Many differentially genes are highlighted in red (p-adj < 0.1) as expected with two different sample types.
Figure (3): Principal Component Analysis (PCA) plot of HBRR and UHRR samples. The PCA shows clustering and clear separation between sample type (PC1) and library prep kit (PC2). This type of analysis is very useful in visualizing the main metadata components contributing towards the variability in RNA-Seq results.
- Learn more analyses at: http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.pdf