Targeted and successful cellular therapies for disease treatment require an extensive mapping of the complex structure and dynamics of molecular mechanisms which determine the behaviour and function of cell. CELL-seq is a genome-wide screening procedure measuring specific and targeted protein quantities as phenotypic readouts and is employed by the Netherlands Cancer Institute to analyze which genes regulate the protein state of interest. This research aims to explore the current compendium of CELL-seq screens that investigate a range of phenotypes, to create a mapping of gene-gene associations that share similar phenotypic profiles and elucidate biology that is hard to uncover with more conventional screening techniques.

We perform exploratory research to investigate the ability of the screen compendium to show network structures that reflect known biological processes. We find that with stringent requirements on interactions the screen compendium shows enrichment for a wide range of biological processes and known protein-protein interactions. We further conclude that the experimental design biases network behaviour and needs to be accounted for when constructing networks. We recommended a mutual k-nearest neighbor network construction approach, which yielded networks with the most biological relevance.
We compare the CELL-seq screens using findings from the approaches to the DepMap dataset, a well-known collection of synthetic lethality CRISPR screens, and find that the behaviour of these datasets is in many ways mirrored. We conclude that this is both due to the biology they represent and the differences in the number of screens in each dataset. Finally, we compare the coverage of biological processes between the HAP1 compendium and DepMap, and show large overlap in their coverage. Nonetheless, the differences they do show leads us to bring forward two hypotheses for gene-gene interactions that score strongly uniquely in the CELL-seq networks which are biologically plausible but are not found in DepMap or curated literature, warranting future investigations.

All code pertaining to the methods and figures in this work are hosted on GitLab by the High Performance Computing Facility of the Netherlands Cancer Institute. As such the code can be viewed by supervisors, but further details could be shared upon request.

Personalized treatment methods for a complex disease such as cancer benefit from using multiple data modalities from a patient's cancer cells. Multiple modalities allow for analysis of dependencies between complex biological processes and downstream tasks, such as drug response and/or expected survival rate. To this end, it is important to gain an understanding of the relationships between modalities in tumor cells. Multimodal Variational Auto-Encoders (MVAEs) are a combination of generative models trained on different sets of data modalities. In this research, the ability of MVAEs to capture common information between different data views from the same tumor cells is assessed. MVAE models discussed here are a Mixture-of-Experts (MoE) and a Product-of-Experts (PoE) approach to combining the generative model posterior distributions into a single common latent space. The performance assessment is done by: i) comparing the loss of information when reconstructing the training data to MOFA+, a linear method for combining multimodal data, and ii) measuring if one modality of a tumor cell can generate another modality, based on characteristics of the latent space learned by the MVAE. Biological data modalities considered are RNA-seq, gene-level copy number and DNA methylation (DNAme), gathered by The Cancer Genome Atlas. It is found that PoE reconstructs data from all data types with a higher accuracy compared to MoE and MOFA+. The mean squared error of PoE's average reconstruction loss is about a quarter of MOFA+'s, and less than a seventh of the MoE's average reconstruction loss. In terms of predicting modalities from other modalities, the PoE again outperforms MoE on all cross-modal predictions. Additionally, it can be concluded that both models have higher losses in their prediction of DNAme from other modalities, indicating a lesser correlation between this data type and the others.