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")Copyright (c) 2019 Emil Hvitfeldt
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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First we apply the meth_aggregate() function to the included example
dataset fake_methylation. 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
#> # Methcon object
#> # .id: gene
#> # .value: meth
#> # 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
#> # Methcon object
#> # .id: gene
#> # .value: meth
#> # 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 0.5 0.49 0.43
#> 2 2 0.817 6 0 0.01 0.01
#> 3 3 0.577 5 0.22 0.35 0.28
#> 4 4 0.279 9 0.99 0.93 0.85
#> 5 5 0.318 5 0.94 0.84 0.71
#> 6 6 0.427 6 0.8 0.6 0.71
#> 7 7 0.736 4 0.05 0.16 0.11
#> 8 8 0.546 2 0.41 0.42 0.41
#> 9 9 0.328 7 0.97 0.86 0.82
#> 10 10 0.202 6 1 1 1
#> # … 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.