This plot shows the Principal component analysis (PCA) of Ribo-Seq experiment. The PCA plot shows the data points projected onto the first two principal components, which are the two new axes that account for the most variance in the data.
This plot shows the Principal component analysis (PCA) of RNAseq
experiment. The PCA plot shows the data points projected onto the first
two principal components, which are the two new axes that account for
the most variance in the
data.
The Translation Efficency (TE) volcano plot shows the distribution of gene based on their log2FoldChange in TE and their p-value. TE = log2(Condition_1(RPF/mRNA) / Condition_2(RPF/mRNA))
The ‘Directional plot’ shows the log2 fold change in Ribo-seq (x-axis) versus the log2 fold change in RNA-seq (y-axis) for each gene. The four dashed lines indicate log2 fold change values of -1 or +1. ‘Homodirectional’ genes have increased or decreased reads in both Ribo-seq and RNA-seq relative to the control (log2 fold change). ‘Crossdirectional’ genes have increased reads in Ribo-seq or RNA-seq relative to the control, but decreased reads in the other assay.