In TeamMacLean/atacr: Analysing Capture Seq Count Data

The atacr package provides functions for getting quick summaries of your data. An overview comes from summary()

library(atacr)
counts <- simulate_counts()

summary(counts)

which shows the on and off target hit counts, the quantiles and the mean read depths.

The count distributions across the bait and non-bait windows by sample can be plotted quickly with coverage_summary().

coverage_summary(counts)

Diagnostic plots

It is possible to look coverage in a given data set and look at raw counts.

plot_counts(counts, which = "bait_windows", log10 = FALSE)

Low counts in windows

The number of windows below a threshold for each experiment can be seen with windows_below_coverage_threshold_plot(), and you can set the lower and upper bounds with the to and from arguments.

windows_below_coverage_threshold_plot(counts, from = 5, to = 25)

MA plots

MA plots of sample count versus all sample median count - to highlight odd looking experiments and extreme outliers - can be displayed with ma_plot(). By default this will use the bait_windows data, but you can set the which argument to use other subsets, e.g non_bait_windows

ma_plot(counts)

Per chromosome plots

These are bar charts of coverage at the windows across the chromosomes (seqnames) provided in the data.

plot_count_by_chromosome(counts)

Sample comparison plots

A matrix of correlation between counts in the samples can be plot with the sample_correlation_plot() function. In this plot the colour and size scale of the dots represents the Pearson correlation coefficient. Pairwise comparisons with p < 0.05 have a blank space.