Our understanding of complex biological processes can be enhanced by combining different kinds of high-throughput experimental data, but the use of incompatible identifiers makes data integration a challenge. We aimed to improve methods for integrating and visualizing different types of omics data. To validate these methods, we applied them to two previous studies on starvation in mice, one using proteomics and the other using transcriptomics technology. We extended the PathVisio software with new plugins to link proteins, transcripts and pathways. A low overall correlation between proteome and transcriptome data was detected (Spearman rank correlation: 0.21). At the level of individual genes, correlation was highly variable. Many mRNA/protein pairs, such as fructose biphosphate aldolase B and ATP Synthase, show good correlation. For other pairs, such as ferritin and elongation factor 2, an interesting effect is observed, where mRNA and protein levels change in opposite directions, suggesting they are not primarily regulated at the transcriptional level. We used pathway diagrams to visualize the integrated datasets and found it encouraging that transcriptomics and proteomics data supported each other at the pathway level. Visualization of the integrated dataset on pathways led to new observations on gene-regulation in the response of the gut to starvation. Our methods are generic and can be applied to any multi-omics study. The PathVisio software can be obtained at http://www.pathvisio.org. Supplemental data are available at http://www.bigcat.unimaas.nl/data/jib-supplemental/, including instructions on reproducing the pathway visualizations of this manuscript.