Cell Ranger ATAC6.1 (latest), printed on 07/03/2022
If you have used Loupe Browser to analyze Gene Expression data, exploring ATAC data will seem familiar in some ways. The Cell Ranger ATAC algorithm documentation covers algorithms and analysis in detail. Here are some key things to keep in mind when looking at ATAC data.
UMI count per cell is the unit of gene expression. Cut sites per cell is the unit of accessibility.
Genes are the rows of a Gene Expression matrix. Peaks are the rows of a Chromatin Accessibility matrix.
Peaks are genomic regions where there were significant upticks in fragment cut sites, which indicate regions of open chromatin. They are named by their location (e.g., "chr1:10244-10510")
Unlike genes, peaks are likely to be different between different datasets.
There are typically more distinct peaks in an ATAC dataset than there are genes in a reference.
The dynamic range of gene expression per cell is typically much wider than the dynamic range of cut sites per peak per cell. This means that you will often use aggregate features (see below) to separate data.
In addition to peaks, there are several aggregate feature types which can also be used to differentiate cells:
An ATAC dataset takes up several times as much disk space (per cell) than a Gene Expression dataset.
To see fragment locations per cluster in high resolution, you need access to the
for that run, generated by the Cell Ranger ATAC pipeline. These files are bundled because they are typically several times larger than
.cloupe file. You can either specify the location of this file on a locally mounted file system, or on the web via a URL.