  We describe a new methodology for studying persistence of topological
features across a family of spaces or point-cloud data sets, called zigzag
persistence. Building on classical results about quiver representations, zigzag
persistence generalises the highly successful theory of persistent homology and
addresses several situations which are not covered by that theory. In this
paper we develop theoretical and algorithmic foundations with a view towards
applications in topological statistics.
