/
GO.run_edgeR.R
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GO.run_edgeR.R
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# ################################################################################################################################################################################################################
# GO.run_edgeR.R
#
# CC-BY Lee Edsall
# email: edsall57@gmail.com
# Twitter: @LeeEdsall
# GitHub: https://github.com/ledsall
#
# This script was used to analyze data for Edsall et al. 2019 which compared DNase-seq data from 5 primates (human, chimpanzee, gorilla, orangutan, macaque)
# ################################################################################################################################################################################################################
# ################################################################################################################################################################################################################
# SECTION 1: SETUP
# 1. load libraries
# 2. set global options
# 3. set global variables
# 4. open connection to log file
# ################################################################################################################################################################################################################
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# edgeR Empirical Analysis of Digital Gene Expression Data in R
# Citation: Robinson MD, McCarthy DJ, Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140.
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
library(edgeR)
library(VennDiagram)
library(gridExtra)
options(scipen=0)
pvalue_threshold = .01
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Open the log file; write date and options
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
sink(file="LOG.run_edgeR.R", append=FALSE, type="output", split=TRUE)
cat("-------------------------------------------------------------------------------------------------\n")
cat("BEGIN:", date(), "\n")
cat("-------------------------------------------------------------------------------------------------\n\n")
cat("Using the following settings\n")
cat("\tLog file: LOG.run_edgeR.R\n")
cat("\tp-value threshold:", pvalue_threshold, "\n\n")
# ################################################################################################################################################################################################################
# END SECTION 1: SETUP
# ################################################################################################################################################################################################################
# ################################################################################################################################################################################################################
# SECTION 2: READ THE GLM RESULTS FILE
# ################################################################################################################################################################################################################
cat("Reading GLM results file (glm_output_and_analysis_results_without_pairwise_analysis_results.txt)... \n")
glm_results <- as.data.frame(read.table("glm_output_and_analysis_results_without_pairwise_analysis_results.txt", sep="\t", header=TRUE))
rownames(glm_results) <- paste(glm_results$chrom, glm_results$start, glm_results$end, glm_results$PLoS_overlap, glm_results$flag_change_type, sep=":")
count_all_sites = nrow(glm_results)
cat("done.\n\n")
# ################################################################################################################################################################################################################
# END SECTION 2: READ THE GLM RESULTS FILE
# ################################################################################################################################################################################################################
# ################################################################################################################################################################################################################
# SECTION 3: RUN edgeR AND CREATE RESULTS TABLES
# ################################################################################################################################################################################################################
cat("Setting up to run edgeR ... \n")
# ================================================================================================================================================================================================================
# Set up the read counts matrix
# Extract just the columns with the scores
# Convert from a data.frame to a matrix
# ================================================================================================================================================================================================================
scores <- as.matrix(glm_results[,c(44:58)])
# ================================================================================================================================================================================================================
# Set up the design matrix
# The species order is human, chimpanzee, gorilla, orangutan, macaque
# The factors automatically get re-sorted, so the scores table does not need to be in order
# ================================================================================================================================================================================================================
species <- factor(c(2,2,2,3,3,3,4,4,4,5,5,5,1,1,1))
design <- model.matrix(~species)
# ================================================================================================================================================================================================================
# Prepare to run edgeR
# 1. Calculate the normalization factor
# 2. Estimate the dispersion factors
# ================================================================================================================================================================================================================
y <- DGEList(counts=scores,group=species)
y <- calcNormFactors(y)
y <- estimateDisp(y,design)
# ================================================================================================================================================================================================================
# Run edgeR
# 1. Run the glmQLFit command to generate the model that can be used to run the Quasi-likelihood F test
# 2. Run the human vs. macaque comparison
# 3. Run the chimpanzee vs. macaque comparison
# 4. Run the gorilla vs. macaque comparison
# 5. Run the orangutan vs. macaque comparison
# ================================================================================================================================================================================================================
cat("Running edgeR ... \n")
fit <- glmQLFit(y,design)
qlf.h <- glmQLFTest(fit, coef=2)
qlf.c <- glmQLFTest(fit, coef=3)
qlf.g <- glmQLFTest(fit, coef=4)
qlf.o <- glmQLFTest(fit, coef=5)
cat("done.\n\n")
# ================================================================================================================================================================================================================
# Create a master table of results
# 1. Extract the results from edgeR
# 2. Perform a BH correction
# 3. Create and update an "edgeR_flag_differential" (same idea as the GLM "flag_gateway_test")
# 4. Create and update an "edgeR_flag_change_type" (same idea as the GLM "flag_change_type" flag)
# 5. Add the GLM results table
# ================================================================================================================================================================================================================
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Extract the edgeR results
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
cat("Extracting the edgeR results ... \n")
h <- as.data.frame(qlf.h$table)
c <- as.data.frame(qlf.c$table)
g <- as.data.frame(qlf.g$table)
o <- as.data.frame(qlf.o$table)
colnames(h) <- c("h_log2FC", "h_log2CPM", "h_F", "h_uncorrected_pvalue")
colnames(c) <- c("c_log2FC", "c_log2CPM", "c_F", "c_uncorrected_pvalue")
colnames(g) <- c("g_log2FC", "g_log2CPM", "g_F", "g_uncorrected_pvalue")
colnames(o) <- c("o_log2FC", "o_log2CPM", "o_F", "o_uncorrected_pvalue")
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Add columns with the ln fold change to match the beta value scale
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
cat("Calculating ln fold change values ... \n")
h$h_lnFC <- log(2^h$h_log2FC)
c$c_lnFC <- log(2^c$c_log2FC)
g$g_lnFC <- log(2^g$g_log2FC)
o$o_lnFC <- log(2^o$o_log2FC)
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Perform a BH correction
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
cat("Performing a BH correction ... \n")
uncorrected_pvalues <- as.vector(cbind(h[,4], c[,4], g[,4], o[,4]))
corrected_pvalues <- p.adjust(uncorrected_pvalues, "BH")
h$h_pvalue <- corrected_pvalues[1:89744]
c$c_pvalue <- corrected_pvalues[89745:179488]
g$g_pvalue <- corrected_pvalues[179489:269232]
o$o_pvalue <- corrected_pvalues[269233:358976]
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Combine the edgeR results tables and the GLM results table
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
cat("Creating a master table of results ... \n")
edgeR_results <- as.data.frame(cbind(glm_results, h, c, g, o))
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Add columns with the flags
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
edgeR_results$edgeR_flag_differential <- "ENS"
edgeR_results$edgeR_flag_change_type <- "ssss"
# ================================================================================================================================================================================================================
# Identify the type of change and update the edgeR flags
# ================================================================================================================================================================================================================
cat("Identifying differential sites and determining type of change ... \n")
cat("\tAnalyzing site 1 of", count_all_sites, "\n")
for (lcv in 1:count_all_sites)
{
# Print periodic status messages
if ( (lcv %% 1000) == 0)
{
cat("\tAnalyzing site", lcv, "of", count_all_sites, "\n")
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Identify differential sites
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] < .01 | edgeR_results$c_pvalue[lcv] < .01 | edgeR_results$g_pvalue[lcv] < .01 | edgeR_results$o_pvalue[lcv] < .01)
{
edgeR_results$edgeR_flag_differential[lcv] <- "ED"
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 1 of 15: Change in human
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] < pvalue_threshold & edgeR_results$c_pvalue[lcv] > pvalue_threshold & edgeR_results$g_pvalue[lcv] > pvalue_threshold & edgeR_results$o_pvalue[lcv] > pvalue_threshold)
{
if (edgeR_results$h_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "Gsss"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "Lsss"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 2 of 15: Change in chimpanzee
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] > pvalue_threshold & edgeR_results$c_pvalue[lcv] < pvalue_threshold & edgeR_results$g_pvalue[lcv] > pvalue_threshold & edgeR_results$o_pvalue[lcv] > pvalue_threshold)
{
if (edgeR_results$c_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sGss"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "sLss"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 3 of 15: Change in gorilla
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] > pvalue_threshold & edgeR_results$c_pvalue[lcv] > pvalue_threshold & edgeR_results$g_pvalue[lcv] < pvalue_threshold & edgeR_results$o_pvalue[lcv] > pvalue_threshold)
{
if (edgeR_results$g_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "ssGs"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "ssLs"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 4 of 15: Change in orangutan
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] > pvalue_threshold & edgeR_results$c_pvalue[lcv] > pvalue_threshold & edgeR_results$g_pvalue[lcv] > pvalue_threshold & edgeR_results$o_pvalue[lcv] < pvalue_threshold)
{
if (edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sssG"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "sssL"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 5 of 15: Changes in HCGO
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] < pvalue_threshold & edgeR_results$c_pvalue[lcv] < pvalue_threshold & edgeR_results$g_pvalue[lcv] < pvalue_threshold & edgeR_results$o_pvalue[lcv] < pvalue_threshold)
{
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GGGG"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GGGL"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GGLG"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GLGG"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LGGG"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GGLL"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GLGL"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GLLG"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LGGL"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LGLG"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LLGG"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GLLL"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LGLL"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LLGL"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LLLG"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "LLLL"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 6 of 15: Changes in human/chimpanzee
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] < pvalue_threshold & edgeR_results$c_pvalue[lcv] < pvalue_threshold & edgeR_results$g_pvalue[lcv] > pvalue_threshold & edgeR_results$o_pvalue[lcv] > pvalue_threshold)
{
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GGss"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GLss"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LGss"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "LLss"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 7 of 15: Changes in human/gorilla
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] < pvalue_threshold & edgeR_results$c_pvalue[lcv] > pvalue_threshold & edgeR_results$g_pvalue[lcv] < pvalue_threshold & edgeR_results$o_pvalue[lcv] > pvalue_threshold)
{
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GsGs"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GsLs"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LsGs"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "LsLs"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 8 of 15: Changes in human/orangutan
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] < pvalue_threshold & edgeR_results$c_pvalue[lcv] > pvalue_threshold & edgeR_results$g_pvalue[lcv] > pvalue_threshold & edgeR_results$o_pvalue[lcv] < pvalue_threshold)
{
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GssG"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GssL"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LssG"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "LssL"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 9 of 15: Changes in chimpanzee/gorilla
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] > pvalue_threshold & edgeR_results$c_pvalue[lcv] < pvalue_threshold & edgeR_results$g_pvalue[lcv] < pvalue_threshold & edgeR_results$o_pvalue[lcv] > pvalue_threshold)
{
if (edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sGGs"
next
}
if (edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sGLs"
next
}
if (edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sLGs"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "sLLs"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 10 of 15: Changes in chimpanzee/orangutan
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] > pvalue_threshold & edgeR_results$c_pvalue[lcv] < pvalue_threshold & edgeR_results$g_pvalue[lcv] > pvalue_threshold & edgeR_results$o_pvalue[lcv] < pvalue_threshold)
{
if (edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sGsG"
next
}
if (edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sGsL"
next
}
if (edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sLsG"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "sLsL"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 11 of 15: Changes in gorilla/orangutan
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] > pvalue_threshold & edgeR_results$c_pvalue[lcv] > pvalue_threshold & edgeR_results$g_pvalue[lcv] < pvalue_threshold & edgeR_results$o_pvalue[lcv] < pvalue_threshold)
{
if (edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "ssGG"
next
}
if (edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "ssGL"
next
}
if (edgeR_results$g_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "ssLG"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "ssLL"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 12 of 15: Changes in human/chimpanzee/gorilla
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] < pvalue_threshold & edgeR_results$c_pvalue[lcv] < pvalue_threshold & edgeR_results$g_pvalue[lcv] < pvalue_threshold & edgeR_results$o_pvalue[lcv] > pvalue_threshold)
{
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GGGs"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GGLs"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LGGs"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GLLs"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LGLs"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LLGs"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "LLLs"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 13 of 15: Changes in human/chimpanzee/orangutan
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] < pvalue_threshold & edgeR_results$c_pvalue[lcv] < pvalue_threshold & edgeR_results$g_pvalue[lcv] > pvalue_threshold & edgeR_results$o_pvalue[lcv] < pvalue_threshold)
{
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GGsG"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GGsL"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LGsG"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GLsL"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LGsL"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LLsG"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "LLsL"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 14 of 15: Changes in human/gorilla/orangutan
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] < pvalue_threshold & edgeR_results$c_pvalue[lcv] > pvalue_threshold & edgeR_results$g_pvalue[lcv] < pvalue_threshold & edgeR_results$o_pvalue[lcv] < pvalue_threshold)
{
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GsGG"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GsGL"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LsGG"
next
}
if (edgeR_results$h_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "GsLL"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LsGL"
next
}
if (edgeR_results$h_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "LsLG"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "LsLL"
next
}
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Test 15 of 15: Changes in chimpanzee/gorilla/orangutan
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (edgeR_results$h_pvalue[lcv] > pvalue_threshold & edgeR_results$c_pvalue[lcv] < pvalue_threshold & edgeR_results$g_pvalue[lcv] < pvalue_threshold & edgeR_results$o_pvalue[lcv] < pvalue_threshold)
{
if (edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sGGG"
next
}
if (edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sGGL"
next
}
if (edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sLGG"
next
}
if (edgeR_results$c_lnFC[lcv] > 0 & edgeR_results$g_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sGLL"
next
}
if (edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] > 0 & edgeR_results$o_lnFC[lcv] < 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sLGL"
next
}
if (edgeR_results$c_lnFC[lcv] < 0 & edgeR_results$g_lnFC[lcv] < 0 & edgeR_results$o_lnFC[lcv] > 0)
{
edgeR_results$edgeR_flag_change_type[lcv] <- "sLLG"
next
}
edgeR_results$edgeR_flag_change_type[lcv] <- "sLLL"
next
}
} # END: for (lcv in 1:count_all_sites)
cat("done.\n\n")
# ################################################################################################################################################################################################################
# END SECTION 3: RUN edgeR AND CREATE RESULTS TABLE
# ################################################################################################################################################################################################################
# ################################################################################################################################################################################################################
# SECTION 4: WRITE RESULTS TO FILE
# ################################################################################################################################################################################################################
cat("Writing results to files ... \n")
write.table(edgeR_results, "edgeR_analysis.all_results.all_information.txt", sep='\t', quote=FALSE, row.names=TRUE, col.names=TRUE)
# ################################################################################################################################################################################################################
# END SECTION 4: WRITE RESULTS TO FILE
# ################################################################################################################################################################################################################
# ################################################################################################################################################################################################################
# SECTION 5: CREATE SUBSETS
# For each species, create 6 subsets
# 1. Increase called by both methods
# 2. Increase called by edgeR only
# 3. Increase called by the GLM only
# 4. Decrease called by both methods
# 5. Decrease called by edgeR only
# 6. Decrease called by the GLM only
# Note: The subsets include every category where the species changed; not just the species-specific one
# ################################################################################################################################################################################################################
# ================================================================================================================================================================================================================
# Non differential sites
# ================================================================================================================================================================================================================
non_differential.both <- subset(edgeR_results, edgeR_flag_differential == "ENS" & flag_gateway_test == "GNS")
non_differential.edgeR <- subset(edgeR_results, edgeR_flag_differential == "ENS" & flag_gateway_test == "GD")
non_differential.glm <- subset(edgeR_results, edgeR_flag_differential == "ED" & flag_gateway_test == "GNS")
# ================================================================================================================================================================================================================
# Differential sites
# ================================================================================================================================================================================================================
differential.both <- subset(edgeR_results, edgeR_flag_differential == "ED" & flag_gateway_test == "GD")
differential.edgeR <- subset(edgeR_results, edgeR_flag_differential == "ED" & flag_gateway_test == "GNS")
differential.glm <- subset(edgeR_results, edgeR_flag_differential == "ENS" & flag_gateway_test == "GD")
# ================================================================================================================================================================================================================
# Human increases
# ================================================================================================================================================================================================================
h_inc.both <- subset(edgeR_results, h_pvalue < .01 & h_lnFC > 0 & flag_change_type %in% list("Gsss","GGss","GsGs","GssG","GGGs","GGsG","GsGG","GGGG"))
h_inc.edgeR <- subset(edgeR_results, h_pvalue < .01 & h_lnFC > 0 & !flag_change_type %in% list("Gsss","GGss","GsGs","GssG","GGGs","GGsG","GsGG","GGGG"))
h_inc.glm <- subset(edgeR_results, h_pvalue > .01 & h_lnFC > 0 & flag_change_type %in% list("Gsss","GGss","GsGs","GssG","GGGs","GGsG","GsGG","GGGG"))
# ================================================================================================================================================================================================================
# Human decreases
# ================================================================================================================================================================================================================
h_dec.both <- subset(edgeR_results, h_pvalue < .01 & h_lnFC < 0 & flag_change_type %in% list("Lsss","LLss","LsLs","LssL","LLLs","LLsL","LsLL","LLLL"))
h_dec.edgeR <- subset(edgeR_results, h_pvalue < .01 & h_lnFC < 0 & !flag_change_type %in% list("Lsss","LLss","LsLs","LssL","LLLs","LLsL","LsLL","LLLL"))
h_dec.glm <- subset(edgeR_results, h_pvalue > .01 & h_lnFC < 0 & flag_change_type %in% list("Lsss","LLss","LsLs","LssL","LLLs","LLsL","LsLL","LLLL"))
# ================================================================================================================================================================================================================
# Chimpanzee increases
# ================================================================================================================================================================================================================
c_inc.both <- subset(edgeR_results, c_pvalue < .01 & c_lnFC > 0 & flag_change_type %in% list("sGss","GGss","sGGs","sGsG","GGGs","GGsG","sGGG","GGGG"))
c_inc.edgeR <- subset(edgeR_results, c_pvalue < .01 & c_lnFC > 0 & !flag_change_type %in% list("sGss","GGss","sGGs","sGsG","GGGs","GGsG","sGGG","GGGG"))
c_inc.glm <- subset(edgeR_results, c_pvalue > .01 & c_lnFC > 0 & flag_change_type %in% list("sGss","GGss","sGGs","sGsG","GGGs","GGsG","sGGG","GGGG"))
# ================================================================================================================================================================================================================
# Chimpanzee decreases
# ================================================================================================================================================================================================================
c_dec.both <- subset(edgeR_results, c_pvalue < .01 & c_lnFC < 0 & flag_change_type %in% list("sLss","LLss","sLLs","sLsL","LLLs","LLsL","sLLL","LLLL"))
c_dec.edgeR <- subset(edgeR_results, c_pvalue < .01 & c_lnFC < 0 & !flag_change_type %in% list("sLss","LLss","sLLs","sLsL","LLLs","LLsL","sLLL","LLLL"))
c_dec.glm <- subset(edgeR_results, c_pvalue > .01 & c_lnFC < 0 & flag_change_type %in% list("sLss","LLss","sLLs","sLsL","LLLs","LLsL","sLLL","LLLL"))
# ================================================================================================================================================================================================================
# Gorilla increases
# ================================================================================================================================================================================================================
g_inc.both <- subset(edgeR_results, g_pvalue < .01 & g_lnFC > 0 & flag_change_type %in% list("ssGs","GsGs","sGGs","ssGG","GGGs","GsGG","sGGG","GGGG"))
g_inc.edgeR <- subset(edgeR_results, g_pvalue < .01 & g_lnFC > 0 & !flag_change_type %in% list("ssGs","GsGs","sGGs","ssGG","GGGs","GsGG","sGGG","GGGG"))
g_inc.glm <- subset(edgeR_results, g_pvalue > .01 & g_lnFC > 0 & flag_change_type %in% list("ssGs","GsGs","sGGs","ssGG","GGGs","GsGG","sGGG","GGGG"))
# ================================================================================================================================================================================================================
# Gorilla decreases
# ================================================================================================================================================================================================================
g_dec.both <- subset(edgeR_results, g_pvalue < .01 & g_lnFC < 0 & flag_change_type %in% list("ssLs","LsLs","sLLs","ssLL","LLLs","LsLL","sLLL","LLLL"))
g_dec.edgeR <- subset(edgeR_results, g_pvalue < .01 & g_lnFC < 0 & !flag_change_type %in% list("ssLs","LsLs","sLLs","ssLL","LLLs","LsLL","sLLL","LLLL"))
g_dec.glm <- subset(edgeR_results, g_pvalue > .01 & g_lnFC < 0 & flag_change_type %in% list("ssLs","LsLs","sLLs","ssLL","LLLs","LsLL","sLLL","LLLL"))
# ================================================================================================================================================================================================================
# Orangutan increases
# ================================================================================================================================================================================================================
o_inc.both <- subset(edgeR_results, o_pvalue < .01 & o_lnFC > 0 & flag_change_type %in% list("sssG","GssG","sGsG","ssGG","GGsG","GsGG","sGGG","GGGG"))
o_inc.edgeR <- subset(edgeR_results, o_pvalue < .01 & o_lnFC > 0 & !flag_change_type %in% list("sssG","GssG","sGsG","ssGG","GGsG","GsGG","sGGG","GGGG"))
o_inc.glm <- subset(edgeR_results, o_pvalue > .01 & o_lnFC > 0 & flag_change_type %in% list("sssG","GssG","sGsG","ssGG","GGsG","GsGG","sGGG","GGGG"))
# ================================================================================================================================================================================================================
# Orangutan decreases
# ================================================================================================================================================================================================================
o_dec.both <- subset(edgeR_results, o_pvalue < .01 & o_lnFC < 0 & flag_change_type %in% list("sssL","LssL","sLsL","ssLL","LLsL","LsLL","sLLL","LLLL"))
o_dec.edgeR <- subset(edgeR_results, o_pvalue < .01 & o_lnFC < 0 & !flag_change_type %in% list("sssL","LssL","sLsL","ssLL","LLsL","LsLL","sLLL","LLLL"))
o_dec.glm <- subset(edgeR_results, o_pvalue > .01 & o_lnFC < 0 & flag_change_type %in% list("sssL","LssL","sLsL","ssLL","LLsL","LsLL","sLLL","LLLL"))
# ================================================================================================================================================================================================================
# Calculate counts
# ================================================================================================================================================================================================================
cat("Calculating counts ... \n")
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Non differential sites
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
count.non_differential.either <- nrow(non_differential.both) + nrow(non_differential.edgeR) + nrow(non_differential.glm)
count.non_differential.both <- nrow(non_differential.both)
count.non_differential.edgeR_only <- nrow(non_differential.edgeR)
count.non_differential.glm_only <- nrow(non_differential.glm)
count.non_differential.edgeR_total <- nrow(non_differential.both) + nrow(non_differential.edgeR)
count.non_differential.glm_total <- nrow(non_differential.both) + nrow(non_differential.glm)
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Differential sites
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
count.differential.either <- nrow(differential.both) + nrow(differential.edgeR) + nrow(differential.glm)
count.differential.both <- nrow(differential.both)
count.differential.edgeR_only <- nrow(differential.edgeR)
count.differential.glm_only <- nrow(differential.glm)
count.differential.edgeR_total <- nrow(differential.both) + nrow(differential.edgeR)
count.differential.glm_total <- nrow(differential.both) + nrow(differential.glm)
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Human increases
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
count.h_inc.either <- nrow(h_inc.both) + nrow(h_inc.edgeR) + nrow(h_inc.glm)
count.h_inc.both <- nrow(h_inc.both)
count.h_inc.edgeR_only <- nrow(h_inc.edgeR)
count.h_inc.glm_only <- nrow(h_inc.glm)
count.h_inc.edgeR_total <- nrow(h_inc.both) + nrow(h_inc.edgeR)
count.h_inc.glm_total <- nrow(h_inc.both) + nrow(h_inc.glm)
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Human decreases
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
count.h_dec.either <- nrow(h_dec.both) + nrow(h_dec.edgeR) + nrow(h_dec.glm)
count.h_dec.both <- nrow(h_dec.both)
count.h_dec.edgeR_only <- nrow(h_dec.edgeR)
count.h_dec.glm_only <- nrow(h_dec.glm)
count.h_dec.edgeR_total <- nrow(h_dec.both) + nrow(h_dec.edgeR)
count.h_dec.glm_total <- nrow(h_dec.both) + nrow(h_dec.glm)
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Chimpanzee increases
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
count.c_inc.either <- nrow(c_inc.both) + nrow(c_inc.edgeR) + nrow(c_inc.glm)
count.c_inc.both <- nrow(c_inc.both)
count.c_inc.edgeR_only <- nrow(c_inc.edgeR)
count.c_inc.glm_only <- nrow(c_inc.glm)
count.c_inc.edgeR_total <- nrow(c_inc.both) + nrow(c_inc.edgeR)
count.c_inc.glm_total <- nrow(c_inc.both) + nrow(c_inc.glm)
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Chimpanzee decreases
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
count.c_dec.either <- nrow(c_dec.both) + nrow(c_dec.edgeR) + nrow(c_dec.glm)
count.c_dec.both <- nrow(c_dec.both)
count.c_dec.edgeR_only <- nrow(c_dec.edgeR)
count.c_dec.glm_only <- nrow(c_dec.glm)
count.c_dec.edgeR_total <- nrow(c_dec.both) + nrow(c_dec.edgeR)
count.c_dec.glm_total <- nrow(c_dec.both) + nrow(c_dec.glm)
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Gorilla increases
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
count.g_inc.either <- nrow(g_inc.both) + nrow(g_inc.edgeR) + nrow(g_inc.glm)
count.g_inc.both <- nrow(g_inc.both)
count.g_inc.edgeR_only <- nrow(g_inc.edgeR)
count.g_inc.glm_only <- nrow(g_inc.glm)
count.g_inc.edgeR_total <- nrow(g_inc.both) + nrow(g_inc.edgeR)
count.g_inc.glm_total <- nrow(g_inc.both) + nrow(g_inc.glm)
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Gorilla decreases
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
count.g_dec.either <- nrow(g_dec.both) + nrow(g_dec.edgeR) + nrow(g_dec.glm)
count.g_dec.both <- nrow(g_dec.both)
count.g_dec.edgeR_only <- nrow(g_dec.edgeR)
count.g_dec.glm_only <- nrow(g_dec.glm)
count.g_dec.edgeR_total <- nrow(g_dec.both) + nrow(g_dec.edgeR)
count.g_dec.glm_total <- nrow(g_dec.both) + nrow(g_dec.glm)
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Orangutan increases
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
count.o_inc.either <- nrow(o_inc.both) + nrow(o_inc.edgeR) + nrow(o_inc.glm)
count.o_inc.both <- nrow(o_inc.both)
count.o_inc.edgeR_only <- nrow(o_inc.edgeR)
count.o_inc.glm_only <- nrow(o_inc.glm)
count.o_inc.edgeR_total <- nrow(o_inc.both) + nrow(o_inc.edgeR)
count.o_inc.glm_total <- nrow(o_inc.both) + nrow(o_inc.glm)
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Orangutan decreases
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
count.o_dec.either <- nrow(o_dec.both) + nrow(o_dec.edgeR) + nrow(o_dec.glm)
count.o_dec.both <- nrow(o_dec.both)
count.o_dec.edgeR_only <- nrow(o_dec.edgeR)
count.o_dec.glm_only <- nrow(o_dec.glm)
count.o_dec.edgeR_total <- nrow(o_dec.both) + nrow(o_dec.edgeR)
count.o_dec.glm_total <- nrow(o_dec.both) + nrow(o_dec.glm)
# ################################################################################################################################################################################################################
# END SECTION 5: CREATE SUBSETS
# ################################################################################################################################################################################################################
# ################################################################################################################################################################################################################
# SECTION 6: CREATE PLOTS
# 1. Scatter plots for all sites (GLM beta value vs edgeR ln fold change value)
# 2 rows and 2 columns
# row 1: human, chimpanzee
# row 2: gorilla, orangutan
# x-axis is the GLM beta value for the species
# y-axis is the edgeR ln fold change value for the species
# 2. Density plots for increases (GLM beta values)
# 4 rows: human, chimpanzee, gorilla, orangutan
# 2 columns: GLM beta values for that species; GLM beta values for macaque
# 3 lines: both methods; edgeR only; glm only
# 3. Density plots for decreases (GLM beta values)
# 4 rows: human, chimpanzee, gorilla, orangutan
# 2 columns: GLM beta values for that species; GLM beta values for macaque
# 3 lines: both methods; edgeR only; glm only
# 4. Density plots for edgeR fold change and GLM beta value
# 4 rows: human, chimpanzee, gorilla, orangutan
# 2 columns: increases, decreases
# 2 lines: edgeR only; glm only
# 5. Scatter plots for increases (GLM beta value vs edgeR ln fold change value)
# 4 rows: human, chimpanzee, gorilla, orangutan
# 3 columns: both methods; edgeR only; GLM only;
# x-axis is the GLM beta value for the species
# y-axis is the edgeR ln fold change value for the species
# 6. Scatter plots for decreases (GLM beta value vs edgeR ln fold change value)
# 4 rows: human, chimpanzee, gorilla, orangutan
# 3 columns: both methods; edgeR only; GLM only;
# x-axis is the GLM beta value for the species
# y-axis is the edgeR ln fold change value for the species
# 7. Scatter plots for increases (GLM beta value for species vs macaque)
# 4 rows: human, chimpanzee, gorilla, orangutan
# 3 columns: both methods; edgeR only; GLM only;
# x-axis is the GLM beta value for the species
# y-axis is the GLM beta value for macaque
# 8. Scatter plots for decreases (GLM beta value for species vs macaque)
# 4 rows: human, chimpanzee, gorilla, orangutan
# 3 columns: both methods; edgeR only; GLM only;
# x-axis is the GLM beta value for the species
# y-axis is the GLM beta value for macaque
# 9. Venn diagrams of the counts of calls for glm only, edgeR only, both
# 5 rows x 2 columns
# row 1: non differential sites; differential sites
# row 2: human increases; human decreases
# row 3: chimpanzee increases; chimpanzee decreases
# row 4: gorilla increases; gorilla decreases
# row 5: orangutan increases; orangutan decreases
# Create 2 versions of the plot: one with count labels and one without
# ################################################################################################################################################################################################################
cat("Generating plots ... \n")
# ================================================================================================================================================================================================================
# Plot #1: Scatter plots for all sites (GLM beta value vs edgeR ln fold change value)
# 2 rows and 2 columns
# row 1: human, chimpanzee
# row 2: gorilla, orangutan
# x-axis is the GLM beta value for the species
# y-axis is the edgeR ln fold change value for the species
# ================================================================================================================================================================================================================
png("scatter_plots.GLM_beta_values_vs_edgeR_ln_fold_change_values.all_sites.png", height=1900, width=1600, res=300)
par(mfrow=c(2,2))
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Human
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
plot(edgeR_results$Bh, edgeR_results$h_lnFC, main="All DHS sites\n Human values",
xlab="GLM", ylab="edgeR", xlim=c(-6,6), ylim=c(-6,6), cex.main=1)
abline(0,1,col="red")
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Chimpanzee
# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
plot(edgeR_results$Bc, edgeR_results$c_lnFC, main="All DHS sites\n Chimpanzee values",
xlab="GLM", ylab="edgeR", xlim=c(-6,6), ylim=c(-6,6), cex.main=1)