As experimental methods for measuring aspects of chromatin state at single-cell resolution continue to be developed and improved, the parallel development of computational tools designed to analyze these datasets becomes increasingly important. Here, we developed Signac for the analysis of single-cell chromatin data and demonstrated running key analysis steps using Signac for the analysis of both unimodal and multimodal single-cell chromatin datasets. These analysis steps can be scaled to datasets containing >700,000 cells and the scalability of these methods will become particularly important as large-scale cell atlas projects are completed53. We further developed a simple modification to the popular LSI dimension reduction method that improved the performance of LSI when applied to single-cell chromatin data, particularly for datasets with low sensitivity. Furthermore, Signac enables running other tools developed by the community for the analysis of single-cell chromatin data, including chromVAR for estimating DNA motif variability between cells32, Monocle for building pseudotime trajectories54, Cicero for finding co-accessible networks of peaks31 and Harmony for performing dataset integration55. While we have focused here on the analysis of DNA accessibility data alone, our suite
