Extended Data Fig. 1: Comparison of read and fragment counts.
From: Modeling fragment counts improves single-cell ATAC-seq analysis
a, b) Read count (a) and fragment count (b) distribution on the Satpathy dataset14. c, d) Read count (c) and fragment count (d) distribution of the sci-ATAC-seq3 dataset16. Plotted is a 10% random subset as the dataset consists of ~700 K cells. e) Fragment count distribution on the fly dataset15. CellRanger ATAC read counts were unavailable for this dataset as we generated fragment counts directly with Signac. f, g) Pie chart showing the percentage of all non-zero peaks with 1, 2, or more than 2 reads for the Satpathy dataset (f), sciATAC-seq3 dataset (10% random subset) (g). h) Pie chart with the percentage of all non-zero peaks with one or more than one fragment for the fly dataset (read counts are not available for this dataset). i, j) Variance of read counts across cells against mean read counts for the Satpathy dataset (i) and sciATAC-seq3 dataset (j). Each dot represents one peak region. When fragment ends (reads) are counted, the variance of read counts is around twice the mean (gray dotted line), which is not consistent with a Poisson distribution (solid gray line). k, l, m) Same as (i, j), but for fragment counts.
