CytoProfile (version 0.1.2)

cyt_errbp: Error-bar Plot.

Description

This function draws an error-bar plot for comparing groups to a baseline group. It creates a barplot of the central tendency (mean or median) and overlays error bars representing the spread (e.g., standard deviation, MAD, or standard error). Optionally, p-value and effect size labels (based on SSMD) are added, either as symbols or numeric values.

Usage

cyt_errbp(
  data,
  p_lab = TRUE,
  es_lab = TRUE,
  class_symbol = TRUE,
  x_lab = "",
  y_lab = "",
  main = ""
)

Value

An error-bar plot is produced.

Arguments

data

A data frame containing the following columns for each group:

• name: Group names.

• center: Mean or median values.

• spread: Standard deviation, MAD, or standard error.

• p.value: P-value for the comparison.

• effect.size: Effect size based on SSMD.

Note: The first row of data must correspond to the baseline group.

p_lab

Logical. Whether to label the p-values on the plot. Default is TRUE.

es_lab

Logical. Whether to label the effect sizes on the plot. Default is TRUE.

class_symbol

Logical. Whether to use symbolic notation for significance and effect size. Default is TRUE.

x_lab

Character. Label for the x-axis.

y_lab

Character. Label for the y-axis.

main

Character. Title of the graph.

Examples

# Load sample data
data_df <- ExampleData1[,-c(3)]
cyt_mat <- log2(data_df[, -c(1:2)])
data_df1 <- data.frame(data_df[, 1:2], cyt_mat)
cytokineNames <- colnames(cyt_mat)
nCytokine <- length(cytokineNames)
condt <- !is.na(cyt_mat) & (cyt_mat > 0)
Cutoff <- min(cyt_mat[condt], na.rm = TRUE) / 10
# Create matrices for ANOVA and Tukey results
p_aov_mat <- matrix(NA, nrow = nCytokine, ncol = 3)
dimnames(p_aov_mat) <- list(cytokineNames,
                         c("Group", "Treatment", "Interaction"))
p_groupComp_mat <- matrix(NA, nrow = nCytokine, ncol = 3)
dimnames(p_groupComp_mat) <- list(cytokineNames,
                                 c("2-1", "3-1", "3-2"))
ssmd_groupComp_stm_mat <- mD_groupComp_stm_mat <- p_groupComp_stm_mat <-
p_groupComp_mat

for (i in 1:nCytokine) {
Cytokine <- (cyt_mat[, i] + Cutoff)
cytokine_aov <- aov(Cytokine ~ Group * Treatment, data = data_df)
aov_table <- summary(cytokine_aov)[[1]]
p_aov_mat[i, ] <- aov_table[1:3, 5]
p_groupComp_mat[i, ] <- TukeyHSD(cytokine_aov)$Group[1:3, 4]
p_groupComp_stm_mat[i, ] <- TukeyHSD(cytokine_aov)$`Group:Treatment`[1:3, 4]
mD_groupComp_stm_mat[i, ] <- TukeyHSD(cytokine_aov)$`Group:Treatment`[1:3, 1]
ssmd_groupComp_stm_mat[i, ] <- mD_groupComp_stm_mat[i, ] / sqrt(2 *
aov_table["Residuals", "Mean Sq"])
}

results <- cyt_skku(ExampleData1[, -c(3)], print_res_log = TRUE,
                 group_cols = c("Group", "Treatment"))

oldpar <- par(no.readonly = TRUE)
par(mfrow = c(2,3), mar = c(8.1, 4.1, 4.1, 2.1))

for (k in 1:nCytokine) {
result_mat <- results[1:9, , k]
center_df <- data.frame(
 name = rownames(result_mat),
 result_mat[, c("center", "spread")],
 p.value = c(1, p_groupComp_stm_mat[k, 1:2]),
 effect.size = c(0, ssmd_groupComp_stm_mat[k, 1:2])
 )
cyt_errbp(center_df, p_lab = TRUE, es_lab = TRUE,
         class_symbol = TRUE,
         y_lab = "Concentration in log2 scale",
         main = cytokineNames[k])
}
par(oldpar)