# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv")%>% filter(ChlNDC >= 0.1 | Chl3Q >= 0.1 | Ksg3Q >= 0.1 | KsgNDC >= 0.1)
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv")
View(fh2)
View(fh2)
colnames(fh2)
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv") %>% filter(ChlNDC_T60 >= 0.5 | Chl3Q_T60 >= 0.5 | Ksg3Q_T60 >= 0.5 | KsgNDC_T60 >= 0.5)
colnames(fh2)[6] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/DEseq2output/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_asRNApadjValues.csv")
colnames(fh2)
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_asRNApadjValues.csv")%>% filter(padj_Chl3Q_T60 <= 0.1 | padj_Ksg3Q_T60 <= 0.1 | padj_ChlNDC_T60 <= 0.1 | padj_KsgNDC_T60 <= 0.1)
colnames(fh2)[6] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/DEseq2output/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/DEseq2output/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/DEseq2output/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/DEseq2output/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Ksg_DEseq2/Ksg3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCKsg")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Ksg_DEseq2/Ksg3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCKsg")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
# Convert fold change variables to numeric if needed
merged_df$log2FCChl <- as.numeric(as.character(merged_df$log2FCChl))
merged_df$log2FCKsg <- as.numeric(as.character(merged_df$log2FCKsg))
# Make sure padj values are numeric
merged_df$Ksg3Q<- as.numeric(as.character(merged_df$Ksg3Q))
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_asRNApadjValues.csv")%>% filter(padj_Chl3Q_T60 <= 0.1 | padj_Ksg3Q_T60 <= 0.1 | padj_ChlNDC_T60 <= 0.1 | padj_KsgNDC_T60 <= 0.1)
colnames(fh2)[6] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Ksg_DEseq2/Ksg3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCKsg")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
# Convert fold change variables to numeric if needed
merged_df$log2FCChl <- as.numeric(as.character(merged_df$log2FCChl))
merged_df$log2FCKsg <- as.numeric(as.character(merged_df$log2FCKsg))
# Make sure padj values are numeric
merged_df$Ksg3Q<- as.numeric(as.character(merged_df$Ksg3Q))
View(merged_df)
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_asRNApadjValues.csv")%>% filter(padj_Chl3Q_T60 <= 0.1 | padj_Ksg3Q_T60 <= 0.1 | padj_ChlNDC_T60 <= 0.1 | padj_KsgNDC_T60 <= 0.1)
colnames(fh2)[6] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Ksg_DEseq2/Ksg3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCKsg")
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_asRNApadjValues.csv")%>% filter(padj_Chl3Q_T60 >= 0.1 | padj_Ksg3Q_T60 >= 0.1 | padj_ChlNDC_T60 >= 0.1 | padj_KsgNDC_T60 >= 0.1)
colnames(fh2)[6] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Ksg_DEseq2/Ksg3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCKsg")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
View(merged_df)
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_asRNApadjValues.csv")
#%>% filter(padj_Chl3Q_T60 >= 0.1 | padj_Ksg3Q_T60 >= 0.1 | padj_ChlNDC_T60 >= 0.1 | padj_KsgNDC_T60 >= 0.1)
colnames(fh2)[6] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Ksg_DEseq2/Ksg3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCKsg")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
View(merged_df)
View(fh1)
View(fh2)
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_asRNApadjValues.csv")
View(fh2)
View(fh1)
View(fh2)
#%>% filter(padj_Chl3Q_T60 >= 0.1 | padj_Ksg3Q_T60 >= 0.1 | padj_ChlNDC_T60 >= 0.1 | padj_KsgNDC_T60 >= 0.1)
colnames(fh2)[1] <- "locusTag"
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_asRNApadjValues.csv")%>% filter(padj_Chl3Q_T60 >= 0.1 | padj_Ksg3Q_T60 >= 0.1 | padj_ChlNDC_T60 >= 0.1 | padj_KsgNDC_T60 >= 0.1)
colnames(fh2)[1] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Ksg_DEseq2/Ksg3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCKsg")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
View(merged_df)
# Convert fold change variables to numeric if needed
merged_df$log2FCChl <- as.numeric(as.character(merged_df$log2FCChl))
merged_df$log2FCKsg <- as.numeric(as.character(merged_df$log2FCKsg))
# Make sure padj values are numeric
merged_df$Ksg3Q<- as.numeric(as.character(merged_df$Ksg3Q))
# Make sure padj values are numeric
merged_df$padj_Ksg3Q_T60<- as.numeric(as.character(merged_df$combined_pval_Ksg3Q_T60))
merged_df$padj_Chl3Q_T60 <- as.numeric(as.character(merged_df$padj_Chl3Q_T60))
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Ksg3Q, y = merged_df$log2FCChl, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over CDS)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs padj") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$KsgNDC, y = merged_df$log2FCKsg), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$padj_Ksg3Q_T60, y = merged_df$log2FCChl, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over CDS)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs padj") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$padj_KsgNDC_T60, y = merged_df$log2FCKsg), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv")%>% filter(padj_Chl3Q_T60 >= 0.1 | padj_Ksg3Q_T60 >= 0.1 | padj_ChlNDC_T60 >= 0.1 | padj_KsgNDC_T60 >= 0.1)
colnames(fh2)[1] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Ksg_DEseq2/Ksg3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCKsg")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv")%>% filter(padj_Chl3Q_T60 >= 0.1 | padj_Ksg3Q_T60 >= 0.1 | padj_ChlNDC_T60 >= 0.1 | padj_KsgNDC_T60 >= 0.1)
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv")
View(fh2)
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv")%>% filter(padj_Chl3Q_T60 >= 0.1 | padj_Ksg3Q_T60 >= 0.1 | padj_ChlNDC_T60 >= 0.1 | padj_KsgNDC_T60 >= 0.1)
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv")%>% filter(Chl3Q_T60 >= 0.1 | Ksg3Q_T60 >= 0.1 | ChlNDC_T60 >= 0.1 | KsgNDC_T60 >= 0.1)
colnames(fh2)[1] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Ksg_DEseq2/Ksg3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCKsg")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
# Convert fold change variables to numeric if needed
merged_df$log2FCChl <- as.numeric(as.character(merged_df$log2FCChl))
merged_df$log2FCKsg <- as.numeric(as.character(merged_df$log2FCKsg))
# Make sure padj values are numeric
merged_df$Ksg3Q_T60<- as.numeric(as.character(merged_df$combined_pval_Ksg3Q_T60))
merged_df$Chl3Q_T60 <- as.numeric(as.character(merged_df$padj_Chl3Q_T60))
# Make sure padj values are numeric
merged_df$Ksg3Q_T60<- as.numeric(as.character(merged_df$Ksg3Q_T60))
merged_df$Chl3Q_T60 <- as.numeric(as.character(merged_df$Chl3Q_T60))
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv")%>% filter(Chl3Q_T60 >= 0.1 | Ksg3Q_T60 >= 0.1 | ChlNDC_T60 >= 0.1 | KsgNDC_T60 >= 0.1)
colnames(fh2)[1] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Ksg_DEseq2/Ksg3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCKsg")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
# Convert fold change variables to numeric if needed
merged_df$log2FCChl <- as.numeric(as.character(merged_df$log2FCChl))
merged_df$log2FCKsg <- as.numeric(as.character(merged_df$log2FCKsg))
# Make sure padj values are numeric
merged_df$Ksg3Q_T60<- as.numeric(as.character(merged_df$Ksg3Q_T60))
merged_df$Chl3Q_T60 <- as.numeric(as.character(merged_df$Chl3Q_T60))
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCChl, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over CDS)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs padj") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$padj_KsgNDC_T60, y = merged_df$log2FCKsg), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Noninteractive
print(p2)
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCChl, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over CDS)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs padj") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$KsgNDC_T60, y = merged_df$log2FCKsg), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Noninteractive
print(p2)
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv")%>% filter(Chl3Q_T60 >= 0.1 | Chl3Q_T60 >= 0.1 | ChlNDC_T60 >= 0.1 | ChlNDC_T60 >= 0.1)
colnames(fh2)[1] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCChl")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
# Convert fold change variables to numeric if needed
merged_df$log2FCChl <- as.numeric(as.character(merged_df$log2FCChl))
merged_df$log2FCChl <- as.numeric(as.character(merged_df$log2FCChl))
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
# Convert fold change variables to numeric if needed
merged_df$log2FCChl <- as.numeric(as.character(merged_df$log2FCChl))
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv")%>% filter(Chl3Q_T60 >= 0.1 | Chl3Q_T60 >= 0.1 | ChlNDC_T60 >= 0.1 | ChlNDC_T60 >= 0.1)
colnames(fh2)[1] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCChl")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv")%>% filter(Chl3Q_T60 >= 0.1 | Ksg3Q_T60 >= 0.1 | ChlNDC_T60 >= 0.1 | KsgNDC_T60 >= 0.1)
colnames(fh2)[1] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Ksg_DEseq2/Ksg3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCKsg")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
# Convert fold change variables to numeric if needed
merged_df$log2FCChl <- as.numeric(as.character(merged_df$log2FCChl))
merged_df$log2FCKsg <- as.numeric(as.character(merged_df$log2FCKsg))
# Make sure padj values are numeric
merged_df$Ksg3Q_T60<- as.numeric(as.character(merged_df$Ksg3Q_T60))
merged_df$Chl3Q_T60 <- as.numeric(as.character(merged_df$Chl3Q_T60))
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCChl, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over CDS)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs padj") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$padj_KsgNDC_T60, y = merged_df$log2FCKsg), alpha = 1, size = 4, color = "black")
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Chl3Q_T60, y = merged_df$log2FCChl, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over CDS)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs padj") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$padj_KsgNDC_T60, y = merged_df$log2FCKsg), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Chl3Q_T60, y = merged_df$log2FCChl, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over CDS)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs padj") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$ChlNDC_T60, y = merged_df$log2FCChl), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Chl3Q_T60, y = merged_df$log2FCChl, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#108080") +
scale_x_continuous(name = "padj (Ratio antisense over CDS)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs padj") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$ChlNDC_T60, y = merged_df$log2FCChl), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Noninteractive
print(p2)
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Chl3Q_T60, y = merged_df$log2FCChl, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#108080") +
scale_x_continuous(name = "padj (Ratio antisense over total)") +
ylab("log2 Fold Change (Chl)") +
theme_minimal() +
ggtitle("Chl log2FC vs ratio") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$ChlNDC_T60, y = merged_df$log2FCChl), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Noninteractive
print(p2)
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over total)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs ratio") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over total)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs ratio") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over total)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs ratio") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Clear environment
rm(list = ls())
# Load libraries
lapply(c("ggplot2", "ggforce", "dplyr", "ggnewscale", "plotly"), library, character.only = TRUE)
# Load data files
fh1 <- read.delim("~/Desktop/Stuff/AMeyer_Lab/Experiment_results/3seq/RNAFOLD/RNAfold_DRIVE/Spn/Chl/Nofilter/NC_003028.v3.17.ncrna.genes", sep = " ")
fh2 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/asRNAanalysis/IremT4RNAseq/asRNAanalysis/T43QRNAseq_T60_GeneRatios_s1_over_total.csv")%>% filter(Chl3Q_T60 >= 0.1 | Ksg3Q_T60 >= 0.1 | ChlNDC_T60 >= 0.1 | KsgNDC_T60 >= 0.1)
colnames(fh2)[1] <- "locusTag"
fh3 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Chl_DEseq2/Chl3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh3)[c(1,3)] <- c("locusTag", "log2FCChl")
fh4 <- read.csv("~/Desktop/Manuscript/AnalysisFiles/DEseq2output/T4_Ksg_DEseq2/Ksg3Q_vs_NDC_t60_split_processed_metadata.csv")
colnames(fh4)[c(1,3)] <- c("locusTag", "log2FCKsg")
# Merge datasets by locusTag (keep all fh1 entries)
merged_df <- merge(fh1, fh2, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh3, by = "locusTag", all.x = TRUE)
merged_df <- merge(merged_df, fh4, by = "locusTag", all.x = TRUE)
# Convert fold change variables to numeric if needed
merged_df$log2FCChl <- as.numeric(as.character(merged_df$log2FCChl))
merged_df$log2FCKsg <- as.numeric(as.character(merged_df$log2FCKsg))
# Make sure padj values are numeric
merged_df$Ksg3Q_T60<- as.numeric(as.character(merged_df$Ksg3Q_T60))
merged_df$Chl3Q_T60 <- as.numeric(as.character(merged_df$Chl3Q_T60))
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over total)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs ratio") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Noninteractive
print(p2)
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over total)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs ratio") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
#geom_point(aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over total)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs ratio") #+
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over total)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs ratio") #+
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Basic static plot
p2 <- ggplot(merged_df, aes(x = merged_df$Ksg3Q_T60, y = merged_df$log2FCKsg, text = locusTag)) +
geom_point(alpha = 0.9, size = 4, color = "#800040") +
scale_x_continuous(name = "padj (Ratio antisense over total)") +
ylab("log2 Fold Change (Ksg)") +
theme_minimal() +
ggtitle("Ksg log2FC vs ratio") +
# Overlay a second pair (log2FCKsg vs Chl3Q) in a different color
geom_point(aes(x = merged_df$KsgNDC_T60, y = merged_df$log2FCKsg), alpha = 1, size = 4, color = "black")
# Interactive version
ggplotly(p2, tooltip = c("text", "x", "y"))
# Noninteractive
print(p2)
