Genes in Space

Objectives

The goal of this project is to increase the statistical power of current genome-wide association studies (GWAS) for brain disorders and aid in their interpretation. This is done by linking genomic variations to observational features in population imaging studies – both imaging and genetic data – using prior information of spatio-temporal gene expression patterns in the brain.

Recent results

The Allen Brain Atlas gene expression data is a very rich source on the genetics of the mammalian brain. We have explored the use of t-distributed stochastic neighbourhood embedding (t-SNE) for retrieving structure in this data. The results show that neuroanatomical regions can be consistently separated based on their expression characteristics. The non-linear method t-SNE captures local similarities much better than for instance PCA and MDS and reveals interesting structural patterns (Mahfouz et al. 2015). Also, we have demonstrated a strong relation between chromatic folding structing of chromosomes in the cell nucleus and the spatial co-expression of genes in the mouse cortex as derived from the Allen Brain Atlas (Babei e.a, 2015).

Left: A 2D embedding of the Allen Mouse Brain Atlas using t-SNE, coloured by anatomical region membership according to the Allen Reference Atlas. Middle: Allen Mouse Brain Atlas mapped back to the 3D volume of the reference atlas and coloured by the L*a*b* colour-map of the t-SNE embedding. Right: Divergence plot for the t-SNE map, showing the similarity between pairs of neuroanatomical regions.

Publications

1. Babaei, S., Mahfouz, A., Hulsman, M., Lelieveldt, B.P.F., de Ridder, J., & Reinders, M.J.T. (2015). Hi-C chromatin interaction networks predict co-expression in the mouse cortex. (In Press)
2. Mahfouz, A., van de Giessen, M., van der Maaten, L., Huisman, S., Reinders, M., Hawrylycz, M. J., & Lelieveldt, B. P. F. (2014). Visualizing the spatial gene expression organization in the brain through non-linear similarity embeddings. Methods (San Diego, Calif.)
3. Mahfouz, A., Meijer, O. C., Lelieveldt, B. P., & Reinders, M. J. (2014). Predicting targets and signaling pathways of steroid hormones using the Allen Brain Atlas. Frontiers in Neuroinformatics.
4. Mahfouz, A., Ziats, M. N., Rennert, O. M., Lelieveldt, B. P. F., & Reinders, M. J. T. (2014). Genomic connectivity networks based on the BrainSpan atlas of the developing human brain. In SPIE Medical Imaging