ggplot(ngs, aes(x = Count.DNA)) + geom_histogram(binwidth = 100) + theme_bw()
Not negative binomial, more like lognormal.
ggplot(ngs, aes(x = Count.DNA, y = GC)) + geom_point(alpha = 0.1) + theme_bw()
Perhaps a weak corrleation between increased reads and increased GC, but as I show below, it has more to do with low GC leading to high expression leading to slower growth rates.
ggplot(ngs, aes(x = Count.DNA, y = Prot)) + geom_point(alpha = 0.1) + theme_bw()
DNA count versus Protein level. Highly expressed constructs have fewer DNA reads (i.e. they grow more slowly).
ggplot(ngs, aes(x = Count.DNA, y = log10(RNA), color = Promoter)) + geom_point(alpha = 0.1) +
theme_bw()
DNA count versus RNA level (RNA/DNA). The stronger promoter (red) has more constructs with fewer reads.