The goal of methcon5 is to identify and rank CpG DNA methylation conservation along the human genome. Specifically, it includes bootstrapping methods to provide ranking which should adjust for the differences in length as without it short regions tend to get higher conservation scores.
The following repository includes an analysis in which this package was used.
Please note that the name of the package is in all lowercase.
You can install the released version of methcon5 from
CRAN with:
install.packages("methcon5")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("EmilHvitfeldt/methcon5")First we apply the meth_aggregate function. This will take the columns
specified in value and apply the fun stratified according to id.
In this case, we want to calculate the mean meth value within each gene.
library(methcon5)
sample_ii <- fake_methylation %>%
meth_aggregate(id = gene, value = meth, fun = mean)
sample_ii
#> # A tibble: 500 x 3
#> gene meth n
#> * <int> <dbl> <int>
#> 1 1 0.509 10
#> 2 2 0.817 6
#> 3 3 0.577 5
#> 4 4 0.279 9
#> 5 5 0.318 5
#> 6 6 0.427 6
#> 7 7 0.736 4
#> 8 8 0.546 2
#> 9 9 0.328 7
#> 10 10 0.202 6
#> # … with 490 more rowsNext we use the meth_bootstrap function. This will take the summarized
data.frame calculated earlier along with the original dataset. The
function with return the original data.frame with the new column
attached to the end, which makes it ideal for piping to apply different
methods to the same data.
adjusted <- sample_ii %>%
meth_bootstrap(reps = 100) %>%
meth_bootstrap(reps = 100, method = "perm_v2") %>%
meth_bootstrap(reps = 100, method = "perm_v3")
adjusted
#> # A tibble: 500 x 6
#> gene meth n meth_perm_v1 meth_perm_v2 meth_perm_v3
#> * <int> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 1 0.509 10 0.52 0.6 0.39
#> 2 2 0.817 6 0 0.01 0
#> 3 3 0.577 5 0.28 0.33 0.42
#> 4 4 0.279 9 1 0.91 0.9
#> 5 5 0.318 5 0.96 0.81 0.76
#> 6 6 0.427 6 0.78 0.69 0.63
#> 7 7 0.736 4 0.05 0.16 0.15
#> 8 8 0.546 2 0.38 0.42 0.54
#> 9 9 0.328 7 1 0.79 0.81
#> 10 10 0.202 6 1 1 0.95
#> # … with 490 more rowslibrary(ggplot2)
ggplot(adjusted, aes(meth_perm_v1, meth_perm_v2, color = n)) +
geom_point() +
scale_color_viridis_c() +
theme_minimal()
We gratefully acknowledge funding from NIH awards 1P01CA196569 and R21 CA226106.
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