Title: BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids
Citation: He, C., Kalafut, N. C., Sandoval, S. O., Risgaard, R., Sirois, C. L., Yang, C., Khullar, S., Suzuki, M., Huang, X., Chang, Q., Zhao, X., Sousa, A. M. M., & Wang, D. (2023). BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids. Cell Reports Methods, 3(2), 100409. https://doi.org/10.1016/j.crmeth.2023.100409
Abstract: Our machine-learning framework, brain and organoid manifold alignment (BOMA), first performs a global alignment of developmental gene expression data between brains and organoids. It then applies manifold learning to locally refine the alignment, revealing conserved and specific developmental trajectories across brains and organoids. Using BOMA, we found that human cortical organoids better align with certain brain cortical regions than with other non-cortical regions, implying organoid-preserved developmental gene expression programs specific to brain regions. Additionally, our alignment of non-human primate and human brains reveals highly conserved gene expression around birth. Also, we integrated and analyzed developmental single-cell RNA sequencing (scRNA-seq) data of human brains and organoids, showing conserved and specific cell trajectories and clusters. Further identification of expressed genes of such clusters and enrichment analyses reveal brain- or organoid-specific developmental functions and pathways. Finally, we experimentally validated important specific expressed genes through the use of immunofluorescence. BOMA is open-source available as a web tool for community use.
About the Lab: The Wang lab focuses on the development of interpretable machine learning approaches and bioinformatics tools for understanding the functional genomics, molecular and cellular mechanisms from genotype to phenotype.
Investigator: Daifeng Wang, PhD