test_reg_mixed: Logistic regression with random effects results for...

In geneticsMiNIng/metR: Tools for Identification and Visualisation of Differentially Methylated Regions

Description

Get p.value and beta coefficient from grouping variable from logistic regression with random effects based on number of methylated and unmethylated citozines in two probes. This function doesn't respect tiles or tiles.common column. This function is using in find.DMR and can be used sepparately.

Usage

test_reg_mixed(data)

Arguments

data

There are two options: 1. dataframe with specific columns: chr, poz, prob, no, meth, unmeth, meth.rate. This dataframe is result of function preprocessing. 2. dataframe with specific columns: chr, poz, prob, no, meth, unmeth, meth.rate, tiles and possible tiles.common columns. This dataframe is result of function create.tiles.min.gap or create.tiles.fixed.length.

Value

vector with p.value and beta coef. from grouping variable from logistic regression with random effects or two-elemented vector of na values if something goes wrong

Examples

data('schizophrenia')
control <- schizophrenia %>% filter(category == 'control') %>%
dplyr::select(-category)

disease <- schizophrenia %>% filter(category == 'disease') %>%
 dplyr::select(-category)

data <- preprocessing(control, disease)
data.tiles <- create_tiles_max_gap(data, gaps.length = 100)
data.test <- data.tiles %>% filter(tiles == 10)
test_reg_mixed(data.test)
# or by some self-defined regions:
data.test.2 <- data.tiles %>% filter(chr == 'chr1', poz > 80000, poz < 100000)
test_reg_mixed(data.test.2)

geneticsMiNIng/metR documentation built on May 28, 2019, 8:41 p.m.