Computational Models of Spatial Transcriptomes
Time: Wed 2024-01-31 10.00
Location: Air & Fire, Tomtebodavägen 23A, Solna
Video link: https://kth-se.zoom.us/j/68950542171
Language: English
Subject area: Biotechnology
Doctoral student: Ludvig Bergenstråhle , Genteknologi, Science for Life Laboratory, SciLifeLab, Joakim Lundeberg
Opponent: Prof Ole Winther, University of Copenhagen
Supervisor: Professor Joakim Lundeberg, Genteknologi, Science for Life Laboratory, SciLifeLab; Professor Jens Lagergren, Beräkningsvetenskap och beräkningsteknik (CST), Science for Life Laboratory, SciLifeLab
QC 2024-01-09
Abstract
Spatial biology is a rapidly growing field that has seen tremendous progress over the last decade. We are now able to measure how the morphology, genome, transcriptome, and proteome of a tissue vary across space. Datasets generated by spatial technologies reflect the complexity of the systems they measure: They are multi-modal, high-dimensional, and layer an intricate web of dependencies between biological compartments at different length scales. To add to this complexity, measurements are often sparse and noisy, obfuscating the underlying biological signal and making the data difficult to interpret. In this thesis, we describe how data from spatial biology experiments can be analyzed with methods from deep learning and generative modeling to accelerate biological discovery. The thesis is divided into two parts. The first part provides an introduction to the fields of deep learning and spatial biology, and how the two can be combined to model spatial biology data. The second part consists of four papers describing methods that we have developed for this purpose. Paper I presents a method for inferring spatial gene expression from hematoxylin and eosin stains. The proposed method offers a data-driven approach to analyzing histopathology images without relying on expert annotations and could be a valuable tool for cancer screening and diagnosis in the clinics. Paper II introduces a method for jointly modeling spatial gene expression with histology images. We show that the method can predict super-resolved gene expression and transcriptionally characterize small-scale anatomical structures. Paper III proposes a method for learning flexible Markov kernels to model continuous and discrete data distributions. We demonstrate the method on various image synthesis tasks, including unconditional image generation and inpainting. Paper IV leverages the techniques introduced in Paper III to integrate data from different spatial biology experiments. The proposed method can be used for data imputation, super resolution, and cross-modality data transfer.