Log2CPM Boxplots of Most Significant DE Genes

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Last updated: 2025-02-07

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---

📌 Log2CPM Boxplots of Most Significant DE Genes

This analysis generates boxplots for the log2CPM values of the most significant DE genes across CX-5461 and DOX treatments.

---

📌 Load Required Libraries

library(dplyr)

Warning: package 'dplyr' was built under R version 4.3.2

library(ggplot2)

Warning: package 'ggplot2' was built under R version 4.3.3

library(ggsignif)

Warning: package 'ggsignif' was built under R version 4.3.1

library(tidyr)

Warning: package 'tidyr' was built under R version 4.3.3

📌 CX_0.1_3 vs VEH_0.1_3

📌 Define File Paths

# Define file paths
input_file <- "data/Feature_count_Matrix_Log2CPM_filtered.csv"
toptable_file <- "data/DEGs/Toptable_CX_0.1_3.csv"
output_boxplot <- "data/Boxplot_CX_0.1_3.csv"
output_log2CPM <- "data/log2CPM_filtered.csv"

📌 Read and Clean Dataset

# Read Log2CPM Matrix
boxplot1 <- read.csv(input_file) %>%
  as.data.frame()
colnames(boxplot1) <- trimws(gsub("^X", "", colnames(boxplot1)))  # Clean column names

# Select relevant columns
matching_columns <- grep("ENTREZID|SYMBOL|CX.5461_0.1_3|VEH_0.1_3", colnames(boxplot1), value = TRUE)

if (!"SYMBOL" %in% colnames(boxplot1)) stop("ERROR: 'SYMBOL' column is missing in boxplot1! Check dataset.")

new_boxplot1 <- boxplot1[, matching_columns]

# Save cleaned datasets
write.csv(boxplot1, output_log2CPM, row.names = FALSE)
write.csv(new_boxplot1, output_boxplot, row.names = FALSE)

# Display first few rows
head(new_boxplot1)

📌 Define Color Palettes

drug_palc <- c("CX.5461" = "#8B006D", "VEH" = "#F1B72B")

📌 Read and Process Toptable Data

toptable <- read.csv(toptable_file)
colnames(toptable)[1] <- "Entrezid"

filtered_toptable <- toptable %>%
  filter(adj.P.Val < 0.05) %>%
  arrange(adj.P.Val) %>%
  slice_head(n = 10)

# Display first few rows of the filtered Toptable
head(filtered_toptable)

   Entrezid    logFC  AveExpr        t  P.Value adj.P.Val        B
1 100996485 2.032914 2.577945 10.55571 1.06e-17  1.52e-13 4.406806

📌 Filter Boxplot Data for Selected Genes

filtered_boxplot <- new_boxplot1 %>%
  filter(ENTREZID %in% filtered_toptable$Entrezid)

if (!"SYMBOL" %in% colnames(filtered_boxplot)) stop("ERROR: 'SYMBOL' column is missing in filtered_boxplot!")

# Pivot data to long format
long_boxplot <- filtered_boxplot %>%
  pivot_longer(cols = -c(ENTREZID, SYMBOL), names_to = "Sample", values_to = "log2CPM") %>%
  as.data.frame()

if (!"SYMBOL" %in% colnames(long_boxplot)) stop("ERROR: 'SYMBOL' column is missing after pivoting!")

long_boxplot$SYMBOL <- as.character(long_boxplot$SYMBOL)

📌 Define Individual Mapping

indv_mapping <- c("75.1" = 1, "78.1" = 2, "87.1" = 3, "17.3" = 4, "84.1" = 5, "90.1" = 6)

# Debug: Print column names before mutate
print("Columns in long_boxplot before mutate():")

[1] "Columns in long_boxplot before mutate():"

print(colnames(long_boxplot))

[1] "ENTREZID" "SYMBOL"   "Sample"   "log2CPM" 

📌Process Data with Metadata

formatted_data <- long_boxplot %>%
  mutate(
    Indv_id = sub("_.*", "", Sample),  # Extract individual ID
    Indv = ifelse(Indv_id %in% names(indv_mapping), as.character(indv_mapping[Indv_id]), NA),
    Drug = ifelse(grepl("CX.5461", Sample), "CX.5461", "VEH"),
    Conc = "0.1",
    Timepoint = "3"
  ) %>%
  dplyr::select(any_of(c("ENTREZID", "SYMBOL", "Sample", "Indv", "Drug", "Conc", "Timepoint", "log2CPM"))) %>%
  as.data.frame()

# Rename SYMBOL → Gene
colnames(formatted_data) <- trimws(colnames(formatted_data))
if ("SYMBOL" %in% colnames(formatted_data)) {
  colnames(formatted_data)[colnames(formatted_data) == "SYMBOL"] <- "Gene"
} else {
  stop("ERROR: 'SYMBOL' column is missing before renaming.")
}

if (!"Gene" %in% colnames(formatted_data)) stop("ERROR: 'Gene' column is missing after renaming.")

formatted_data$Indv <- as.character(formatted_data$Indv)

# Display first few rows
head(formatted_data)

   ENTREZID      Gene             Sample Indv    Drug Conc Timepoint  log2CPM
1 100996485 PITX1-AS1 17.3_CX.5461_0.1_3    4 CX.5461  0.1         3 4.166038
2 100996485 PITX1-AS1 84.1_CX.5461_0.1_3    5 CX.5461  0.1         3 3.859530
3 100996485 PITX1-AS1     84.1_VEH_0.1_3    5     VEH  0.1         3 1.704291
4 100996485 PITX1-AS1 90.1_CX.5461_0.1_3    6 CX.5461  0.1         3 3.566918
5 100996485 PITX1-AS1     90.1_VEH_0.1_3    6     VEH  0.1         3 1.431025
6 100996485 PITX1-AS1     17.3_VEH_0.1_3    4     VEH  0.1         3 2.229019

📌Generate Boxplot

ggplot(formatted_data, aes(x = Drug, y = log2CPM, fill = Drug)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = drug_palc) +
  facet_wrap(~ Gene, ncol = 5) +
  geom_point(aes(color = Indv), size = 2, alpha = 0.5, position = position_jitter(width = -1, height = 0)) +
  ggtitle("log2CPM CX.5461_0.1 vs Vehicle_0.1 (3hrs)") +
  labs(x = "Drugs", y = "log2CPM") +
  theme_bw() +
  theme(
    plot.title = element_text(size = rel(2), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.ticks = element_line(linewidth = 1.5),
    axis.line = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black"),
    strip.text.y = element_text(color = "white")
  )

📌 CX_0.1_24 vs VEH_0.1_24

📌 Define File Paths

# Define file paths
input_file <- "data/Feature_count_Matrix_Log2CPM_filtered.csv"
toptable_file <- "data/DEGs/Toptable_CX_0.1_24.csv"
output_boxplot <- "data/Boxplot_CX_0.1_24.csv"
output_log2CPM <- "data/log2CPM_filtered.csv"

📌 Read and Clean Dataset

# Read Log2CPM Matrix
boxplot1 <- read.csv(input_file) %>%
  as.data.frame()
colnames(boxplot1) <- trimws(gsub("^X", "", colnames(boxplot1)))  # Clean column names

# Select relevant columns
matching_columns <- grep("ENTREZID|SYMBOL|CX.5461_0.1_24|VEH_0.1_24", colnames(boxplot1), value = TRUE)

if (!"SYMBOL" %in% colnames(boxplot1)) stop("ERROR: 'SYMBOL' column is missing in boxplot1! Check dataset.")

new_boxplot1 <- boxplot1[, matching_columns]

# Save cleaned datasets
write.csv(boxplot1, output_log2CPM, row.names = FALSE)
write.csv(new_boxplot1, output_boxplot, row.names = FALSE)

# Display first few rows
head(new_boxplot1)

📌 Define Color Palettes

drug_palc <- c("CX.5461" = "#8B006D", "VEH" = "#F1B72B")

📌 Read and Process Toptable Data

toptable <- read.csv(toptable_file)
colnames(toptable)[1] <- "Entrezid"

filtered_toptable <- toptable %>%
  filter(adj.P.Val < 0.05) %>%
  arrange(adj.P.Val) %>%
  slice_head(n = 10)

# Display first few rows of the filtered Toptable
head(filtered_toptable)

   Entrezid     logFC  AveExpr          t  P.Value adj.P.Val        B
1      5347 -2.133241 3.460559 -10.514869 1.30e-17  1.86e-13 29.37176
2     10112 -2.657049 3.471443 -10.205466 5.94e-17  4.24e-13 27.86551
3      1062 -1.973517 2.975691  -9.795318 4.47e-16  2.11e-12 25.91921
4 100996485  1.869814 2.577945   9.738606 5.91e-16  2.11e-12 25.55081
5      9787 -2.084540 3.045935  -9.363571 3.75e-15  1.07e-11 23.88908
6     55635 -1.887472 3.616797  -9.263926 6.12e-15  1.37e-11 23.50584

📌 Filter Boxplot Data for Selected Genes

filtered_boxplot <- new_boxplot1 %>%
  filter(ENTREZID %in% filtered_toptable$Entrezid)

if (!"SYMBOL" %in% colnames(filtered_boxplot)) stop("ERROR: 'SYMBOL' column is missing in filtered_boxplot!")

# Pivot data to long format
long_boxplot <- filtered_boxplot %>%
  pivot_longer(cols = -c(ENTREZID, SYMBOL), names_to = "Sample", values_to = "log2CPM") %>%
  as.data.frame()

if (!"SYMBOL" %in% colnames(long_boxplot)) stop("ERROR: 'SYMBOL' column is missing after pivoting!")

long_boxplot$SYMBOL <- as.character(long_boxplot$SYMBOL)

📌 Define Individual Mapping

indv_mapping <- c("75.1" = 1, "78.1" = 2, "87.1" = 3, "17.3" = 4, "84.1" = 5, "90.1" = 6)

# Debug: Print column names before mutate
print("Columns in long_boxplot before mutate():")

[1] "Columns in long_boxplot before mutate():"

print(colnames(long_boxplot))

[1] "ENTREZID" "SYMBOL"   "Sample"   "log2CPM" 

📌Process Data with Metadata

formatted_data <- long_boxplot %>%
  mutate(
    Indv_id = sub("_.*", "", Sample),  # Extract individual ID
    Indv = ifelse(Indv_id %in% names(indv_mapping), as.character(indv_mapping[Indv_id]), NA),
    Drug = ifelse(grepl("CX.5461", Sample), "CX.5461", "VEH"),
    Conc = "0.1",
    Timepoint = "24"
  ) %>%
  dplyr::select(any_of(c("ENTREZID", "SYMBOL", "Sample", "Indv", "Drug", "Conc", "Timepoint", "log2CPM"))) %>%
  as.data.frame()

# Rename SYMBOL → Gene
colnames(formatted_data) <- trimws(colnames(formatted_data))
if ("SYMBOL" %in% colnames(formatted_data)) {
  colnames(formatted_data)[colnames(formatted_data) == "SYMBOL"] <- "Gene"
} else {
  stop("ERROR: 'SYMBOL' column is missing before renaming.")
}

if (!"Gene" %in% colnames(formatted_data)) stop("ERROR: 'Gene' column is missing after renaming.")

formatted_data$Indv <- as.character(formatted_data$Indv)

# Display first few rows
head(formatted_data)

  ENTREZID   Gene              Sample Indv    Drug Conc Timepoint  log2CPM
1    55635 DEPDC1     87.1_VEH_0.1_24    3     VEH  0.1        24 4.942031
2    55635 DEPDC1     17.3_VEH_0.1_24    4     VEH  0.1        24 4.691377
3    55635 DEPDC1 84.1_CX.5461_0.1_24    5 CX.5461  0.1        24 3.294382
4    55635 DEPDC1     84.1_VEH_0.1_24    5     VEH  0.1        24 4.869754
5    55635 DEPDC1 90.1_CX.5461_0.1_24    6 CX.5461  0.1        24 2.598657
6    55635 DEPDC1     90.1_VEH_0.1_24    6     VEH  0.1        24 4.176633

📌Generate Boxplot

ggplot(formatted_data, aes(x = Drug, y = log2CPM, fill = Drug)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = drug_palc) +
  facet_wrap(~ Gene, ncol = 5) +
  geom_point(aes(color = Indv), size = 2, alpha = 0.5, position = position_jitter(width = -1, height = 0)) +
  ggtitle("log2CPM CX.5461_0.1 vs Vehicle_0.1 (24hrs)") +
  labs(x = "Drugs", y = "log2CPM") +
  theme_bw() +
  theme(
    plot.title = element_text(size = rel(2), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.ticks = element_line(linewidth = 1.5),
    axis.line = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black"),
    strip.text.y = element_text(color = "white")
  )

📌 CX_0.1_48 vs VEH_0.1_48

📌 Define File Paths

# Define file paths
input_file <- "data/Feature_count_Matrix_Log2CPM_filtered.csv"
toptable_file <- "data/DEGs/Toptable_CX_0.1_48.csv"
output_boxplot <- "data/Boxplot_CX_0.1_48.csv"
output_log2CPM <- "data/log2CPM_filtered.csv"

📌 Read and Clean Dataset

# Read Log2CPM Matrix
boxplot1 <- read.csv(input_file) %>%
  as.data.frame()
colnames(boxplot1) <- trimws(gsub("^X", "", colnames(boxplot1)))  # Clean column names

# Select relevant columns
matching_columns <- grep("ENTREZID|SYMBOL|CX.5461_0.1_48|VEH_0.1_48", colnames(boxplot1), value = TRUE)

if (!"SYMBOL" %in% colnames(boxplot1)) stop("ERROR: 'SYMBOL' column is missing in boxplot1! Check dataset.")

new_boxplot1 <- boxplot1[, matching_columns]

# Save cleaned datasets
write.csv(boxplot1, output_log2CPM, row.names = FALSE)
write.csv(new_boxplot1, output_boxplot, row.names = FALSE)

# Display first few rows
head(new_boxplot1)

📌 Define Color Palettes

drug_palc <- c("CX.5461" = "#8B006D", "VEH" = "#F1B72B")

📌 Read and Process Toptable Data

toptable <- read.csv(toptable_file)
colnames(toptable)[1] <- "Entrezid"

filtered_toptable <- toptable %>%
  filter(adj.P.Val < 0.05) %>%
  arrange(adj.P.Val) %>%
  slice_head(n = 10)

# Display first few rows of the filtered Toptable
head(filtered_toptable)

  Entrezid     logFC  AveExpr         t  P.Value adj.P.Val        B
1     9787 -3.749715 3.045935 -14.24839 2.49e-25  1.50e-21 46.78947
2     5347 -3.292374 3.460559 -14.29479 2.02e-25  1.50e-21 47.13645
3     1062 -3.116017 2.975691 -14.19627 3.16e-25  1.50e-21 46.67902
4    55635 -3.247028 3.616797 -14.11906 4.50e-25  1.61e-21 46.41416
5    10112 -4.106672 3.471443 -13.42736 1.10e-23  3.15e-20 43.16052
6     9824 -2.534304 3.672008 -12.97567 9.16e-23  2.18e-19 41.25660

📌 Filter Boxplot Data for Selected Genes

filtered_boxplot <- new_boxplot1 %>%
  filter(ENTREZID %in% filtered_toptable$Entrezid)

if (!"SYMBOL" %in% colnames(filtered_boxplot)) stop("ERROR: 'SYMBOL' column is missing in filtered_boxplot!")

# Pivot data to long format
long_boxplot <- filtered_boxplot %>%
  pivot_longer(cols = -c(ENTREZID, SYMBOL), names_to = "Sample", values_to = "log2CPM") %>%
  as.data.frame()

if (!"SYMBOL" %in% colnames(long_boxplot)) stop("ERROR: 'SYMBOL' column is missing after pivoting!")

long_boxplot$SYMBOL <- as.character(long_boxplot$SYMBOL)

📌 Define Individual Mapping

indv_mapping <- c("75.1" = 1, "78.1" = 2, "87.1" = 3, "17.3" = 4, "84.1" = 5, "90.1" = 6)

# Debug: Print column names before mutate
print("Columns in long_boxplot before mutate():")

[1] "Columns in long_boxplot before mutate():"

print(colnames(long_boxplot))

[1] "ENTREZID" "SYMBOL"   "Sample"   "log2CPM" 

📌Process Data with Metadata

formatted_data <- long_boxplot %>%
  mutate(
    Indv_id = sub("_.*", "", Sample),  # Extract individual ID
    Indv = ifelse(Indv_id %in% names(indv_mapping), as.character(indv_mapping[Indv_id]), NA),
    Drug = ifelse(grepl("CX.5461", Sample), "CX.5461", "VEH"),
    Conc = "0.1",
    Timepoint = "48"
  ) %>%
  dplyr::select(any_of(c("ENTREZID", "SYMBOL", "Sample", "Indv", "Drug", "Conc", "Timepoint", "log2CPM"))) %>%
  as.data.frame()

# Rename SYMBOL → Gene
colnames(formatted_data) <- trimws(colnames(formatted_data))
if ("SYMBOL" %in% colnames(formatted_data)) {
  colnames(formatted_data)[colnames(formatted_data) == "SYMBOL"] <- "Gene"
} else {
  stop("ERROR: 'SYMBOL' column is missing before renaming.")
}

if (!"Gene" %in% colnames(formatted_data)) stop("ERROR: 'Gene' column is missing after renaming.")

formatted_data$Indv <- as.character(formatted_data$Indv)

# Display first few rows
head(formatted_data)

  ENTREZID   Gene              Sample Indv    Drug Conc Timepoint  log2CPM
1    55635 DEPDC1 87.1_CX.5461_0.1_48    3 CX.5461  0.1        48 1.923754
2    55635 DEPDC1     87.1_VEH_0.1_48    3     VEH  0.1        48 5.592110
3    55635 DEPDC1 17.3_CX.5461_0.1_48    4 CX.5461  0.1        48 1.432382
4    55635 DEPDC1     17.3_VEH_0.1_48    4     VEH  0.1        48 5.042248
5    55635 DEPDC1 84.1_CX.5461_0.1_48    5 CX.5461  0.1        48 2.177464
6    55635 DEPDC1     84.1_VEH_0.1_48    5     VEH  0.1        48 4.930465

📌Generate Boxplot

ggplot(formatted_data, aes(x = Drug, y = log2CPM, fill = Drug)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = drug_palc) +
  facet_wrap(~ Gene, ncol = 5) +
  geom_point(aes(color = Indv), size = 2, alpha = 0.5, position = position_jitter(width = -1, height = 0)) +
  ggtitle("log2CPM CX.5461_0.1 vs Vehicle_0.1 (48hrs)") +
  labs(x = "Drugs", y = "log2CPM") +
  theme_bw() +
  theme(
    plot.title = element_text(size = rel(2), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.ticks = element_line(linewidth = 1.5),
    axis.line = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black"),
    strip.text.y = element_text(color = "white")
  )

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 22631)

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] tidyr_1.3.1     ggsignif_0.6.4  ggplot2_3.5.1   dplyr_1.1.4    
[5] workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      jsonlite_1.8.9    compiler_4.3.0    promises_1.3.0   
 [5] tidyselect_1.2.1  Rcpp_1.0.12       stringr_1.5.1     git2r_0.35.0     
 [9] callr_3.7.6       later_1.3.2       jquerylib_0.1.4   scales_1.3.0     
[13] yaml_2.3.10       fastmap_1.1.1     R6_2.5.1          labeling_0.4.3   
[17] generics_0.1.3    knitr_1.49        tibble_3.2.1      munsell_0.5.1    
[21] rprojroot_2.0.4   bslib_0.8.0       pillar_1.10.1     rlang_1.1.3      
[25] cachem_1.0.8      stringi_1.8.3     httpuv_1.6.15     xfun_0.50        
[29] getPass_0.2-4     fs_1.6.3          sass_0.4.9        cli_3.6.1        
[33] withr_3.0.2       magrittr_2.0.3    ps_1.8.1          grid_4.3.0       
[37] digest_0.6.34     processx_3.8.5    rstudioapi_0.17.1 lifecycle_1.0.4  
[41] vctrs_0.6.5       evaluate_1.0.3    glue_1.7.0        farver_2.1.2     
[45] whisker_0.4.1     colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.29   
[49] httr_1.4.7        tools_4.3.0       pkgconfig_2.0.3   htmltools_0.5.8.1