Venn Diagram Overlap of DE Genes

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Last updated: 2025-07-03

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Knit directory: Paul_CX_2025/

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    Modified:   data/Total_number_of_Mapped_reads_by_Individuals.csv

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---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Overlap_DEGs.Rmd) and HTML (docs/Overlap_DEGs.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	52c525b	sayanpaul01	2025-02-20	Added Overlap of GO Terms across drugs, concentrations, and timepoints and updated index
html	6a1580c	sayanpaul01	2025-02-19	Build site.
Rmd	0c8c19f	sayanpaul01	2025-02-19	Added UpSet plots for DEGs across samples, drugs, concentrations, and
html	0ea6c0c	sayanpaul01	2025-02-19	Build site.
Rmd	bef2c81	sayanpaul01	2025-02-19	Added UpSet plots for DEGs across samples, drugs, concentrations, and
html	1dcb0c8	sayanpaul01	2025-02-19	Build site.
Rmd	af51575	sayanpaul01	2025-02-19	Added Overlap of DEGs analysis and updated index.

---

📌 overlap of DE genes

📌 Read and Process DEG Data

# Load DEGs Data
CX_0.1_3 <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")

DOX_0.1_3 <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")

# Extract Significant DEGs
DEG1 <- CX_0.1_3$Entrez_ID[CX_0.1_3$adj.P.Val < 0.05]
DEG2 <- CX_0.1_24$Entrez_ID[CX_0.1_24$adj.P.Val < 0.05]
DEG3 <- CX_0.1_48$Entrez_ID[CX_0.1_48$adj.P.Val < 0.05]
DEG4 <- CX_0.5_3$Entrez_ID[CX_0.5_3$adj.P.Val < 0.05]
DEG5 <- CX_0.5_24$Entrez_ID[CX_0.5_24$adj.P.Val < 0.05]
DEG6 <- CX_0.5_48$Entrez_ID[CX_0.5_48$adj.P.Val < 0.05]

DEG7 <- DOX_0.1_3$Entrez_ID[DOX_0.1_3$adj.P.Val < 0.05]
DEG8 <- DOX_0.1_24$Entrez_ID[DOX_0.1_24$adj.P.Val < 0.05]
DEG9 <- DOX_0.1_48$Entrez_ID[DOX_0.1_48$adj.P.Val < 0.05]
DEG10 <- DOX_0.5_3$Entrez_ID[DOX_0.5_3$adj.P.Val < 0.05]
DEG11 <- DOX_0.5_24$Entrez_ID[DOX_0.5_24$adj.P.Val < 0.05]
DEG12 <- DOX_0.5_48$Entrez_ID[DOX_0.5_48$adj.P.Val < 0.05]

📌 Load Libraries

library(ggplot2)
library(ggVennDiagram)
library(UpSetR)

📌 Across Drugs

📌Venn Diagram: CX-5461 vs VEH (0.1 µM)

venntest <- list(DEG1, DEG2, DEG3)
ggVennDiagram(
  venntest,
  category.names = c("CX_0.1_3", "CX_0.1_24", "CX_0.1_48"),
  fill = c("red", "blue", "green")
) + ggtitle("CX-5461 Vs VEH 0.1 micromolar")+
  theme(
    plot.title = element_text(size = 16, face = "bold"),  # Increase title size
    text = element_text(size = 16)  # Increase text size globally
  )

Past versions of venn1-1.png

Version	Author	Date
1dcb0c8	sayanpaul01	2025-02-19

📌Venn Diagram: CX-5461 vs VEH (0.5 µM)

venntest1 <- list(DEG4, DEG5, DEG6)
ggVennDiagram(
  venntest1,
  category.names = c("CX_0.5_3", "CX_0.5_24", "CX_0.5_48"),
  fill = c("red", "blue", "green")
) + ggtitle("CX-5461 Vs VEH 0.5 micromolar")+
  theme(
    plot.title = element_text(size = 16, face = "bold"),  # Increase title size
    text = element_text(size = 16)  # Increase text size globally
  )

Past versions of venn2-1.png

Version	Author	Date
1dcb0c8	sayanpaul01	2025-02-19

📌Venn Diagram: DOX vs VEH (0.1 µM)

venntest2 <- list(DEG7, DEG8, DEG9)
ggVennDiagram(
  venntest2,
  category.names = c("DOX_0.1_3", "DOX_0.1_24", "DOX_0.1_48"),
  fill = c("red", "blue", "green")
) + ggtitle("DOX Vs VEH 0.1 micromolar")+
  theme(
    plot.title = element_text(size = 16, face = "bold"),  # Increase title size
    text = element_text(size = 16)  # Increase text size globally
  )

Past versions of venn3-1.png

Version	Author	Date
1dcb0c8	sayanpaul01	2025-02-19

📌Venn Diagram: DOX vs VEH (0.5 µM)

venntest3 <- list(DEG10, DEG11, DEG12)
ggVennDiagram(
  venntest3,
  category.names = c("DOX_0.5_3", "DOX_0.5_24", "DOX_0.5_48"),
  fill = c("red", "blue", "green")
) + ggtitle("DOX Vs VEH 0.5 micromolar")+
  theme(
    plot.title = element_text(size = 16, face = "bold"),  # Increase title size
    text = element_text(size = 16)  # Increase text size globally
  )

Past versions of venn4-1.png

Version	Author	Date
1dcb0c8	sayanpaul01	2025-02-19

📌 Across Concentrations

📌Venn Diagram: 0.1 Micromolar

venntest7 <- list(DEG1, DEG2, DEG3, DEG7, DEG8, DEG9)
ggVennDiagram(
  venntest7, label = "count",
  category.names = c("CX_0.1_3", "CX_0.1_24", "CX_0.1_48", "DOX_0.1_3", "DOX_0.1_24", "DOX_0.1_48")
) + ggtitle("0.1 micromolar")+
  theme(
    plot.title = element_text(size = 16, face = "bold"),  # Increase title size
    text = element_text(size = 16)  # Increase text size globally
  )

Past versions of venn5-1.png

Version	Author	Date
1dcb0c8	sayanpaul01	2025-02-19

📌Venn Diagram: 0.5 Micromolar

venntest8 <- list(DEG4, DEG5, DEG6, DEG10, DEG11, DEG12)
ggVennDiagram(
  venntest8, label = "count",
  category.names = c("CX_0.5_3", "CX_0.5_24", "CX_0.5_48", "DOX_0.5_3", "DOX_0.5_24", "DOX_0.5_48")
) + ggtitle("0.5 micromolar")+
  theme(
    plot.title = element_text(size = 16, face = "bold"),  # Increase title size
    text = element_text(size = 16)  # Increase text size globally
  )

Past versions of venn6-1.png

Version	Author	Date
1dcb0c8	sayanpaul01	2025-02-19

📌 Across Timepoints

📌Venn Diagram: 3-hour Timepoint

venntest4 <- list(DEG1, DEG4, DEG7, DEG10)
ggVennDiagram(
  venntest4, label_percent_digit = 2,
  category.names = c("CX_0.1_3", "CX_0.5_3", "DOX_0.1_3", "DOX_0.5_3")
) + ggtitle("3hr")+
  theme(
    plot.title = element_text(size = 16, face = "bold"),  # Increase title size
    text = element_text(size = 16)  # Increase text size globally
  )

Past versions of venn7-1.png

Version	Author	Date
1dcb0c8	sayanpaul01	2025-02-19

📌Venn Diagram: 24-hour Timepoint

venntest5 <- list(DEG2, DEG5, DEG8, DEG11)
ggVennDiagram(
  venntest5, label_percent_digit = 2,
  category.names = c("CX_0.1_24", "CX_0.5_24", "DOX_0.1_24", "DOX_0.5_24")
) + ggtitle("24hr")+
  theme(
    plot.title = element_text(size = 16, face = "bold"),  # Increase title size
    text = element_text(size = 16)  # Increase text size globally
  )

Past versions of venn8-1.png

Version	Author	Date
1dcb0c8	sayanpaul01	2025-02-19

📌Venn Diagram: 48-hour Timepoint

venntest6 <- list(DEG3, DEG6, DEG9, DEG12)
ggVennDiagram(
  venntest6, label_percent_digit = 2,
  category.names = c("CX_0.1_48", "CX_0.5_48", "DOX_0.1_48", "DOX_0.5_48")
) + ggtitle("48hr")+
  theme(
    plot.title = element_text(size = 16, face = "bold"),  # Increase title size
    text = element_text(size = 16)  # Increase text size globally
  )

Past versions of venn9-1.png

Version	Author	Date
1dcb0c8	sayanpaul01	2025-02-19

📌 Overlap of DEGs (Upset Plot)

📌 Loading data

# Extract Significant DEGs
# Create a list of DEGs for each sample
DEG_list <- list(
  CX_0.1_3 = CX_0.1_3$Entrez_ID[CX_0.1_3$adj.P.Val < 0.05],
  CX_0.1_24 = CX_0.1_24$Entrez_ID[CX_0.1_24$adj.P.Val < 0.05],
  CX_0.1_48 = CX_0.1_48$Entrez_ID[CX_0.1_48$adj.P.Val < 0.05],
  CX_0.5_3 = CX_0.5_3$Entrez_ID[CX_0.5_3$adj.P.Val < 0.05],
  CX_0.5_24 = CX_0.5_24$Entrez_ID[CX_0.5_24$adj.P.Val < 0.05],
  CX_0.5_48 = CX_0.5_48$Entrez_ID[CX_0.5_48$adj.P.Val < 0.05],
  DOX_0.1_3 = DOX_0.1_3$Entrez_ID[DOX_0.1_3$adj.P.Val < 0.05],
  DOX_0.1_24 = DOX_0.1_24$Entrez_ID[DOX_0.1_24$adj.P.Val < 0.05],
  DOX_0.1_48 = DOX_0.1_48$Entrez_ID[DOX_0.1_48$adj.P.Val < 0.05],
  DOX_0.5_3 = DOX_0.5_3$Entrez_ID[DOX_0.5_3$adj.P.Val < 0.05],
  DOX_0.5_24 = DOX_0.5_24$Entrez_ID[DOX_0.5_24$adj.P.Val < 0.05],
  DOX_0.5_48 = DOX_0.5_48$Entrez_ID[DOX_0.5_48$adj.P.Val < 0.05]
)

# Convert list to binary matrix
DEG_matrix <- fromList(DEG_list)

# Define order of sets
set_order <- names(DEG_list)

📌 UpSet Plot of DEGs Across Samples(Show all intersections till lowest size 5)

upset(
  DEG_matrix,
  sets = set_order,  # Specify the exact order of sets
  order.by = "freq",  # Order intersections by frequency
  main.bar.color = "blue",  # Color for the intersection bars
  matrix.color = "black",  # Color for matrix dots
  sets.bar.color = rainbow(length(DEG_list)),  # Assign different colors to set size bars
  keep.order = TRUE,  # Keep the specified order of sets
  number.angles = 0,  # Angle of numbers in intersection size bars
  point.size = 2.5,  # Size of points in the matrix
  text.scale = 1,  # Scale for text elements
  show.numbers = "yes"  # Show intersection size numbers directly
)

Past versions of upset2-1.png

Version	Author	Date
0ea6c0c	sayanpaul01	2025-02-19

📌 UpSet Plot of DEGs Across Samples(Show all intersections till last lowest size)

upset(
  DEG_matrix,
  sets = set_order,  # Specify the exact order of sets
  order.by = "freq",  # Order intersections by frequency
  main.bar.color = "blue",  # Color for the intersection bars
  matrix.color = "black",  # Color for matrix dots
  sets.bar.color = rainbow(length(DEG_list)),  # Assign different colors to set size bars
  keep.order = TRUE,  # Keep the specified order of sets
  number.angles = 0,  # Angle of numbers in intersection size bars
  point.size = 2.5,  # Size of points in the matrix
  text.scale = 1,  # Scale for text elements
  show.numbers = "yes",  # Show intersection size numbers directly
  nintersects = NA  # Show all intersections including those with lowest size
)

Past versions of upset3-1.png

Version	Author	Date
6a1580c	sayanpaul01	2025-02-19
0ea6c0c	sayanpaul01	2025-02-19

📌 Drug Specific DE genes

📌 UpSet Plot: CX-5461 vs DOX

# Combine DEGs under CX-5461 and DOX
CX_DEGs <- unique(unlist(DEG_list[1:6]))
DOX_DEGs <- unique(unlist(DEG_list[7:12]))

# Create binary matrix for drug-specific DEGs
DEG_matrix_drug <- fromList(list(CX_5461 = CX_DEGs, DOX = DOX_DEGs))

# Generate the UpSet plot for drugs
upset(
  DEG_matrix_drug,
  sets = c("CX_5461", "DOX"),
  order.by = "freq",
  main.bar.color = "darkgreen",
  point.size = 3,
  text.scale = 1.5,
  matrix.color = "purple",
  sets.bar.color = c("blue", "red")
)

Past versions of upset4-1.png

Version	Author	Date
0ea6c0c	sayanpaul01	2025-02-19

📌 Conc. Specific DE genes

📌 UpSet Plot: Concentration-Specific DEGs

# Combine DEGs under concentrations 0.1 and 0.5
DEG_0.1 <- unique(unlist(DEG_list[c(1, 2, 3, 7, 8, 9)]))
DEG_0.5 <- unique(unlist(DEG_list[c(4, 5, 6, 10, 11, 12)]))

# Create binary matrix for concentration-specific DEGs
DEG_matrix_concentration <- fromList(list(Concentration_0.1 = DEG_0.1, Concentration_0.5 = DEG_0.5))

# Generate the UpSet plot for concentration
upset(
  DEG_matrix_concentration,
  sets = c("Concentration_0.1", "Concentration_0.5"),
  order.by = "freq",
  main.bar.color = "darkorange",
  matrix.color = "darkblue",
  sets.bar.color = c("cyan", "magenta"),
  text.scale = 1.5,
  keep.order = TRUE
)

Past versions of upset5-1.png

Version	Author	Date
0ea6c0c	sayanpaul01	2025-02-19

📌 Timepoints Specific DE genes

📌 UpSet Plot: Timepoint-Specific DEGs

# Combine DEGs under timepoints 3hr, 24hr, and 48hr
DEG_3hr <- unique(unlist(DEG_list[c(1, 4, 7, 10)]))
DEG_24hr <- unique(unlist(DEG_list[c(2, 5, 8, 11)]))
DEG_48hr <- unique(unlist(DEG_list[c(3, 6, 9, 12)]))

# Create binary matrix for timepoint-specific DEGs
DEG_matrix_timepoint <- fromList(list(Timepoint_3hr = DEG_3hr, Timepoint_24hr = DEG_24hr, Timepoint_48hr = DEG_48hr))

# Generate the UpSet plot for timepoints
upset(
  DEG_matrix_timepoint,
  sets = c("Timepoint_3hr", "Timepoint_24hr", "Timepoint_48hr"),
  order.by = "freq",
  main.bar.color = "darkgreen",
  matrix.color = "darkred",
  sets.bar.color = c("blue", "orange", "purple"),
  text.scale = 1.5,
  keep.order = TRUE
)

Past versions of upset6-1.png

Version	Author	Date
0ea6c0c	sayanpaul01	2025-02-19

Session information

sessionInfo()

R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] UpSetR_1.4.0        ggVennDiagram_1.5.2 ggplot2_3.5.2      
[4] workflowr_1.7.1    

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      jsonlite_2.0.0    dplyr_1.1.4       compiler_4.3.0   
 [5] promises_1.3.2    tidyselect_1.2.1  Rcpp_1.0.12       stringr_1.5.1    
 [9] git2r_0.36.2      gridExtra_2.3     callr_3.7.6       later_1.3.2      
[13] jquerylib_0.1.4   scales_1.3.0      yaml_2.3.10       fastmap_1.2.0    
[17] plyr_1.8.9        R6_2.6.1          labeling_0.4.3    generics_0.1.3   
[21] knitr_1.50        tibble_3.2.1      munsell_0.5.1     rprojroot_2.0.4  
[25] bslib_0.9.0       pillar_1.10.2     rlang_1.1.3       cachem_1.1.0     
[29] stringi_1.8.3     httpuv_1.6.15     xfun_0.52         getPass_0.2-4    
[33] fs_1.6.3          sass_0.4.10       cli_3.6.1         withr_3.0.2      
[37] magrittr_2.0.3    ps_1.8.1          digest_0.6.34     grid_4.3.0       
[41] processx_3.8.6    rstudioapi_0.17.1 lifecycle_1.0.4   vctrs_0.6.5      
[45] evaluate_1.0.3    glue_1.7.0        farver_2.1.2      whisker_0.4.1    
[49] colorspace_2.1-0  rmarkdown_2.29    httr_1.4.7        tools_4.3.0      
[53] pkgconfig_2.0.3   htmltools_0.5.8.1