The paper introduces chromVAR, an R package designed to analyze sparse chromatin accessibility data from single-cell ATAC-seq (scATAC) experiments. The package addresses the challenges posed by the sparse nature of single-cell epigenomic data by estimating gain or loss of accessibility within peaks sharing the same motif or annotation while controlling for technical biases. chromVAR enables accurate clustering of scATAC-seq profiles and characterizes known and de novo sequence motifs associated with variation in chromatin accessibility. The package takes aligned sequencing reads, chromatin accessibility peaks, and a set of chromatin features (motif position weight matrices or genomic annotations) as inputs. It computes "raw accessibility deviations" for each motif and cell, which are then bias-corrected to account for technical confounders. These bias-corrected deviations and z-scores can be used for downstream analyses, including de novo clustering of cells and identification of key regulators. The authors demonstrate the robustness of chromVAR through simulations and real-world data, showing that it can effectively identify transcription factor (TF) motifs that define different cell types and vary within populations. The package also includes tools for generating analyses of TF binding site correlation and chromatin variability, and for de novo motif assembly. chromVAR is expected to be broadly applicable to single-cell and bulk epigenomics data, providing an unbiased characterization of cell types and their defining *trans* regulators.The paper introduces chromVAR, an R package designed to analyze sparse chromatin accessibility data from single-cell ATAC-seq (scATAC) experiments. The package addresses the challenges posed by the sparse nature of single-cell epigenomic data by estimating gain or loss of accessibility within peaks sharing the same motif or annotation while controlling for technical biases. chromVAR enables accurate clustering of scATAC-seq profiles and characterizes known and de novo sequence motifs associated with variation in chromatin accessibility. The package takes aligned sequencing reads, chromatin accessibility peaks, and a set of chromatin features (motif position weight matrices or genomic annotations) as inputs. It computes "raw accessibility deviations" for each motif and cell, which are then bias-corrected to account for technical confounders. These bias-corrected deviations and z-scores can be used for downstream analyses, including de novo clustering of cells and identification of key regulators. The authors demonstrate the robustness of chromVAR through simulations and real-world data, showing that it can effectively identify transcription factor (TF) motifs that define different cell types and vary within populations. The package also includes tools for generating analyses of TF binding site correlation and chromatin variability, and for de novo motif assembly. chromVAR is expected to be broadly applicable to single-cell and bulk epigenomics data, providing an unbiased characterization of cell types and their defining *trans* regulators.