One way of separating sources from a single mixture recording is by extracting spectral components and then combining them to form estimates of the sources. The grouping process remains a difficult problem. We propose, for instances when multiple mixture signals are available, clustering the components based on their relative contribution to each mixture (i.e., their spatial position). We introduce novel factorizations of magnitude spectrograms from multiple recordings and derive update rules that extend independent subspace analysis and non-negative matrix factorization to concurrently estimate the spectral shape, time envelope and spatial position of each component. We show that estimated component positions are near the position of their corresponding source, and that multichannel non-negative matrix factorization can distinguish three pianos by their position in the mixture.