Last updated: 2025-03-10

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Rmd df69a87 sayanpaul01 2025-03-07 Commit
html df69a87 sayanpaul01 2025-03-07 Commit

📌 CX and DOX dose response curve

library(drc)
Warning: package 'drc' was built under R version 4.3.2
Warning: package 'MASS' was built under R version 4.3.1
# Load Data
DRC <- read.csv("data/CX-5461.csv")
DRC2 <- read.csv("data/DOX.csv")

# Fit dose-response models (including 0 concentration for curve fitting)
curved_fit <- drm(Ratio ~ Conc, data = DRC, fct = LL.4(c(NA, NA, 1, NA), 
                                                       names = c("hill", "min_value", "max_value", "EC_50")))
curved_fit2 <- drm(Ratio ~ Conc, data = DRC2, fct = LL.4(c(NA, NA, 1, NA), 
                                                         names = c("hill", "min_value", "max_value", "EC_50")))

# **Extract EC50 (LD50) Values**
EC50_CX <- summary(curved_fit)$coefficients["EC_50:(Intercept)", "Estimate"]
EC50_DOX <- summary(curved_fit2)$coefficients["EC_50:(Intercept)", "Estimate"]

# Define X-axis range (ensuring it starts from 0)
x_min <- min(c(DRC$Conc, DRC2$Conc))  # Keep 0 for the curves
x_max <- max(c(DRC$Conc, DRC2$Conc))  # Maximum concentration
x_range <- seq(x_min, x_max, length.out = 100)  # Generate smooth curve range

# **Create Base Plot with CX-5461 Curve (Thicker Line)**
plot(curved_fit, col = "blue", xlab = "Concentration (µM)", ylab = "Proportion of Viable Cells",
     confidence.level = 0.95, type = "all", main = "Dose-Response Curves with LD50",
     xlim = c(x_min, x_max), lwd = 3.5)

# **Add DOX Curve (Thicker Line)**
plot(curved_fit2, col = "red", confidence.level = 0.95, type = "all", add = TRUE, lwd = 3.5)

# **Ensure we exclude 0 concentration points before plotting**
DRC_no_zero <- subset(DRC, Conc > 0)  
DRC2_no_zero <- subset(DRC2, Conc > 0)  

# **Function to Add Individual Points**
add_points <- function(data, color) {
  data <- subset(data, Conc > 0)  # Remove 0 concentration points
  data$Ind <- as.numeric(data$Ind)
  
  # Assign shapes for each individual
  shape_mapping <- c(15, 16, 17)  # Square, Circle, Triangle
  
  for (i in seq_along(shape_mapping)) {
    ind_subset <- subset(data, Ind == i)  
    if (nrow(ind_subset) > 0) {  
      points(ind_subset$Conc, ind_subset$Ratio, 
             pch = shape_mapping[i], col = color, cex = 2, lwd = 2)
    }
  }
}

# **Now call add_points() with the filtered datasets (without 0 concentration points)**
add_points(DRC_no_zero, "blue")
add_points(DRC2_no_zero, "red")

# **Add Vertical LD50 (EC50) Lines**
abline(v = EC50_CX, col = "blue", lty = 2, lwd = 2)  # CX-5461 LD50 Line (Dashed)
abline(v = EC50_DOX, col = "red", lty = 2, lwd = 2)  # DOX LD50 Line (Dashed)

# **Add Labels for LD50**
text(EC50_CX, 0.5, labels = paste("LD50 CX:", round(EC50_CX, 2)), col = "blue", pos = 4, cex = 0.5, font = 2)
text(EC50_DOX, 0.5, labels = paste("LD50 DOX:", round(EC50_DOX, 2)), col = "red", pos = 4, cex = 0.5, font = 2)

# **Add Legends**
legend("topright", legend = c("CX-5461", "DOX"), col = c("blue", "red"), lty = 1, lwd = 3.5)
legend("bottomleft", title = "Individuals", legend = c("1", "2", "3"), 
       col = "black", pch = c(15, 16, 17), cex = 1.2)

Version Author Date
df69a87 sayanpaul01 2025-03-07

📌 Statistical Test

# Load necessary libraries
library(drc)

# Fit dose-response models
curved_fit_CX <- drm(Ratio ~ Conc, data = DRC, fct = LL.4())
curved_fit_DOX <- drm(Ratio ~ Conc, data = DRC2, fct = LL.4())

# Extract EC50 values with confidence intervals
EC50_CX <- ED(curved_fit_CX, 50, interval = "delta")

Estimated effective doses

       Estimate Std. Error   Lower   Upper
e:1:50   8.9057     3.6539  1.1966 16.6147
EC50_DOX <- ED(curved_fit_DOX, 50, interval = "delta")

Estimated effective doses

       Estimate Std. Error    Lower    Upper
e:1:50  0.57065    0.43538 -0.36315  1.50444
# Extract EC50 estimates and standard errors (Corrected)
EC50_CX_value <- EC50_CX[1,1]  # First row, first column
EC50_CX_SE <- EC50_CX[1,2]     # First row, second column (Standard Error)

EC50_DOX_value <- EC50_DOX[1,1]  # First row, first column
EC50_DOX_SE <- EC50_DOX[1,2]     # First row, second column (Standard Error)

# Perform a z-test to compare EC50 values
z_score <- abs(EC50_CX_value - EC50_DOX_value) / sqrt(EC50_CX_SE^2 + EC50_DOX_SE^2)
p_value <- 2 * (1 - pnorm(z_score))  # Two-tailed test

# Print results
print(paste("EC50 for CX-5461:", round(EC50_CX_value, 2)))
[1] "EC50 for CX-5461: 8.91"
print(paste("EC50 for DOX:", round(EC50_DOX_value, 2)))
[1] "EC50 for DOX: 0.57"
print(paste("P-value:", p_value))
[1] "P-value: 0.0235055023198667"
# Interpretation
if (p_value < 0.05) {
  print("The EC50 values are significantly different (p < 0.05).")
} else {
  print("The EC50 values are NOT significantly different (p ≥ 0.05).")
}
[1] "The EC50 values are significantly different (p < 0.05)."

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] drc_3.0-1   MASS_7.3-60

loaded via a namespace (and not attached):
 [1] sandwich_3.1-1    sass_0.4.9        gtools_3.9.5      stringi_1.8.3    
 [5] lattice_0.22-5    digest_0.6.34     magrittr_2.0.3    evaluate_1.0.3   
 [9] grid_4.3.0        mvtnorm_1.3-2     fastmap_1.1.1     rprojroot_2.0.4  
[13] workflowr_1.7.1   jsonlite_1.8.9    Matrix_1.6-1.1    whisker_0.4.1    
[17] Formula_1.2-5     survival_3.7-0    multcomp_1.4-26   promises_1.3.0   
[21] scales_1.3.0      TH.data_1.1-2     codetools_0.2-20  jquerylib_0.1.4  
[25] abind_1.4-8       cli_3.6.1         rlang_1.1.3       munsell_0.5.1    
[29] splines_4.3.0     plotrix_3.8-4     cachem_1.0.8      yaml_2.3.10      
[33] tools_4.3.0       colorspace_2.1-0  httpuv_1.6.15     vctrs_0.6.5      
[37] R6_2.5.1          zoo_1.8-12        lifecycle_1.0.4   git2r_0.35.0     
[41] stringr_1.5.1     fs_1.6.3          car_3.1-3         pkgconfig_2.0.3  
[45] pillar_1.10.1     bslib_0.8.0       later_1.3.2       glue_1.7.0       
[49] Rcpp_1.0.12       xfun_0.50         tibble_3.2.1      rstudioapi_0.17.1
[53] knitr_1.49        htmltools_0.5.8.1 rmarkdown_2.29    carData_3.0-5    
[57] compiler_4.3.0