我对 R 比较陌生，并且在显示显着差异的星号时遇到问题，尽管方差分析和 Tukey 事后显示 p <0.05. I also cannot seem to get the tukey comparisons to only show comparisons to Concentration_0. Any help would be appreciated
代码：
# Step 0: Packages
library(tidyverse)
library(ggsignif)
# Step 1: Create the data frame (Your provided data)
data <- data.frame(
Concentration_100 = c(37064, 43632, 41114, 30876),
Concentration_10 = c(45548, 47482, 41894, 44574),
Concentration_1 = c(45587, 48042, 47934, 40098),
Concentration_0.1 = c(32750, 41024, 39483, 34704),
Concentration_0.01 = c(45407, 53434, 48684, 42951),
Concentration_0 = c(101895, 132464, 103751, 108483)
)
# Step 2: Reshape the data to long format using tidyr
long_data <- data %>%
pivot_longer(cols = everything(),
names_to = "Concentration",
values_to = "Value")
# Step 3: Calculate the averages and standard errors for each concentration
average_data <- long_data %>%
group_by(Concentration) %>%
summarize(Average = mean(Value), .groups = "drop")
# Calculate the standard error
se_data <- long_data %>%
group_by(Concentration) %>%
summarize(SE = sd(Value) / sqrt(n()), .groups = "drop")
# Merge averages and standard errors into a single data frame
average_data <- merge(average_data, se_data, by = "Concentration")
# Step 4: Convert Concentration to a factor for proper ordering
long_data$Concentration <- factor(long_data$Concentration,
levels = c("Concentration_100",
"Concentration_10",
"Concentration_1",
"Concentration_0.1",
"Concentration_0.01",
"Concentration_0"))
# Step 5: Perform the one-way ANOVA
anova_result <- aov(Value ~ Concentration, data = long_data)
# Step 6: Run Tukey's HSD (Post-Hoc Test)
tukey_result <- TukeyHSD(anova_result)
# Step 7: Extract only comparisons with Concentration_0
tukey_comparisons <- tukey_result$Concentration
# Filter to show comparisons with Concentration_0
comparisons_with_control <- tukey_result$`Concentration`[
grep("Concentration_0", rownames(tukey_result$`Concentration`),
ignore.case = TRUE), ]
# Print the filtered comparisons
print(comparisons_with_control)
# Step 8: Define a custom color palette for the columns (from light to dark blue)
blue_colors <- c(
"Concentration_100" = "#A3C9F1", # light blue
"Concentration_10" = "#6FA9D2", # medium-light blue
"Concentration_1" = "#3989B1", # medium blue
"Concentration_0.1" = "#1F6C96", # darker blue
"Concentration_0.01" = "#115F82", # dark blue
"Concentration_0" = "#0C4E6D" # darkest blue
)
# Find the maximum value in the data to ensure it's not clipped
max_value <- max(long_data$Value, na.rm = TRUE) # Get the maximum value from the raw data points
# Set the upper limit for the y-axis to be a bit higher than the maximum value
y_axis_limit <- max_value * 1.1 # Increase by 10% to add some space
# Step 9: Create the plot with significance indicators and custom colors
ggplot() +
# Add the average columns (bar heights) with custom colors
geom_col(data = average_data, aes(x = Concentration,
y = Average, fill = Concentration),
width = 0.6, color = "black") +
# Add individual data points in front of the columns
geom_jitter(data = long_data, aes(x = Concentration, y = Value),
color = "black", size = 3, width = 0.2,
height = 0, alpha = 0.8, shape = 21, fill = "gray") +
# Add standard error bars
geom_errorbar(data = average_data, aes(x = Concentration,
ymin = Average - SE, ymax = Average + SE),
width = 0.2, color = "black") +
# Customize axis labels and title
labs(x = "Bortezomib (µM)", # x-axis title
y = "Relative Fluorescence Intensity (544/590 nm)") + # y-axis title
# Adjust x-axis labels to show just the concentration values
scale_x_discrete(labels = c("Concentration_100" = "100",
"Concentration_10" = "10",
"Concentration_1" = "1",
"Concentration_0.1" = "0.1",
"Concentration_0.01" = "0.01",
"Concentration_0" = "0")) +
# Adjust the y-axis to reduce padding (set expand to c(0, 0) to remove space above the columns)
scale_y_continuous(expand = c(0, 0), limits = c(0, y_axis_limit)) +
# Apply the custom color palette
scale_fill_manual(values = blue_colors) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5,
colour = "black"),
axis.text.y = element_text(colour = "black"),
axis.line = element_line(color = "black", size = 0.5), # Add axis lines
panel.background = element_blank(), # Remove panel background
panel.grid = element_blank(), # Remove grid lines
legend.position = "none", # Remove the legend
axis.ticks.x = element_line(color = "black")) +
# Add significance indicators using ggsignif for only comparisons with Concentration_0
geom_signif(
comparisons = list(
c("Concentration_0", "Concentration_100"),
c("Concentration_0", "Concentration_10"),
c("Concentration_0", "Concentration_1"),
c("Concentration_0", "Concentration_0.1"),
c("Concentration_0", "Concentration_0.01")
),
map_signif_level = TRUE, textsize = 3,
y_position = y_axis_limit * 1.05
)
Chatgpt 无法帮助我解决这个问题
您没有将任何数据或美学映射传递给 geom_signif()。将其添加到 ggplot 函数或 geom_signif 中。此外，y_position 超出范围，因此您需要扩大范围或降低范围。
例如：
geom_signif(
data = average_data,
aes(x = Concentration,
y = Average),
comparisons = list(
c("Concentration_0", "Concentration_100"),
c("Concentration_0", "Concentration_10"),
c("Concentration_0", "Concentration_1"),
c("Concentration_0", "Concentration_0.1"),
c("Concentration_0", "Concentration_0.01")
),
map_signif_level = TRUE, textsize = 3,
y_position = y_axis_limit * 0.9
)