Topological data analysis (TDA) allows one to extract rich information from structured data (such as graphs or time series) that occurs in modern machine learning problems. This information will be represented as descriptors such as persistence diagrams, which can be described as point measures supported on a half-plane. While persistence diagrams are not elements of a vector space, they can still be compared using partial matching metrics. The similarities between these metrics and those routinely used in optimal transport—another ﬁeld of mathematics—are known for long, but a formal connection between these two ﬁelds is yet to come.The purpose of this thesis is to clarify this connection and develop new theoretical and computational tools to manipulate persistence diagrams, targeting statistical applications. First, we show how optimal partial transport with boundary, a variation of classic optimal transport theory, provides a formalism that encompasses standard metrics in TDA. We then show-case the beneﬁts of this connection in different situations: a theoretical study and the development of an algorithm to perform fast estimation of barycenters of persistence diagrams, the characterization of continuous linear representations of persistence diagrams and how to learn such representations using a neural network, and eventually a stability result in the context of linearly averaging random persistence diagrams.