Causal machine learning for single-cell genomics

Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells. When combined with large-scale perturbation screens, through which specific biological mechanisms can be targeted, these technologies allow for measuring the effect of targeted perturbations on the whole transcriptome. These advances provide an opportunity to better understand the causative role of genes in complex biological processes such as gene regulation, disease progression or cellular development. However, the high-dimensional nature of the data, coupled with the intricate complexity of biological systems renders this task nontrivial. Within the machine learning community, there has been a recent increase of interest in causality, with a focus on adapting established causal techniques and algorithms to handle high-dimensional data. In this perspective, we delineate the application of these methodologies within the realm of single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology and discuss and challenge the assumptions it entails from the biological point of view. We then identify open problems in the application of causal approaches to single-cell data: generalising to unseen environments, learning interpretable models, and learning causal models of dynamics. For each problem, we discuss how various research directions - including the development of computational approaches and the adaptation of experimental protocols - may offer ways forward, or on the contrary pose some difficulties. With the advent of single cell atlases and increasing perturbation data, we expect causal models to become a crucial tool for informed experimental design.Comment: 35 pages, 7 figures, 3 tables, 1 bo

Learning knowledge representations to predict and uncover novel drugs

The recent advances in single-cell experimental technologies have opened the door to a broad study of cell perturbations such as drugs or gene knock- outs. Knowing how a cell would respond to a certain perturbation can boost drug discovery field accelerating the development of new drugs and therapies. Nevertheless, the perturbation search space is so large that an exhaustive classical search is not feasible. For this reason, computational methods to predict the perturbation response and guide the search must be developed. Those computational methods must be able to work out-of- distribution and predict the behaviour of cells under unknown perturbations, since not possible combinations of perturbations can be seen in training time. We hypothesise that prior biological existing knowledge can help current Deep Learning systems to perform better OOD and generalize. To do so, in this thesis we present a system that incorporates prior biological knowledge into Deep Learning systems structuring the data using Gene Regulatory Networks (GRN) and feeding these data to Graph Neural Networks (GNN). We explore, in a biological problem, the idea of using prior existing knowledge about the nature of the system to regularize the models in such a way that their OOD performance improves. We propose different architectures: from ones that trust exclusively in prior knowledge graph structured data to others that merge prior knowledge-driven embeddings and tabular data embeddings. We show that, unfortunately, leveraging GRN to encode the data in such a way that prior knowledge is exploited is useful for doing in-distribution predictions but it is not for OOD settings. Finally, we point out at the current state of the existing prior knowledge as the main bottleneck of the performance of the system