Plot multiple metagene data objects on a single plot.

Plots multiple metagene object using the raw data generated by score_metagene().

multiple_metagenes(data_list, set_names, title, xaxis, yaxis, legend.title, col, se_alpha)

Arguments

data_list

List of score_metagene() raw data output

set_names

Character vector of names for score_metagene() object

title

Output plot title

xaxis

Output plot x-axis title

yaxis

Output plot y-axis title

legend.title

T/F, whether to show legend title

col

Vector of colours to use for lines

se_alpha

0-1, alpha value for standard error shading

Value

Returns a grob containing a plot of the input metagene data.

Examples

ranks <- getPCRanks(eigen, IDs = c("trt", "ctl"), PC = 1)
DMRs <- Get_Novel_DMRs(ranks, 2940, minCpGs=10)
#> Splitting data by chromosome...
#> Bootstrapping background distributions for each chromosome...
#> Compressing nearby seeds... 
#> done! Collapsed 2940 seeds to 250 seeds!
#> 
#> Expanding DMRs from 250 seeds...
#> Trimming 133 DMRs...
#> done!

# Select all significantly hypomethylated DMRs:
hypo_DMRs <- DMRs[DMRs$FDR <= 0.05 & DMRs$DMR_Zscore < 0,] 
# Select all significantly hypermethylated DMRs:
hyper_DMRs <- DMRs[DMRs$FDR <= 0.05 & DMRs$DMR_Zscore > 0,]

# select chrom, start, and end of all hyper DMRs
regions_hypo <- hypo_DMRs[c(1:3)] 
regions_hyper <- hyper_DMRs[c(1:3)] 

# return.data = T returns raw data instead of a plot:
hyper_metagene <- score_metagene(ranks, regions_hyper, return.data = TRUE)
#> Creating chromDict Object. If you plant to run the score_metagene() function multiple times, it is strongly recommended to generate a chromDict Object with the chromDict() function, and specify it with score_metagene(..., chromDictObj=OBJECT). This is the most computationally intensive part of metagene creation, and only needs to be done once.
hypo_metagene <- score_metagene(ranks, regions_hypo, return.data = TRUE)
#> Creating chromDict Object. If you plant to run the score_metagene() function multiple times, it is strongly recommended to generate a chromDict Object with the chromDict() function, and specify it with score_metagene(..., chromDictObj=OBJECT). This is the most computationally intensive part of metagene creation, and only needs to be done once.

# The multiple_metagenes function plots multiple metagenes 
# using a list of raw data objects from score_metagene().
multiple_metagenes(data_list = list(hyper_metagene, hypo_metagene),
set_names = c("Hyper DMRs", "Hypo DMRs"),
title="Metagenes of DMR Regions", legend.title = FALSE)