ATAC-seq is a recently developed method to identify the areas of open chromatin 
in a cell. These regions usually correspond to active regulatory elements and 
their location profile is unique to a given cell type. When done at single-cell 
resolution, ATAC-seq provides an insight into the cell-to-cell variability that 
emerges from otherwise identical DNA sequences by identifying the variability in 
the genomic location of open chromatin sites in each of the cells. This paper 
presents Scasat (single-cell ATAC-seq analysis tool), a complete pipeline to 
process scATAC-seq data with simple steps. Scasat treats the data as binary and 
applies statistical methods that are especially suitable for binary data. The 
pipeline is developed in a Jupyter notebook environment that holds the 
executable code along with the necessary description and results. It is robust, 
flexible, interactive and easy to extend. Within Scasat we developed a novel 
differential accessibility analysis method based on information gain to identify 
the peaks that are unique to a cell. The results from Scasat showed that open 
chromatin locations corresponding to potential regulatory elements can account 
for cellular heterogeneity and can identify regulatory regions that separates 
cells from a complex population.