The page shows identified miRNA-gene interactions in Reactome pathway categories.
One can choose a pathway category to draw a heat map and tabulate identified miRNA-gene interactions in a category.
To filter the contents of the heat map and table, one can adjust the threshold for Pearson correlation and/or select gene types of interest.
The heat map has two components that share columns that list differentially expressed protein-coding genes identified from RNA-seq data.
In the upper part, rows represent pathways of a root category (e.g., immune system), and grid cell colours indicate whether a protein-coding gene is involved in the pathway (grey), involved in the pathway and an immune gene (grey grid with red border), or not involved in the pathway (white).
The figures next to a pathway name indicate how many differentially expressed immune genes (left) and how many protein-coding genes found in our RNA-seq data (right) belong to it.
The top annotation highlights immune genes of different types using a colour code. The right annotation shows the statistics of the gene set enrichment analysis including the enrichment score and the FDR.
The bar plot between the heat map components shows the log2 fold-change of the protein-coding genes in caIKK-enhanced DCs (blue: downregulated; red: upregulated).
In the lower component, the rows represent the ranking miRNAs in immune system (from high to low) and the grid cells show the regulative influence of a protein-coding gene by a miRNA, which is estimated by the Pearson correlation coefficients between their expression profiles.
If a gene is a known immune gene, the corresponding grid cell has a red border. The numbers in the parentheses next to the miRNA names show the number of DE immune genes and the number of DE protein-coding genes that are regulated by a miRNA. The right annotation shows the results of the differential expression analysis including the log2 fold-change of miRNA expressions and their FDRs.
The table shows identified miRNA-gene interactions in the pathway category.
It has 5 columns that are miRNA names, gene symbols of miRNA targets, Pearson correlation between miRNAs and their targets, log2 fold-change of miRNAs and their targets, and immune categories of miRNA target genes.
Xin Lai, Florian S. Dreyer, Martina Cantone,
Martin Eberhardt, Kerstin F. Gerer, Tanushree Jaitly,
Steffen Uebe, Christopher Lischer, Arif Ekici,
Jürgen Wittmann, Hans-Martin Jäck, Niels Schaft,
Jan Dörrie, Julio Vera.
Network- and systems-based re-engineering of dendritic cells
with non-coding RNAs for cancer immunotherapy.