We propose a nonparametric Bayesian sparse factor analysis model that accounts for uncertainty in the number of factors and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We apply this model to two problems in gene-expression analysis.