PCA for RNA-Seq

Principal Component Analysis makes it possible to project a high-dimensional dataset (where the number of dimensions equals the number of genes or transcripts) onto two or three dimensions. This helps in identifying outlying samples for quality control, and gives a feeling for the principal causes of variation in a dataset. The analysis proceeds by transforming a large set of variables (in this case, the counts for each individual gene or transcript) to a smaller set of orthogonal principal components. The first principal component specifies the direction with the largest variability in the data, the second component is the direction with the second largest variation, and so on.

The PCA for RNA-Seq tool clusters samples in 2D or 3D. Known metadata about each sample is added as an overlay. In addition, the following filtering and normalization are performed:

For more detail about these steps, see RNA-seq normalization.

To start the analysis:

        Toolbox | RNA-Seq Analysis | PCA for RNA-Seq (Image pca)

Select a number of expression tracks (Image rnaseqtrack_16_h_p) and click Next.