#wd <- "/Users/usri/Desktop/Camk2a.Sep26.24.analysis/Camk2a.16.OCT24"
library(WGCNA)
## Warning: package 'WGCNA' was built under R version 4.3.3
## Loading required package: dynamicTreeCut
## Loading required package: fastcluster
##
## Attaching package: 'fastcluster'
## The following object is masked from 'package:stats':
##
## hclust
##
##
## Attaching package: 'WGCNA'
## The following object is masked from 'package:stats':
##
## cor
library(tidyverse)
## Warning: package 'ggplot2' was built under R version 4.3.2
## Warning: package 'tidyr' was built under R version 4.3.2
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0.9000 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)
library(org.Hs.eg.db)
## Loading required package: AnnotationDbi
## Warning: package 'AnnotationDbi' was built under R version 4.3.2
## Loading required package: stats4
## Loading required package: BiocGenerics
## Warning: package 'BiocGenerics' was built under R version 4.3.1
##
## Attaching package: 'BiocGenerics'
##
## The following objects are masked from 'package:lubridate':
##
## intersect, setdiff, union
##
## The following objects are masked from 'package:dplyr':
##
## combine, intersect, setdiff, union
##
## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs
##
## The following objects are masked from 'package:base':
##
## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
## table, tapply, union, unique, unsplit, which.max, which.min
##
## Loading required package: Biobase
## Warning: package 'Biobase' was built under R version 4.3.1
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
##
## Loading required package: IRanges
## Warning: package 'IRanges' was built under R version 4.3.1
## Loading required package: S4Vectors
## Warning: package 'S4Vectors' was built under R version 4.3.2
##
## Attaching package: 'S4Vectors'
##
## The following objects are masked from 'package:lubridate':
##
## second, second<-
##
## The following objects are masked from 'package:dplyr':
##
## first, rename
##
## The following object is masked from 'package:tidyr':
##
## expand
##
## The following object is masked from 'package:utils':
##
## findMatches
##
## The following objects are masked from 'package:base':
##
## expand.grid, I, unname
##
##
## Attaching package: 'IRanges'
##
## The following object is masked from 'package:lubridate':
##
## %within%
##
## The following objects are masked from 'package:dplyr':
##
## collapse, desc, slice
##
## The following object is masked from 'package:purrr':
##
## reduce
##
##
## Attaching package: 'AnnotationDbi'
##
## The following object is masked from 'package:dplyr':
##
## select
library(org.Mm.eg.db)
##
library(pheatmap)
library(ggplot2)
library(ComplexHeatmap)
## Warning: package 'ComplexHeatmap' was built under R version 4.3.1
## Loading required package: grid
## ========================================
## ComplexHeatmap version 2.18.0
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
##
## If you use it in published research, please cite either one:
## - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
## - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
##
##
## The new InteractiveComplexHeatmap package can directly export static
## complex heatmaps into an interactive Shiny app with zero effort. Have a try!
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(ComplexHeatmap))
## ========================================
## ! pheatmap() has been masked by ComplexHeatmap::pheatmap(). Most of the arguments
## in the original pheatmap() are identically supported in the new function. You
## can still use the original function by explicitly calling pheatmap::pheatmap().
##
##
## Attaching package: 'ComplexHeatmap'
##
## The following object is masked from 'package:pheatmap':
##
## pheatmap
numericMetaMEGA <- readRDS("~/Desktop/Camk2a.Sep26.24.analysis/Camk2a.16.OCT24/numericMetaMEGA.rds")
netMEGA <- readRDS("~/Desktop/Camk2a.Sep26.24.analysis/Camk2a.16.OCT24/netMEGA.rds")
cleanDatMEGA <- readRDS("~/Desktop/Camk2a.Sep26.24.analysis/Camk2a.16.OCT24/cleanDatMEGA.rds")
cleanDat <- readRDS("~/Desktop/Camk2a.Sep26.24.analysis/Camk2a.16.OCT24/camk2a.cleanDat.after.outlier.removal.15samples.rds")
net <- readRDS("~/Desktop/Camk2a.Sep26.24.analysis/Camk2a.16.OCT24/camk2a.wgcna.net.rds")
netCAMK2A <- net
MEGAsymbols <- readRDS("~/Desktop/Camk2a.Sep26.24.analysis/Camk2a.16.OCT24/MEGAsymbols.rds")
#calculate and store the number of overlapping genes:
# Initialize a matrix to store the number of overlapping genes between each CAMK2A and MEGA module
camk2aModules <- unique(netCAMK2A$colors)
megaModules <- unique(netMEGA$colors)
camk2aSymbols <- readRDS("~/Desktop/Camk2a.Sep26.24.analysis/Camk2a.16.OCT24/camk2aSymbols.rds")
# Remove grey modules if applicable
camk2aModules <- camk2aModules[camk2aModules != "grey"]
megaModules <- megaModules[megaModules != "grey"]
# Initialize matrix to store overlap counts
overlapMatrix <- matrix(0, nrow = length(camk2aModules), ncol = length(megaModules))
rownames(overlapMatrix) <- camk2aModules
colnames(overlapMatrix) <- megaModules
# Calculate overlap for each CAMK2A and MEGA module pair
for (camk2aModule in camk2aModules) {
camk2aGenes <- camk2aSymbols[which(netCAMK2A$colors == camk2aModule)]
for (megaModule in megaModules) {
megaGenes <- MEGAsymbols[which(netMEGA$colors == megaModule)]
# Find overlapping genes between CAMK2A and MEGA module
overlap <- intersect(camk2aGenes, megaGenes)
# Store the number of overlapping genes
overlapMatrix[camk2aModule, megaModule] <- length(overlap)
}
}
# Print the overlap matrix
print(overlapMatrix)
## yellow tan greenyellow orange turquoise green black darkgreen
## pink 2 2 2 3 4 3 1 5
## greenyellow 0 0 1 0 2 3 0 3
## blue 11 4 3 3 28 11 4 6
## green 8 1 3 4 10 7 5 4
## yellow 6 2 4 4 16 3 2 2
## turquoise 9 5 6 7 32 12 6 2
## black 6 0 0 2 7 2 2 3
## red 6 4 1 3 11 7 4 3
## magenta 5 1 0 2 7 0 0 0
## tan 0 0 0 0 1 1 1 0
## brown 13 2 1 1 15 3 4 3
## purple 3 0 1 1 5 2 3 0
## salmon 2 1 0 0 1 1 0 0
## saddlebrown ivory brown steelblue darkred lightcyan1 pink salmon
## pink 2 2 1 0 0 0 0 1
## greenyellow 0 0 0 2 0 0 2 1
## blue 4 1 5 2 1 0 15 2
## green 2 0 4 1 0 0 5 1
## yellow 1 1 2 1 0 0 3 2
## turquoise 1 1 3 2 0 0 6 5
## black 0 1 2 1 0 0 2 0
## red 0 1 1 3 2 0 6 2
## magenta 0 1 1 1 1 0 1 0
## tan 0 0 3 0 0 0 0 0
## brown 2 0 2 2 1 0 6 1
## purple 1 0 2 1 0 0 2 0
## salmon 1 0 0 0 0 0 0 0
## white blue midnightblue darkorange cyan paleturquoise
## pink 0 3 0 0 6 2
## greenyellow 0 3 2 0 1 0
## blue 0 21 2 0 7 0
## green 0 14 2 1 5 0
## yellow 0 14 2 1 4 1
## turquoise 1 15 5 0 5 1
## black 0 2 1 0 1 0
## red 0 8 7 0 3 1
## magenta 0 4 4 0 1 0
## tan 0 2 1 0 3 0
## brown 0 12 7 1 3 1
## purple 0 4 1 0 4 1
## salmon 0 1 0 0 0 0
## darkolivegreen red darkgrey darkturquoise violet plum1 magenta
## pink 0 1 1 0 1 0 0
## greenyellow 0 1 0 1 0 0 1
## blue 0 3 2 0 2 2 1
## green 0 1 0 2 0 0 1
## yellow 1 1 0 2 1 1 0
## turquoise 3 3 1 1 4 2 0
## black 1 0 0 0 0 0 2
## red 0 0 0 0 1 1 1
## magenta 1 0 0 0 0 1 1
## tan 0 0 1 0 0 0 1
## brown 1 0 1 0 0 0 0
## purple 0 0 1 0 0 1 1
## salmon 0 0 0 0 0 0 0
## yellowgreen purple skyblue3 royalblue lightcyan lightyellow
## pink 1 0 1 0 0 0
## greenyellow 2 1 1 0 0 0
## blue 1 3 2 1 2 1
## green 0 3 2 0 1 0
## yellow 0 2 1 0 3 0
## turquoise 1 3 0 0 7 0
## black 2 0 0 0 1 0
## red 1 1 0 1 2 1
## magenta 1 0 1 0 0 0
## tan 0 0 0 0 0 0
## brown 1 1 1 0 2 1
## purple 0 1 0 0 1 1
## salmon 0 1 0 0 0 0
## lightgreen orangered4 grey60 lightsteelblue1 skyblue darkmagenta
## pink 1 0 0 1 1 0
## greenyellow 1 0 0 0 0 0
## blue 2 0 1 0 3 0
## green 0 1 0 0 3 1
## yellow 0 0 0 0 3 0
## turquoise 2 3 0 0 0 0
## black 0 0 0 0 2 1
## red 1 3 0 0 1 0
## magenta 0 0 1 0 0 0
## tan 0 1 0 0 2 1
## brown 2 1 0 0 1 1
## purple 0 0 0 1 1 0
## salmon 1 0 0 1 0 0
## sienna3 mediumpurple3 floralwhite
## pink 2 0 0
## greenyellow 0 0 0
## blue 1 0 3
## green 0 0 1
## yellow 1 0 2
## turquoise 1 0 6
## black 2 0 0
## red 0 0 0
## magenta 0 0 0
## tan 0 0 0
## brown 0 0 0
## purple 0 0 1
## salmon 0 0 0
#To determine which CAMK2A modules overlap the most with MEGA modules and then assess their statistical significance, you can use the following approach:
#Steps:
#1-Calculate the number of overlapping genes between each CAMK2A module and each MEGA module.
#2-Construct a contingency table for each pair of CAMK2A and MEGA modules.
#3-Perform a statistical test, such as Fisher’s exact test or a hypergeometric test, to evaluate the significance of the overlap between modules.
#4-Summarize the results to identify which CAMK2A modules have the most overlap with which MEGA modules and whether that overlap is statistically significant.
#1. Calculate Overlapping Genes Between CAMK2A and MEGA Modules
##Statistical Significance of the Overlap (Hypergeometric or Fisher’s Exact Test)
#2.1. Performing a Hypergeometric Test
#A hypergeometric test will test whether the overlap is greater than expected by chance, given the total number of genes in both datasets.
# Perform hypergeometric test for each CAMK2A-MEGA module pair
pvalMatrix <- matrix(NA, nrow = length(camk2aModules), ncol = length(megaModules))
rownames(pvalMatrix) <- camk2aModules
colnames(pvalMatrix) <- megaModules
# Total number of genes in the universe (total genes in both datasets combined)
totalGenes <- length(unique(c(camk2aSymbols, MEGAsymbols)))
for (camk2aModule in camk2aModules) {
camk2aGenes <- camk2aSymbols[which(netCAMK2A$colors == camk2aModule)]
numCamk2aGenes <- length(camk2aGenes)
for (megaModule in megaModules) {
megaGenes <- MEGAsymbols[which(netMEGA$colors == megaModule)]
numMegaGenes <- length(megaGenes)
overlap <- intersect(camk2aGenes, megaGenes)
numOverlap <- length(overlap)
# Hypergeometric test
pvalMatrix[camk2aModule, megaModule] <- phyper(numOverlap - 1, numCamk2aGenes, totalGenes - numCamk2aGenes, numMegaGenes, lower.tail = FALSE)
}
}
# Print p-value matrix
print(pvalMatrix)
## yellow tan greenyellow orange turquoise
## pink 0.6238139690 0.3300336 0.4178549 0.03836807 5.755144e-01
## greenyellow 1.0000000000 1.0000000 0.6178317 1.00000000 7.578316e-01
## blue 0.1352080087 0.6200609 0.8926047 0.50929486 5.358546e-04
## green 0.0463243339 0.8988086 0.5142032 0.05443058 2.351269e-01
## yellow 0.3154680127 0.7387716 0.3851620 0.08306062 1.487673e-02
## turquoise 0.3637003570 0.4383047 0.4265218 0.02002751 2.339155e-05
## black 0.0188426230 1.0000000 1.0000000 0.17589386 1.102920e-01
## red 0.2041586347 0.1840244 0.9358243 0.16809075 1.362418e-01
## magenta 0.0468616497 0.6772456 1.0000000 0.15729852 8.218141e-02
## tan 1.0000000000 1.0000000 1.0000000 1.00000000 7.839499e-01
## brown 0.0009190771 0.7543711 0.9634922 0.82239477 3.870414e-02
## purple 0.2669977146 1.0000000 0.7109662 0.47692364 2.596132e-01
## salmon 0.1653875025 0.3415915 1.0000000 1.00000000 7.675744e-01
## green black darkgreen saddlebrown ivory brown
## pink 0.26486485 0.8193571 0.001505317 0.1146342 0.01936605 0.9472364
## greenyellow 0.11545912 1.0000000 0.018498763 1.0000000 1.00000000 1.0000000
## blue 0.05908699 0.8623733 0.086030668 0.1584804 0.54618942 0.9799500
## green 0.05491744 0.2184302 0.078780434 0.3058112 1.00000000 0.8033498
## yellow 0.76166333 0.8905824 0.562269303 0.7281007 0.38169535 0.9887054
## turquoise 0.03238808 0.5893865 0.824250095 0.8872204 0.55319119 0.9986475
## black 0.54747716 0.5137101 0.054742679 1.0000000 0.19776367 0.7953914
## red 0.05273287 0.3944990 0.216433251 1.0000000 0.33742298 0.9962753
## magenta 1.00000000 1.0000000 1.000000000 1.0000000 0.18543682 0.9383667
## tan 0.48827580 0.4668749 1.000000000 1.0000000 1.00000000 0.0851671
## brown 0.77998077 0.5367798 0.304585826 0.3839383 1.00000000 0.9905792
## purple 0.45771533 0.1747175 1.000000000 0.3958750 1.00000000 0.7123845
## salmon 0.47166527 1.0000000 1.000000000 0.1856175 1.00000000 1.0000000
## steelblue darkred lightcyan1 pink salmon white
## pink 1.00000000 1.0000000 1 1.0000000000 0.6924775 1.0000000
## greenyellow 0.04738233 1.0000000 1 0.3054007241 0.5439640 1.0000000
## blue 0.56903819 0.9645232 1 0.0009992517 0.9268487 1.0000000
## green 0.63713294 1.0000000 1 0.2134766911 0.8959115 1.0000000
## yellow 0.69059320 1.0000000 1 0.7179700973 0.7325502 1.0000000
## turquoise 0.57959984 1.0000000 1 0.5809242941 0.4278411 0.9185335
## black 0.41588481 1.0000000 1 0.5090883217 1.0000000 1.0000000
## red 0.07688446 0.5142971 1 0.0959509620 0.6512940 1.0000000
## magenta 0.39374214 0.5796714 1 0.7994704711 1.0000000 1.0000000
## tan 1.00000000 1.0000000 1 1.0000000000 1.0000000 1.0000000
## brown 0.33652054 0.8771430 1 0.1800472248 0.9329331 1.0000000
## purple 0.36489664 1.0000000 1 0.4216919289 1.0000000 1.0000000
## salmon 1.00000000 1.0000000 1 1.0000000000 1.0000000 1.0000000
## blue midnightblue darkorange cyan paleturquoise
## pink 0.558556174 1.000000000 1.0000000 0.001009651 0.0941319
## greenyellow 0.303383570 0.174429797 1.0000000 0.543963979 1.0000000
## blue 0.001733351 0.920233375 1.0000000 0.128008747 1.0000000
## green 0.001164893 0.642711245 0.7405175 0.071732522 1.0000000
## yellow 0.004580315 0.719733655 0.7901102 0.255890782 0.6855577
## turquoise 0.113582474 0.406813279 1.0000000 0.427841133 0.8561115
## black 0.801172653 0.689646042 1.0000000 0.698797253 1.0000000
## red 0.181981121 0.005625638 1.0000000 0.379125452 0.6286212
## magenta 0.293113622 0.022096755 1.0000000 0.672716665 1.0000000
## tan 0.289023518 0.344682769 1.0000000 0.008464912 1.0000000
## brown 0.032072102 0.014861309 0.8003416 0.497617348 0.6969900
## purple 0.236331898 0.627647650 1.0000000 0.017355802 0.3609166
## salmon 0.647626174 1.000000000 1.0000000 1.000000000 1.0000000
## darkolivegreen red darkgrey darkturquoise violet
## pink 1.0000000 0.8372047 0.5530629 1.0000000 0.4061227
## greenyellow 1.0000000 0.7014139 1.0000000 0.4150576 1.0000000
## blue 1.0000000 0.9589752 0.7882699 1.0000000 0.5615052
## green 1.0000000 0.9692837 1.0000000 0.4525123 1.0000000
## yellow 0.6479582 0.9822366 1.0000000 0.5290441 0.6855577
## turquoise 0.2464761 0.9627447 0.9500322 0.9500322 0.1226121
## black 0.3802828 1.0000000 1.0000000 1.0000000 1.0000000
## red 1.0000000 1.0000000 1.0000000 1.0000000 0.6286212
## magenta 0.3594187 1.0000000 1.0000000 1.0000000 1.0000000
## tan 1.0000000 1.0000000 0.2562303 1.0000000 1.0000000
## brown 0.6595288 1.0000000 0.8420287 1.0000000 1.0000000
## purple 1.0000000 1.0000000 0.4994033 1.0000000 1.0000000
## salmon 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## plum1 magenta yellowgreen purple skyblue3 royalblue
## pink 1.0000000 1.0000000 0.35202555 1.0000000 0.3329392 1.0000000
## greenyellow 1.0000000 0.6687376 0.03316919 0.6215743 0.2363079 1.0000000
## blue 0.4016486 0.9976537 0.79468149 0.8964967 0.4288283 0.9762457
## green 1.0000000 0.9585672 1.00000000 0.5210016 0.1791352 1.0000000
## yellow 0.5728473 1.0000000 1.00000000 0.8312943 0.5929967 1.0000000
## turquoise 0.4110693 1.0000000 0.80098035 0.9036974 1.0000000 1.0000000
## black 1.0000000 0.4974138 0.07090508 1.0000000 1.0000000 1.0000000
## red 0.5172510 0.9573304 0.56168250 0.9376024 1.0000000 0.8574974
## magenta 0.3043290 0.7922990 0.33700548 1.0000000 0.3185223 1.0000000
## tan 1.0000000 0.4565986 1.00000000 1.0000000 1.0000000 1.0000000
## brown 1.0000000 1.0000000 0.62999105 0.9647080 0.6045414 1.0000000
## purple 0.2804822 0.7596536 1.00000000 0.7146135 1.0000000 1.0000000
## salmon 1.0000000 1.0000000 1.00000000 0.4000153 1.0000000 1.0000000
## lightcyan lightyellow lightgreen orangered4 grey60
## pink 1.0000000 1.0000000 0.6591564 1.00000000 1.0000000
## greenyellow 1.0000000 1.0000000 0.5116310 1.00000000 1.0000000
## blue 0.9130610 0.9768731 0.9011669 1.00000000 0.9827759
## green 0.8834780 1.0000000 1.0000000 0.48597956 1.0000000
## yellow 0.4437126 1.0000000 1.0000000 1.00000000 1.0000000
## turquoise 0.1135607 1.0000000 0.9070573 0.13315666 1.0000000
## black 0.6802217 1.0000000 1.0000000 1.00000000 1.0000000
## red 0.6237032 0.8594708 0.8707550 0.02719222 1.0000000
## magenta 1.0000000 1.0000000 1.0000000 1.00000000 0.6515525
## tan 1.0000000 1.0000000 1.0000000 0.11970532 1.0000000
## brown 0.7226075 0.9060903 0.7019486 0.54831635 1.0000000
## purple 0.6181231 0.5880946 1.0000000 1.00000000 1.0000000
## salmon 1.0000000 1.0000000 0.3139258 1.00000000 1.0000000
## lightsteelblue1 skyblue darkmagenta sienna3 mediumpurple3
## pink 0.23446742 0.46364539 1.0000000 0.06878518 1
## greenyellow 1.00000000 1.00000000 1.0000000 1.00000000 1
## blue 1.00000000 0.38675442 1.0000000 0.79468149 1
## green 1.00000000 0.11413954 0.5710570 1.00000000 1
## yellow 1.00000000 0.15597545 1.0000000 0.61840274 1
## turquoise 1.00000000 1.00000000 1.0000000 0.80098035 1
## black 1.00000000 0.13011623 0.3616905 0.07090508 1
## red 1.00000000 0.69400784 1.0000000 1.00000000 1
## magenta 1.00000000 1.00000000 1.0000000 1.00000000 1
## tan 1.00000000 0.02130863 0.1496975 1.00000000 1
## brown 1.00000000 0.76007598 0.6360953 1.00000000 1
## purple 0.20512267 0.41447951 1.0000000 1.00000000 1
## salmon 0.08928843 1.00000000 1.0000000 1.00000000 1
## floralwhite
## pink 1.000000e+00
## greenyellow 1.000000e+00
## blue 3.501014e-02
## green 3.213908e-01
## yellow 7.224877e-02
## turquoise 7.377017e-05
## black 1.000000e+00
## red 1.000000e+00
## magenta 1.000000e+00
## tan 1.000000e+00
## brown 1.000000e+00
## purple 1.593902e-01
## salmon 1.000000e+00
#2.2. Fisher’s Exact Test
#Alternatively, you can use Fisher’s exact test to compare the contingency table of overlapping and non-overlapping genes between modules:
# Perform Fisher's exact test for each CAMK2A-MEGA module pair
pvalMatrixFisher <- matrix(NA, nrow = length(camk2aModules), ncol = length(megaModules))
rownames(pvalMatrixFisher) <- camk2aModules
colnames(pvalMatrixFisher) <- megaModules
for (camk2aModule in camk2aModules) {
camk2aGenes <- camk2aSymbols[which(netCAMK2A$colors == camk2aModule)]
numCamk2aGenes <- length(camk2aGenes)
for (megaModule in megaModules) {
megaGenes <- MEGAsymbols[which(netMEGA$colors == megaModule)]
numMegaGenes <- length(megaGenes)
overlap <- intersect(camk2aGenes, megaGenes)
numOverlap <- length(overlap)
# Create a contingency table
contingencyTable <- matrix(c(numOverlap, numCamk2aGenes - numOverlap,
numMegaGenes - numOverlap,
totalGenes - (numCamk2aGenes + numMegaGenes - numOverlap)),
nrow = 2)
# Fisher's exact test
fisherResult <- fisher.test(contingencyTable, alternative = "greater")
pvalMatrixFisher[camk2aModule, megaModule] <- fisherResult$p.value
}
}
# Print p-value matrix from Fisher's test
print(pvalMatrixFisher)
## yellow tan greenyellow orange turquoise
## pink 0.6238139690 0.3300336 0.4178549 0.03836807 5.755144e-01
## greenyellow 1.0000000000 1.0000000 0.6178317 1.00000000 7.578316e-01
## blue 0.1352080087 0.6200609 0.8926047 0.50929486 5.358546e-04
## green 0.0463243339 0.8988086 0.5142032 0.05443058 2.351269e-01
## yellow 0.3154680127 0.7387716 0.3851620 0.08306062 1.487673e-02
## turquoise 0.3637003570 0.4383047 0.4265218 0.02002751 2.339155e-05
## black 0.0188426230 1.0000000 1.0000000 0.17589386 1.102920e-01
## red 0.2041586347 0.1840244 0.9358243 0.16809075 1.362418e-01
## magenta 0.0468616497 0.6772456 1.0000000 0.15729852 8.218141e-02
## tan 1.0000000000 1.0000000 1.0000000 1.00000000 7.839499e-01
## brown 0.0009190771 0.7543711 0.9634922 0.82239477 3.870414e-02
## purple 0.2669977146 1.0000000 0.7109662 0.47692364 2.596132e-01
## salmon 0.1653875025 0.3415915 1.0000000 1.00000000 7.675744e-01
## green black darkgreen saddlebrown ivory brown
## pink 0.26486485 0.8193571 0.001505317 0.1146342 0.01936605 0.9472364
## greenyellow 0.11545912 1.0000000 0.018498763 1.0000000 1.00000000 1.0000000
## blue 0.05908699 0.8623733 0.086030668 0.1584804 0.54618942 0.9799500
## green 0.05491744 0.2184302 0.078780434 0.3058112 1.00000000 0.8033498
## yellow 0.76166333 0.8905824 0.562269303 0.7281007 0.38169535 0.9887054
## turquoise 0.03238808 0.5893865 0.824250095 0.8872204 0.55319119 0.9986475
## black 0.54747716 0.5137101 0.054742679 1.0000000 0.19776367 0.7953914
## red 0.05273287 0.3944990 0.216433251 1.0000000 0.33742298 0.9962753
## magenta 1.00000000 1.0000000 1.000000000 1.0000000 0.18543682 0.9383667
## tan 0.48827580 0.4668749 1.000000000 1.0000000 1.00000000 0.0851671
## brown 0.77998077 0.5367798 0.304585826 0.3839383 1.00000000 0.9905792
## purple 0.45771533 0.1747175 1.000000000 0.3958750 1.00000000 0.7123845
## salmon 0.47166527 1.0000000 1.000000000 0.1856175 1.00000000 1.0000000
## steelblue darkred lightcyan1 pink salmon white
## pink 1.00000000 1.0000000 1 1.0000000000 0.6924775 1.0000000
## greenyellow 0.04738233 1.0000000 1 0.3054007241 0.5439640 1.0000000
## blue 0.56903819 0.9645232 1 0.0009992517 0.9268487 1.0000000
## green 0.63713294 1.0000000 1 0.2134766911 0.8959115 1.0000000
## yellow 0.69059320 1.0000000 1 0.7179700973 0.7325502 1.0000000
## turquoise 0.57959984 1.0000000 1 0.5809242941 0.4278411 0.9185335
## black 0.41588481 1.0000000 1 0.5090883217 1.0000000 1.0000000
## red 0.07688446 0.5142971 1 0.0959509620 0.6512940 1.0000000
## magenta 0.39374214 0.5796714 1 0.7994704711 1.0000000 1.0000000
## tan 1.00000000 1.0000000 1 1.0000000000 1.0000000 1.0000000
## brown 0.33652054 0.8771430 1 0.1800472248 0.9329331 1.0000000
## purple 0.36489664 1.0000000 1 0.4216919289 1.0000000 1.0000000
## salmon 1.00000000 1.0000000 1 1.0000000000 1.0000000 1.0000000
## blue midnightblue darkorange cyan paleturquoise
## pink 0.558556174 1.000000000 1.0000000 0.001009651 0.0941319
## greenyellow 0.303383570 0.174429797 1.0000000 0.543963979 1.0000000
## blue 0.001733351 0.920233375 1.0000000 0.128008747 1.0000000
## green 0.001164893 0.642711245 0.7405175 0.071732522 1.0000000
## yellow 0.004580315 0.719733655 0.7901102 0.255890782 0.6855577
## turquoise 0.113582474 0.406813279 1.0000000 0.427841133 0.8561115
## black 0.801172653 0.689646042 1.0000000 0.698797253 1.0000000
## red 0.181981121 0.005625638 1.0000000 0.379125452 0.6286212
## magenta 0.293113622 0.022096755 1.0000000 0.672716665 1.0000000
## tan 0.289023518 0.344682769 1.0000000 0.008464912 1.0000000
## brown 0.032072102 0.014861309 0.8003416 0.497617348 0.6969900
## purple 0.236331898 0.627647650 1.0000000 0.017355802 0.3609166
## salmon 0.647626174 1.000000000 1.0000000 1.000000000 1.0000000
## darkolivegreen red darkgrey darkturquoise violet
## pink 1.0000000 0.8372047 0.5530629 1.0000000 0.4061227
## greenyellow 1.0000000 0.7014139 1.0000000 0.4150576 1.0000000
## blue 1.0000000 0.9589752 0.7882699 1.0000000 0.5615052
## green 1.0000000 0.9692837 1.0000000 0.4525123 1.0000000
## yellow 0.6479582 0.9822366 1.0000000 0.5290441 0.6855577
## turquoise 0.2464761 0.9627447 0.9500322 0.9500322 0.1226121
## black 0.3802828 1.0000000 1.0000000 1.0000000 1.0000000
## red 1.0000000 1.0000000 1.0000000 1.0000000 0.6286212
## magenta 0.3594187 1.0000000 1.0000000 1.0000000 1.0000000
## tan 1.0000000 1.0000000 0.2562303 1.0000000 1.0000000
## brown 0.6595288 1.0000000 0.8420287 1.0000000 1.0000000
## purple 1.0000000 1.0000000 0.4994033 1.0000000 1.0000000
## salmon 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## plum1 magenta yellowgreen purple skyblue3 royalblue
## pink 1.0000000 1.0000000 0.35202555 1.0000000 0.3329392 1.0000000
## greenyellow 1.0000000 0.6687376 0.03316919 0.6215743 0.2363079 1.0000000
## blue 0.4016486 0.9976537 0.79468149 0.8964967 0.4288283 0.9762457
## green 1.0000000 0.9585672 1.00000000 0.5210016 0.1791352 1.0000000
## yellow 0.5728473 1.0000000 1.00000000 0.8312943 0.5929967 1.0000000
## turquoise 0.4110693 1.0000000 0.80098035 0.9036974 1.0000000 1.0000000
## black 1.0000000 0.4974138 0.07090508 1.0000000 1.0000000 1.0000000
## red 0.5172510 0.9573304 0.56168250 0.9376024 1.0000000 0.8574974
## magenta 0.3043290 0.7922990 0.33700548 1.0000000 0.3185223 1.0000000
## tan 1.0000000 0.4565986 1.00000000 1.0000000 1.0000000 1.0000000
## brown 1.0000000 1.0000000 0.62999105 0.9647080 0.6045414 1.0000000
## purple 0.2804822 0.7596536 1.00000000 0.7146135 1.0000000 1.0000000
## salmon 1.0000000 1.0000000 1.00000000 0.4000153 1.0000000 1.0000000
## lightcyan lightyellow lightgreen orangered4 grey60
## pink 1.0000000 1.0000000 0.6591564 1.00000000 1.0000000
## greenyellow 1.0000000 1.0000000 0.5116310 1.00000000 1.0000000
## blue 0.9130610 0.9768731 0.9011669 1.00000000 0.9827759
## green 0.8834780 1.0000000 1.0000000 0.48597956 1.0000000
## yellow 0.4437126 1.0000000 1.0000000 1.00000000 1.0000000
## turquoise 0.1135607 1.0000000 0.9070573 0.13315666 1.0000000
## black 0.6802217 1.0000000 1.0000000 1.00000000 1.0000000
## red 0.6237032 0.8594708 0.8707550 0.02719222 1.0000000
## magenta 1.0000000 1.0000000 1.0000000 1.00000000 0.6515525
## tan 1.0000000 1.0000000 1.0000000 0.11970532 1.0000000
## brown 0.7226075 0.9060903 0.7019486 0.54831635 1.0000000
## purple 0.6181231 0.5880946 1.0000000 1.00000000 1.0000000
## salmon 1.0000000 1.0000000 0.3139258 1.00000000 1.0000000
## lightsteelblue1 skyblue darkmagenta sienna3 mediumpurple3
## pink 0.23446742 0.46364539 1.0000000 0.06878518 1
## greenyellow 1.00000000 1.00000000 1.0000000 1.00000000 1
## blue 1.00000000 0.38675442 1.0000000 0.79468149 1
## green 1.00000000 0.11413954 0.5710570 1.00000000 1
## yellow 1.00000000 0.15597545 1.0000000 0.61840274 1
## turquoise 1.00000000 1.00000000 1.0000000 0.80098035 1
## black 1.00000000 0.13011623 0.3616905 0.07090508 1
## red 1.00000000 0.69400784 1.0000000 1.00000000 1
## magenta 1.00000000 1.00000000 1.0000000 1.00000000 1
## tan 1.00000000 0.02130863 0.1496975 1.00000000 1
## brown 1.00000000 0.76007598 0.6360953 1.00000000 1
## purple 0.20512267 0.41447951 1.0000000 1.00000000 1
## salmon 0.08928843 1.00000000 1.0000000 1.00000000 1
## floralwhite
## pink 1.000000e+00
## greenyellow 1.000000e+00
## blue 3.501014e-02
## green 3.213908e-01
## yellow 7.224877e-02
## turquoise 7.377017e-05
## black 1.000000e+00
## red 1.000000e+00
## magenta 1.000000e+00
## tan 1.000000e+00
## brown 1.000000e+00
## purple 1.593902e-01
## salmon 1.000000e+00
############################
#3. Adjusting for Multiple Comparisons
#Because you are performing multiple tests (one for each CAMK2A-MEGA module pair), it’s important to adjust the p-values to control for false discovery rate (FDR) using methods like Benjamini-Hochberg correction:
# Adjust p-values for multiple comparisons using the Benjamini-Hochberg method
adjustedPvalMatrix <- p.adjust(pvalMatrix, method = "BH")
# Or for Fisher's exact test:
adjustedPvalMatrixFisher <- p.adjust(pvalMatrixFisher, method = "BH")
# Print adjusted p-values
print(adjustedPvalMatrix)
## [1] 1.00000000 1.00000000 1.00000000 0.87428045 1.00000000 1.00000000
## [7] 0.60187351 1.00000000 0.87428045 1.00000000 0.09518842 1.00000000
## [13] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [19] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [25] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [31] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [37] 1.00000000 1.00000000 1.00000000 0.79067025 1.00000000 1.00000000
## [43] 0.89750794 1.00000000 0.60187351 1.00000000 1.00000000 1.00000000
## [49] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [55] 0.09518842 1.00000000 0.60187351 0.01337997 1.00000000 1.00000000
## [61] 1.00000000 1.00000000 0.79067025 1.00000000 1.00000000 1.00000000
## [67] 1.00000000 0.93882659 0.89750794 1.00000000 0.75891096 1.00000000
## [73] 0.89750794 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [79] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [85] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [91] 1.00000000 0.10763020 0.60187351 1.00000000 1.00000000 1.00000000
## [97] 1.00000000 0.89750794 1.00000000 1.00000000 1.00000000 1.00000000
## [103] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [109] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [115] 1.00000000 1.00000000 1.00000000 0.60187351 1.00000000 1.00000000
## [121] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [127] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [133] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [139] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [145] 0.87428045 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [151] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [157] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [163] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [169] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [175] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [181] 1.00000000 1.00000000 1.00000000 1.00000000 0.09518842 1.00000000
## [187] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [193] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [199] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [205] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [211] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [217] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [223] 1.00000000 0.11016406 0.09518842 0.26199399 1.00000000 1.00000000
## [229] 1.00000000 1.00000000 1.00000000 0.75891096 1.00000000 1.00000000
## [235] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [241] 1.00000000 0.29253316 0.60187351 1.00000000 0.60187351 1.00000000
## [247] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [253] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [259] 1.00000000 1.00000000 0.09518842 1.00000000 1.00000000 1.00000000
## [265] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 0.40349416
## [271] 1.00000000 0.60187351 1.00000000 1.00000000 1.00000000 1.00000000
## [277] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [283] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [289] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [295] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [301] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [307] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [313] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [319] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [325] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [331] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [337] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [343] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [349] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [355] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [361] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [367] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [373] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [379] 0.75891096 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [385] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [391] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [397] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [403] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [409] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [415] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [421] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [427] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [433] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [439] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [445] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [451] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [457] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [463] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [469] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [475] 1.00000000 0.70699779 1.00000000 1.00000000 1.00000000 1.00000000
## [481] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [487] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [493] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [499] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [505] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [511] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [517] 0.60187351 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [523] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [529] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [535] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [541] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [547] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [553] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [559] 1.00000000 1.00000000 1.00000000 0.77022318 1.00000000 1.00000000
## [565] 0.02109827 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [571] 1.00000000 1.00000000
print(adjustedPvalMatrixFisher)
## [1] 1.00000000 1.00000000 1.00000000 0.87428045 1.00000000 1.00000000
## [7] 0.60187351 1.00000000 0.87428045 1.00000000 0.09518842 1.00000000
## [13] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [19] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [25] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [31] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [37] 1.00000000 1.00000000 1.00000000 0.79067025 1.00000000 1.00000000
## [43] 0.89750794 1.00000000 0.60187351 1.00000000 1.00000000 1.00000000
## [49] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [55] 0.09518842 1.00000000 0.60187351 0.01337997 1.00000000 1.00000000
## [61] 1.00000000 1.00000000 0.79067025 1.00000000 1.00000000 1.00000000
## [67] 1.00000000 0.93882659 0.89750794 1.00000000 0.75891096 1.00000000
## [73] 0.89750794 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [79] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [85] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [91] 1.00000000 0.10763020 0.60187351 1.00000000 1.00000000 1.00000000
## [97] 1.00000000 0.89750794 1.00000000 1.00000000 1.00000000 1.00000000
## [103] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [109] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [115] 1.00000000 1.00000000 1.00000000 0.60187351 1.00000000 1.00000000
## [121] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [127] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [133] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [139] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [145] 0.87428045 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [151] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [157] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [163] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [169] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [175] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [181] 1.00000000 1.00000000 1.00000000 1.00000000 0.09518842 1.00000000
## [187] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [193] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [199] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [205] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [211] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [217] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [223] 1.00000000 0.11016406 0.09518842 0.26199399 1.00000000 1.00000000
## [229] 1.00000000 1.00000000 1.00000000 0.75891096 1.00000000 1.00000000
## [235] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [241] 1.00000000 0.29253316 0.60187351 1.00000000 0.60187351 1.00000000
## [247] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [253] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [259] 1.00000000 1.00000000 0.09518842 1.00000000 1.00000000 1.00000000
## [265] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 0.40349416
## [271] 1.00000000 0.60187351 1.00000000 1.00000000 1.00000000 1.00000000
## [277] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [283] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [289] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [295] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [301] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [307] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [313] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [319] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [325] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [331] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [337] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [343] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [349] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [355] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [361] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [367] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [373] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [379] 0.75891096 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [385] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [391] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [397] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [403] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [409] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [415] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [421] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [427] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [433] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [439] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [445] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [451] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [457] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [463] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [469] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [475] 1.00000000 0.70699779 1.00000000 1.00000000 1.00000000 1.00000000
## [481] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [487] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [493] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [499] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [505] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [511] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [517] 0.60187351 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [523] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [529] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [535] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [541] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [547] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [553] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [559] 1.00000000 1.00000000 1.00000000 0.77022318 1.00000000 1.00000000
## [565] 0.02109827 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## [571] 1.00000000 1.00000000
#4. Identify Significant Overlaps
#You can now identify which module pairs have a statistically significant overlap by applying a threshold (e.g., p-value < 0.05):
# Identify significant overlaps (e.g., p-value < 0.05)
significantOverlaps <- which(adjustedPvalMatrix < 0.05, arr.ind = TRUE)
print(significantOverlaps)
## [1] 58 565
# For Fisher's exact test:
significantOverlapsFisher <- which(adjustedPvalMatrixFisher < 0.05, arr.ind = TRUE)
print(significantOverlapsFisher)
## [1] 58 565
# Example datasets: camk2aSymbols and megaSymbols are lists of gene symbols for each module
# Initialize a matrix to store the overlap counts between CAMK2A and MEGA modules
camk2aModules <- unique(netCAMK2A$colors) # List of CAMK2A module colors
megaModules <- unique(netMEGA$colors) # List of MEGA module colors
# Initialize matrices to store overlap counts and p-values
overlapMatrix <- matrix(0, nrow = length(camk2aModules), ncol = length(megaModules))
rownames(overlapMatrix) <- camk2aModules
colnames(overlapMatrix) <- megaModules
pvalMatrix <- matrix(NA, nrow = length(camk2aModules), ncol = length(megaModules))
rownames(pvalMatrix) <- camk2aModules
colnames(pvalMatrix) <- megaModules
# Perform overlap calculation and Fisher's exact test
totalGenes <- length(unique(c(rownames(cleanDat), rownames(cleanDatMEGA)))) # Total number of unique genes
for (camkMod in camk2aModules) {
camkGenes <- rownames(cleanDat)[netCAMK2A$colors == camkMod] # Genes in CAMK2A module
for (megaMod in megaModules) {
megaGenes <- rownames(cleanDatMEGA)[netMEGA$colors == megaMod] # Genes in MEGA module
# Calculate overlap
overlapGenes <- intersect(camkGenes, megaGenes)
overlapCount <- length(overlapGenes)
overlapMatrix[camkMod, megaMod] <- overlapCount # Store overlap count
# Create contingency table for Fisher's Exact Test
a <- overlapCount # Number of overlapping genes
b <- length(camkGenes) - a # Genes in CAMK2A module but not in MEGA
c <- length(megaGenes) - a # Genes in MEGA module but not in CAMK2A
d <- totalGenes - (a + b + c) # Genes not in either module
contingencyTable <- matrix(c(a, b, c, d), nrow = 2)
# Fisher's Exact Test to check significance of the overlap
testResult <- fisher.test(contingencyTable, alternative = "greater")
# Store p-value
pvalMatrix[camkMod, megaMod] <- testResult$p.value
}
}
# View the overlap matrix and p-value matrix
print("Overlap Matrix (Counts of Overlapping Genes):")
## [1] "Overlap Matrix (Counts of Overlapping Genes):"
print(overlapMatrix)
## yellow tan greenyellow orange turquoise green grey black darkgreen
## pink 3 2 1 0 13 8 1 0 5
## greenyellow 1 0 0 0 8 2 1 0 6
## blue 19 2 3 2 25 10 20 1 0
## green 6 0 1 0 13 9 11 1 1
## grey 23 5 9 8 47 13 33 10 7
## yellow 3 1 1 5 10 4 9 6 5
## turquoise 12 1 7 15 25 2 7 16 1
## black 3 1 0 3 7 1 3 4 1
## red 7 2 1 0 26 14 8 0 3
## magenta 4 1 1 4 7 2 6 1 0
## tan 0 0 1 1 2 1 2 1 1
## brown 6 3 7 2 7 4 5 3 2
## purple 4 0 1 3 12 3 5 1 2
## salmon 1 0 0 0 2 0 1 1 1
## saddlebrown ivory brown steelblue darkred lightcyan1 pink salmon
## pink 0 0 1 0 1 0 2 0
## greenyellow 0 0 1 0 0 0 0 0
## blue 0 0 2 1 1 0 6 0
## green 1 0 2 0 1 0 5 0
## grey 5 2 15 8 1 0 16 7
## yellow 3 2 4 5 0 0 3 9
## turquoise 0 3 11 5 1 0 17 2
## black 1 0 2 5 0 0 1 1
## red 0 0 0 2 0 0 4 1
## magenta 1 1 0 0 0 0 3 0
## tan 1 1 0 0 0 0 0 1
## brown 7 0 2 0 2 1 3 4
## purple 0 0 1 1 0 0 2 0
## salmon 1 0 0 1 0 0 0 0
## white blue midnightblue darkorange cyan paleturquoise
## pink 0 3 2 0 3 1
## greenyellow 0 1 3 1 3 0
## blue 0 56 8 1 1 0
## green 0 22 4 0 1 2
## grey 1 33 11 0 12 5
## yellow 0 0 3 0 9 1
## turquoise 1 4 9 1 23 0
## black 0 1 2 0 10 1
## red 0 11 2 1 3 2
## magenta 0 3 1 0 0 0
## tan 0 1 0 0 1 0
## brown 0 9 1 0 2 0
## purple 0 5 1 0 0 0
## salmon 0 1 0 0 2 0
## darkolivegreen red darkgrey darkturquoise violet plum1 magenta
## pink 0 1 0 0 0 1 0
## greenyellow 0 0 0 1 0 1 0
## blue 1 6 0 2 2 0 1
## green 1 2 0 0 1 2 1
## grey 3 1 2 4 4 4 2
## yellow 0 1 2 0 1 1 0
## turquoise 3 1 5 2 1 3 0
## black 1 0 0 0 1 1 1
## red 0 2 0 1 0 0 1
## magenta 0 1 0 1 0 0 1
## tan 0 0 0 1 0 1 0
## brown 0 0 0 1 1 0 4
## purple 0 0 0 0 0 0 0
## salmon 0 0 0 0 0 0 0
## yellowgreen purple skyblue3 royalblue lightcyan lightyellow
## pink 1 0 0 0 0 1
## greenyellow 0 1 0 1 2 0
## blue 4 2 1 0 1 0
## green 1 0 0 0 1 0
## grey 6 4 2 3 3 1
## yellow 1 4 1 1 1 0
## turquoise 2 3 5 2 5 1
## black 0 1 0 0 2 0
## red 1 2 0 0 1 0
## magenta 0 0 1 0 1 0
## tan 0 0 0 1 0 0
## brown 1 3 0 0 3 0
## purple 0 1 0 0 0 0
## salmon 1 0 0 0 2 1
## lightgreen orangered4 grey60 lightsteelblue1 skyblue darkmagenta
## pink 0 0 0 0 0 0
## greenyellow 0 0 0 0 1 0
## blue 0 0 1 0 0 4
## green 3 0 0 0 3 0
## grey 0 3 1 1 6 2
## yellow 4 2 1 0 2 1
## turquoise 2 1 0 0 0 0
## black 0 1 0 0 0 0
## red 0 0 0 0 0 0
## magenta 0 3 0 0 1 0
## tan 0 0 0 0 0 0
## brown 3 1 0 0 18 0
## purple 0 0 0 0 0 0
## salmon 1 2 0 0 1 0
## sienna3 mediumpurple3 floralwhite
## pink 0 0 0
## greenyellow 0 0 0
## blue 0 0 0
## green 2 0 2
## grey 1 0 1
## yellow 5 0 0
## turquoise 0 0 0
## black 0 0 0
## red 0 0 0
## magenta 1 0 0
## tan 0 0 0
## brown 1 0 17
## purple 0 0 0
## salmon 0 0 0
print("P-Value Matrix (Fisher's Exact Test Results):")
## [1] "P-Value Matrix (Fisher's Exact Test Results):"
print(pvalMatrix)
## yellow tan greenyellow orange turquoise
## pink 2.935239e-01 0.2889259 0.72922394 1.000000e+00 4.462012e-05
## greenyellow 7.235263e-01 1.0000000 1.00000000 1.000000e+00 2.285833e-03
## blue 5.202014e-05 0.9020261 0.84923810 7.064645e-01 1.346248e-03
## green 1.539242e-01 1.0000000 0.91839714 1.000000e+00 2.066343e-02
## grey 1.813541e-03 0.8060052 0.41979125 6.204639e-02 1.367195e-06
## yellow 7.944547e-01 0.9090016 0.94495437 1.682257e-02 2.808405e-01
## turquoise 4.696051e-02 0.9819488 0.19717159 2.168282e-08 1.765740e-03
## black 3.026700e-01 0.6667930 1.00000000 3.126596e-02 7.376711e-02
## red 6.751066e-02 0.6037842 0.91648728 1.000000e+00 2.218654e-09
## magenta 1.036203e-01 0.6404716 0.70988752 3.751742e-03 5.382505e-02
## tan 1.000000e+00 1.0000000 0.38141838 2.219171e-01 3.901104e-01
## brown 2.630534e-01 0.4405291 0.02771937 4.586216e-01 7.294933e-01
## purple 7.868240e-02 1.0000000 0.67456060 2.014453e-02 4.120353e-05
## salmon 4.913242e-01 1.0000000 1.00000000 1.000000e+00 3.668533e-01
## green grey black darkgreen saddlebrown
## pink 1.876545e-04 1.0000000 1.0000000000 9.725258e-04 1.0000000000
## greenyellow 2.938602e-01 0.9999897 1.0000000000 1.017053e-05 1.0000000000
## blue 6.883319e-02 1.0000000 0.9964500763 1.000000e+00 1.0000000000
## green 3.752884e-03 0.9999918 0.9485817632 7.738289e-01 0.6387495366
## grey 2.182641e-01 1.0000000 0.4879270191 2.071768e-01 0.2355469863
## yellow 4.829662e-01 1.0000000 0.1206185786 2.735218e-02 0.1107328791
## turquoise 9.840581e-01 1.0000000 0.0001515875 9.439071e-01 1.0000000000
## black 8.130457e-01 0.9999945 0.0686125792 5.455333e-01 0.4173637280
## red 1.998035e-06 0.9999999 1.0000000000 1.778605e-01 1.0000000000
## magenta 4.565180e-01 0.9980805 0.7690520672 1.000000e+00 0.3951783531
## tan 4.543877e-01 0.9851683 0.4338243401 2.479334e-01 0.1773060549
## brown 5.074568e-01 1.0000000 0.6793775703 5.253359e-01 0.0001875714
## purple 1.619571e-01 0.9984217 0.7353842305 1.407503e-01 1.0000000000
## salmon 1.000000e+00 0.9976149 0.4182598307 2.376478e-01 0.1696155518
## ivory brown steelblue darkred lightcyan1 pink
## pink 1.00000000 0.9298586 1.000000000 0.5630401 1.0000000 4.478410e-01
## greenyellow 1.00000000 0.8295856 1.000000000 1.0000000 1.0000000 1.000000e+00
## blue 1.00000000 0.9993153 0.825044787 0.9511090 1.0000000 4.647534e-01
## green 1.00000000 0.9617671 1.000000000 0.7956617 1.0000000 1.621675e-01
## grey 0.34823602 0.6647005 0.009494185 0.9949706 1.0000000 2.804776e-02
## yellow 0.06866473 0.8304869 0.003901403 1.0000000 1.0000000 6.529395e-01
## turquoise 0.03421933 0.3567740 0.030360554 0.9539252 1.0000000 3.955094e-05
## black 1.00000000 0.7471479 0.000113349 1.0000000 1.0000000 7.900423e-01
## red 1.00000000 1.0000000 0.226881709 1.0000000 1.0000000 3.203808e-01
## magenta 0.16946464 1.0000000 1.000000000 1.0000000 1.0000000 1.712322e-01
## tan 0.06953818 1.0000000 1.000000000 1.0000000 1.0000000 1.000000e+00
## brown 1.00000000 0.9834502 1.000000000 0.5609250 0.3805781 6.737096e-01
## purple 1.00000000 0.8980450 0.336942231 1.0000000 1.0000000 3.737286e-01
## salmon 1.00000000 1.0000000 0.154166074 1.0000000 1.0000000 1.000000e+00
## salmon white blue midnightblue darkorange
## pink 1.0000000000 1.0000000 4.908225e-01 0.27440409 1.0000000
## greenyellow 1.0000000000 1.0000000 8.334208e-01 0.03058301 0.3454007
## blue 1.0000000000 1.0000000 8.990532e-29 0.03265950 0.9016849
## green 1.0000000000 1.0000000 3.222851e-09 0.13369920 1.0000000
## grey 0.4945159268 0.9797327 9.927915e-05 0.05852170 1.0000000
## yellow 0.0005233923 1.0000000 1.000000e+00 0.39741159 1.0000000
## turquoise 0.9041771270 0.8963889 9.888483e-01 0.01358827 0.9060668
## black 0.6622545845 1.0000000 9.353524e-01 0.28111348 1.0000000
## red 0.8682804321 1.0000000 1.060537e-02 0.58335780 0.7015550
## magenta 1.0000000000 1.0000000 4.549719e-01 0.62663285 1.0000000
## tan 0.3244127535 1.0000000 6.282328e-01 1.00000000 1.0000000
## brown 0.2196381941 1.0000000 1.438321e-01 0.90765757 1.0000000
## purple 1.0000000000 1.0000000 7.309750e-02 0.59086616 1.0000000
## salmon 1.0000000000 1.0000000 6.102750e-01 1.00000000 1.0000000
## cyan paleturquoise darkolivegreen red darkgrey
## pink 8.805020e-02 0.3760095 1.0000000 0.8062466 1.0000000
## greenyellow 3.259499e-02 1.0000000 1.0000000 1.0000000 1.0000000
## blue 9.795276e-01 1.0000000 0.7880629 0.5298633 1.0000000
## green 8.707450e-01 0.2253640 0.5579383 0.8190316 1.0000000
## grey 3.326953e-02 0.1703829 0.4991435 0.9999723 0.9451053
## yellow 5.233923e-04 0.6489031 1.0000000 0.9738129 0.4762111
## turquoise 4.306614e-12 1.0000000 0.2037852 0.9977591 0.1274130
## black 8.559859e-08 0.3811682 0.3514966 1.0000000 1.0000000
## red 3.221520e-01 0.2223334 1.0000000 0.8150155 1.0000000
## magenta 1.000000e+00 1.0000000 1.0000000 0.7887093 1.0000000
## tan 3.244128e-01 1.0000000 1.0000000 1.0000000 1.0000000
## brown 6.971981e-01 1.0000000 1.0000000 1.0000000 1.0000000
## purple 1.000000e+00 1.0000000 1.0000000 1.0000000 1.0000000
## salmon 5.188273e-02 1.0000000 1.0000000 1.0000000 1.0000000
## darkturquoise violet plum1 magenta yellowgreen purple
## pink 1.0000000 1.0000000 0.2930429 1.0000000 0.32479927 1.0000000
## greenyellow 0.3844759 1.0000000 0.2062112 1.0000000 1.00000000 0.5847636
## blue 0.7398683 0.5076978 1.0000000 0.9957880 0.05207892 0.9517781
## green 1.0000000 0.5952887 0.1405893 0.9437416 0.52918759 1.0000000
## grey 0.6702257 0.3390194 0.1697452 0.9992537 0.03491013 0.9663344
## yellow 1.0000000 0.6489031 0.5368187 1.0000000 0.58173466 0.3241482
## turquoise 0.7495156 0.8267533 0.1334115 1.0000000 0.42329802 0.8628594
## black 1.0000000 0.3811682 0.2973452 0.7827624 1.00000000 0.7389508
## red 0.7496292 1.0000000 1.0000000 0.9422269 0.52590209 0.7106728
## magenta 0.4985476 1.0000000 1.0000000 0.7585612 1.00000000 1.0000000
## tan 0.2350306 1.0000000 0.1197058 1.0000000 1.00000000 1.0000000
## brown 0.8115710 0.6604628 1.0000000 0.4359013 0.59323465 0.5732938
## purple 1.0000000 1.0000000 1.0000000 1.0000000 1.00000000 0.6782725
## salmon 1.0000000 1.0000000 1.0000000 1.0000000 0.12847679 1.0000000
## skyblue3 royalblue lightcyan lightyellow lightgreen orangered4
## pink 1.00000000 1.0000000 1.00000000 0.6069865 1.0000000 1.000000000
## greenyellow 1.00000000 0.4607139 0.14332838 1.0000000 1.0000000 1.000000000
## blue 0.73709144 1.0000000 0.97515039 1.0000000 1.0000000 1.000000000
## green 1.00000000 1.0000000 0.85687349 1.0000000 0.2795127 1.000000000
## grey 0.67330856 0.9319063 0.95430224 0.9974461 1.0000000 0.312251609
## yellow 0.55662617 0.8722908 0.89454254 1.0000000 0.1638758 0.151215923
## turquoise 0.01067327 0.8561154 0.30937929 0.9689305 0.8741626 0.688720742
## black 1.00000000 1.0000000 0.27126011 1.0000000 1.0000000 0.273492306
## red 1.00000000 1.0000000 0.85428160 1.0000000 1.0000000 1.000000000
## magenta 0.29328632 1.0000000 0.61711799 1.0000000 1.0000000 0.003142396
## tan 1.00000000 0.2888847 1.00000000 1.0000000 1.0000000 1.000000000
## brown 1.00000000 1.0000000 0.40086340 1.0000000 0.3767010 0.512817463
## purple 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000 1.000000000
## salmon 1.00000000 1.0000000 0.04737891 0.2789085 0.2888871 0.005238662
## grey60 lightsteelblue1 skyblue darkmagenta sienna3
## pink 1.0000000 1.0000000 1.000000e+00 1.00000000 1.000000000
## greenyellow 1.0000000 1.0000000 3.130142e-01 1.00000000 1.000000000
## blue 0.9745415 1.0000000 1.000000e+00 0.05475108 1.000000000
## green 1.0000000 1.0000000 9.143002e-02 1.00000000 0.171067893
## grey 0.9983985 0.7869081 1.388321e-01 0.71834984 0.918768757
## yellow 0.8929769 1.0000000 3.513988e-01 0.58778812 0.001644944
## turquoise 1.0000000 1.0000000 1.000000e+00 1.00000000 1.000000000
## black 1.0000000 1.0000000 1.000000e+00 1.00000000 1.000000000
## red 1.0000000 1.0000000 1.000000e+00 1.00000000 1.000000000
## magenta 1.0000000 1.0000000 4.137530e-01 1.00000000 0.310652817
## tan 1.0000000 1.0000000 1.000000e+00 1.00000000 1.000000000
## brown 1.0000000 1.0000000 2.369272e-16 1.00000000 0.593234649
## purple 1.0000000 1.0000000 1.000000e+00 1.00000000 1.000000000
## salmon 1.0000000 1.0000000 1.791349e-01 1.00000000 1.000000000
## mediumpurple3 floralwhite
## pink 1 1.000000e+00
## greenyellow 1 1.000000e+00
## blue 1 1.000000e+00
## green 1 4.694767e-02
## grey 1 6.894328e-01
## yellow 1 1.000000e+00
## turquoise 1 1.000000e+00
## black 1 1.000000e+00
## red 1 1.000000e+00
## magenta 1 1.000000e+00
## tan 1 1.000000e+00
## brown 1 5.182473e-25
## purple 1 1.000000e+00
## salmon 1 1.000000e+00
# Adjust p-values for multiple testing (optional, to control false discovery rate)
pvalMatrixAdjusted <- p.adjust(pvalMatrix, method = "BH")
print("Adjusted P-Value Matrix (Fisher's Exact Test with BH correction):")
## [1] "Adjusted P-Value Matrix (Fisher's Exact Test with BH correction):"
print(pvalMatrixAdjusted)
## [1] 1.000000e+00 1.000000e+00 2.184846e-03 1.000000e+00 4.231595e-02
## [6] 1.000000e+00 5.527540e-01 1.000000e+00 6.739855e-01 9.194478e-01
## [11] 1.000000e+00 1.000000e+00 7.289693e-01 1.000000e+00 1.000000e+00
## [16] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [21] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [26] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [31] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [36] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 4.109323e-01
## [41] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [46] 1.000000e+00 6.514871e-01 2.789005e-01 1.951454e-06 4.277511e-01
## [51] 1.000000e+00 7.626829e-02 1.000000e+00 1.000000e+00 3.254116e-01
## [56] 1.000000e+00 2.007905e-03 5.143125e-02 3.533901e-02 3.254489e-01
## [61] 9.570363e-05 1.000000e+00 4.231595e-02 6.936310e-01 2.795504e-07
## [66] 5.947100e-01 1.000000e+00 1.000000e+00 1.996787e-03 1.000000e+00
## [71] 5.911118e-03 1.000000e+00 6.739855e-01 7.626829e-02 1.000000e+00
## [76] 1.000000e+00 1.000000e+00 1.000000e+00 1.258762e-04 1.000000e+00
## [81] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [86] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [91] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [96] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [101] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 5.305562e-03
## [106] 6.739855e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [111] 1.000000e+00 1.000000e+00 2.663875e-02 5.824939e-04 1.000000e+00
## [116] 1.000000e+00 1.000000e+00 4.109323e-01 1.000000e+00 1.000000e+00
## [121] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [126] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [131] 1.000000e+00 9.689127e-01 1.000000e+00 1.000000e+00 1.000000e+00
## [136] 1.000000e+00 1.000000e+00 5.911118e-03 1.000000e+00 1.000000e+00
## [141] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [146] 6.739855e-01 4.311635e-01 1.000000e+00 1.000000e+00 1.000000e+00
## [151] 6.739855e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [156] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [161] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [166] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [171] 1.000000e+00 1.000000e+00 1.759217e-01 7.680886e-02 4.277511e-01
## [176] 4.200581e-03 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [181] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [186] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [191] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [196] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [201] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [206] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [211] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 4.109323e-01
## [216] 1.000000e+00 1.996787e-03 1.000000e+00 1.000000e+00 1.000000e+00
## [221] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [226] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.498805e-02
## [231] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [236] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [241] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [246] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [251] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 5.664035e-26
## [256] 3.383994e-07 3.909116e-03 1.000000e+00 1.000000e+00 1.000000e+00
## [261] 1.867823e-01 1.000000e+00 1.000000e+00 1.000000e+00 6.936310e-01
## [266] 1.000000e+00 1.000000e+00 4.277511e-01 4.277511e-01 1.000000e+00
## [271] 6.248927e-01 1.000000e+00 2.313678e-01 1.000000e+00 1.000000e+00
## [276] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [281] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [286] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [291] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 8.039366e-01
## [296] 4.277511e-01 1.000000e+00 1.000000e+00 4.277511e-01 1.498805e-02
## [301] 6.782917e-10 6.740889e-06 1.000000e+00 1.000000e+00 1.000000e+00
## [306] 1.000000e+00 1.000000e+00 5.858878e-01 1.000000e+00 1.000000e+00
## [311] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [316] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [321] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [326] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [331] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [336] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [341] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [346] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [351] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [356] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [361] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [366] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [371] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [376] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [381] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [386] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [391] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [396] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [401] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [406] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [411] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [416] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [421] 1.000000e+00 1.000000e+00 5.858878e-01 1.000000e+00 4.312428e-01
## [426] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [431] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [436] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [441] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [446] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [451] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.867823e-01
## [456] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [461] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [466] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [471] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [476] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [481] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [486] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 5.527540e-01
## [491] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [496] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [501] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [506] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [511] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [516] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [521] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [526] 1.000000e+00 1.000000e+00 6.826585e-02 1.000000e+00 1.000000e+00
## [531] 1.000000e+00 1.000108e-01 1.000000e+00 1.000000e+00 1.000000e+00
## [536] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [541] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [546] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [551] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [556] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [561] 1.000000e+00 1.000000e+00 1.000000e+00 8.228702e-01 1.000000e+00
## [566] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [571] 1.000000e+00 4.975471e-14 1.000000e+00 1.000000e+00 1.000000e+00
## [576] 1.000000e+00 5.947100e-01 1.000000e+00 1.000000e+00 1.000000e+00
## [581] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [586] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [591] 1.000000e+00 1.000000e+00 1.000000e+00 4.145259e-02 1.000000e+00
## [596] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [601] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [606] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [611] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [616] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 5.527540e-01
## [621] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [626] 1.000000e+00 1.000000e+00 1.632479e-22 1.000000e+00 1.000000e+00
# Example: Visualize the overlap matrix with a heatmap
heatmap(overlapMatrix, main = "Gene Overlap Between CAMK2A and MEGA Modules",
xlab = "MEGA Modules", ylab = "CAMK2A Modules", col = heat.colors(100))
# Load necessary library for better heatmap visualization
library(pheatmap)
# Adjust p-values for multiple testing using Benjamini-Hochberg (FDR correction)
pvalMatrixAdjusted <- matrix(p.adjust(pvalMatrix, method = "BH"), nrow = length(camk2aModules), ncol = length(megaModules))
rownames(pvalMatrixAdjusted) <- camk2aModules
colnames(pvalMatrixAdjusted) <- megaModules
# Set a significance threshold (e.g., p < 0.05)
significantThreshold <- 0.05
# Create a binary significance matrix (1 = significant, 0 = not significant)
significantMatrix <- ifelse(pvalMatrixAdjusted < significantThreshold, 1, 0)
# Visualize the significant overlaps with a heatmap
pheatmap(significantMatrix,
main = "Significant Overlaps Between CAMK2A and MEGA Modules (p < 0.05)",
display_numbers = pvalMatrixAdjusted, # Show adjusted p-values on heatmap
color = c("white", "red"), # Red for significant overlaps, white for non-significant
cluster_rows = FALSE, # Don't cluster rows (CAMK2A modules)
cluster_cols = FALSE, # Don't cluster columns (MEGA modules)
fontsize_number = 10, # Font size for displayed p-values
legend_breaks = c(0, 1),
legend_labels = c("Not Significant", "Significant"),
fontsize = 12)
# Load necessary library
library(pheatmap)
# Set the significance threshold (e.g., p < 0.05)
significantThreshold <- 0.05
# Adjust p-values for multiple testing using Benjamini-Hochberg correction
pvalMatrixAdjusted <- matrix(p.adjust(pvalMatrix, method = "BH"), nrow = length(camk2aModules), ncol = length(megaModules))
rownames(pvalMatrixAdjusted) <- camk2aModules
colnames(pvalMatrixAdjusted) <- megaModules
# Replace non-significant p-values with NA for clearer visualization
pvalMatrixSignificant <- pvalMatrixAdjusted
pvalMatrixSignificant[pvalMatrixSignificant >= significantThreshold] <- NA
# Create a color palette that emphasizes significant values
colorPalette <- colorRampPalette(c("red", "red1", "red2"))(158)
# Plot the heatmap with only significant p-values shown
pheatmap(pvalMatrixSignificant,
main = "Significant Overlaps Between CAMK2A and MEGA Modules (p < 0.05)",
display_numbers = ifelse(!is.na(pvalMatrixSignificant), sprintf("%.3f", pvalMatrixSignificant), ""), # Show p-values
color = colorPalette, # Use a gradient from white to blue
cluster_rows = FALSE, # Don't cluster rows (CAMK2A modules)
cluster_cols = FALSE, # Don't cluster columns (MEGA modules)
fontsize_number = 10, # Font size for displayed p-values
fontsize = 14, # General font size for module labels
na_col = "white" # Color for non-significant cells (light gray)
)
