library(purrr)
library(dplyr)
library(magrittr)
load("~/Qual/data/varInfoWithHistoneMarkAnnotations.RData")
varInfo %>%
select(11:27) %>%
select(-VariantShift) %>%
gather(markType, value, -DeepSeaDnaase) %>%
split(.$markType) %>%
map(~ lm(DeepSeaDnaase ~ value, data = .)) %>%
map(summary)
varInfo %>%
select(12:27) %>%
gather(markType, value, -VariantShift) %>%
split(.$markType) %>%
map(~ lm(VariantShift ~ value, data = .)) %>%
map(summary)
varInfo %>%
select(VariantShift, BroadK562H3k36me3) %>%
ggplot(aes(BroadK562H3k36me3, VariantShift)) +
geom_point()
#So in isolation none of these are amazing predictors for transcriptional shift
# Let's try LASSO
library(glmnet)
dat = varInfo %>%
select(12:27) %>% as.matrix
fit = glmnet(dat[,-1], dat[,1],
alpha = 1)
cv.fit = cv.glmnet(dat[,-1], dat[,1],
alpha = 1)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.