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)
It is possible to look coverage in a given data set and look at raw counts.
plot_counts(counts, which = "bait_windows", log10 = FALSE)
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 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)
These are bar charts of coverage at the windows across the chromosomes (seqnames
) provided in the data.
plot_count_by_chromosome(counts)
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.
sample_correlation_plot(counts)
A PCA plot that clusters the most simlar samples can also be generated using the sample_pca_plot()
function.
sample_pca_plot(counts)
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