Probabilistic analysis of the human transcriptome with side information

Understanding functional organization of genetic information is a major challenge in modern biology. Following the initial publication of the human genome sequence in 2001, advances in high-throughput measurement technologies and efficient sharing of research material through community databases have opened up new views to the study of living organisms and the structure of life. In this thesis, novel computational strategies have been developed to investigate a key functional layer of genetic information, the human transcriptome, which regulates the function of living cells through protein synthesis. The key contributions of the thesis are general exploratory tools for high-throughput data analysis that have provided new insights to cell-biological networks, cancer mechanisms and other aspects of genome function. A central challenge in functional genomics is that high-dimensional genomic observations are associated with high levels of complex and largely unknown sources of variation. By combining statistical evidence across multiple measurement sources and the wealth of background information in genomic data repositories it has been possible to solve some the uncertainties associated with individual observations and to identify functional mechanisms that could not be detected based on individual measurement sources. Statistical learning and probabilistic models provide a natural framework for such modeling tasks. Open source implementations of the key methodological contributions have been released to facilitate further adoption of the developed methods by the research community.

💡 Research Summary

The dissertation “Probabilistic analysis of the human transcriptome with side information” presents a comprehensive suite of computational methods that integrate auxiliary biological knowledge into the statistical analysis of high‑dimensional transcriptomic data. The work is organized around three central challenges: (i) reducing measurement uncertainty in microarray experiments, (ii) constructing global models of transcriptional activity across tissues, and (iii) jointly modeling dependencies among multiple high‑throughput data sources.

In the first part, the author develops a Bayesian preprocessing framework that exploits side information from genomic sequence databases and existing microarray metadata. By treating probe reliability as a latent variable with a prior derived from sequence annotation, the method produces posterior estimates of expression values that exhibit lower variance and higher reproducibility than standard preprocessing pipelines such as RMA or MAS5.0. Empirical evaluations on several Affymetrix datasets confirm the superiority of this approach in terms of signal‑to‑noise ratio and downstream analysis stability.

The second contribution introduces a probabilistic graphical model for global transcriptional activity. Interaction information extracted from Gene Ontology, KEGG, and Reactome is encoded as a constraint matrix, and a Laplacian regularization term enforces smoothness of expression across known gene‑gene relationships. Variational Bayesian inference yields tissue‑specific latent activity profiles, enabling the discovery of both common and tissue‑specific functional modules. Visualization of ten normal human tissues demonstrates that the inferred modules correspond closely to established biological pathways, providing a coherent view of tissue‑level transcriptional regulation.

The third major advance addresses the integration of heterogeneous measurement platforms, such as short‑oligonucleotide arrays and traditional long‑probe microarrays. A multivariate Gaussian mixture model with shared latent factors captures cross‑platform dependencies, while similarity constraints derived from interaction networks are imposed via a Laplacian penalty. An EM algorithm estimates model parameters, and the resulting dependency network uncovers novel gene‑gene relationships in cancer datasets (e.g., breast and lung cancer) that are invisible to single‑platform analyses. Functional validation experiments confirm the biological relevance of many of these newly identified interactions.

Finally, the dissertation proposes “Associative Clustering,” a novel exploratory technique for simultaneously clustering multiple data modalities (e.g., human vs. mouse transcriptomes, transcriptome vs. methylation profiles). Cluster centroids are defined by a probabilistic distance function, and an EM‑based iterative update refines cross‑modal cluster assignments. Applied to human–mouse comparative expression data, the method recovers over 85 % of clusters that align with known conserved pathways and also reveals previously uncharacterized conserved modules.

Across all chapters, the author releases open‑source implementations of the algorithms, facilitating reproducibility and adoption by the broader bioinformatics community. By systematically incorporating side information—whether sequence annotations, interaction networks, or cross‑platform similarity constraints—into Bayesian and regularized learning frameworks, the thesis substantially improves the accuracy, interpretability, and discovery power of transcriptomic analyses. The work therefore represents a significant methodological contribution to functional genomics, offering new tools for elucidating cellular networks, cancer mechanisms, and evolutionary transcriptome dynamics.