实验四：qPCR 数据分析¶
📖 实验简介¶
qPCR（实时荧光定量 PCR）是分子生物学中常用的基因表达定量技术。本实验学习如何处理和分析 qPCR 数据。
🎯 实验目的¶
- 理解 qPCR 原理和数据格式
- 掌握 Ct 值的处理方法
- 学会相对表达量的计算（ΔΔCt 方法）
- 进行统计检验和可视化
📚 理论背景¶
qPCR 原理¶
实时荧光定量 PCR 通过实时监测 PCR 反应中荧光信号的变化来定量 DNA/RNA 模板。
Ct 值¶
Ct (Cycle threshold)：荧光信号达到设定阈值时的循环数。
- Ct 值越小，表示起始模板量越多
- Ct 值与起始模板量呈对数关系
ΔΔCt 方法¶
相对定量的标准方法：
\[
\Delta Ct = Ct_{目标基因} - Ct_{内参基因}
\]
\[
\Delta\Delta Ct = \Delta Ct_{实验组} - \Delta Ct_{对照组}
\]
\[
相对表达量 = 2^{-\Delta\Delta Ct}
\]
💻 实验步骤¶
1. 数据导入和检查¶
library(ggplot2)
library(dplyr)
library(tidyr)
# 读取数据
qpcr_data <- read.csv("qPCR_data.csv")
# 查看数据结构
head(qpcr_data)
str(qpcr_data)
# 数据格式示例：
# Sample Gene Ct Group
# Sample1 GAPDH 18.5 Control
# Sample1 Gene1 22.3 Control
# ...
2. 数据质控¶
# 检查技术重复的一致性
qpcr_summary <- qpcr_data %>%
group_by(Sample, Gene, Group) %>%
summarise(
mean_Ct = mean(Ct, na.rm = TRUE),
sd_Ct = sd(Ct, na.rm = TRUE),
cv = sd_Ct / mean_Ct * 100, # 变异系数
.groups = "drop"
)
# 标记CV过大的数据点（通常>2%需要注意）
qpcr_summary$qc_flag <- ifelse(qpcr_summary$cv > 2, "Check", "Pass")
# 可视化技术重复的一致性
ggplot(qpcr_summary, aes(x = Gene, y = cv, fill = qc_flag)) +
geom_bar(stat = "identity") +
geom_hline(yintercept = 2, linetype = "dashed", color = "red") +
labs(title = "Coefficient of Variation for Technical Replicates",
x = "Gene",
y = "CV (%)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
3. 计算 ΔCt¶
# 分离内参基因（假设为 GAPDH）
housekeeping_gene <- "GAPDH"
# 获取每个样本的内参 Ct 值
hk_ct <- qpcr_summary %>%
filter(Gene == housekeeping_gene) %>%
select(Sample, Group, hk_Ct = mean_Ct)
# 计算 ΔCt
delta_ct <- qpcr_summary %>%
filter(Gene != housekeeping_gene) %>%
left_join(hk_ct, by = c("Sample", "Group")) %>%
mutate(delta_Ct = mean_Ct - hk_Ct)
head(delta_ct)
4. 计算 ΔΔCt 和相对表达量¶
# 计算对照组的平均 ΔCt
control_delta_ct <- delta_ct %>%
filter(Group == "Control") %>%
group_by(Gene) %>%
summarise(control_mean_delta_Ct = mean(delta_Ct), .groups = "drop")
# 计算 ΔΔCt
ddct_data <- delta_ct %>%
left_join(control_delta_ct, by = "Gene") %>%
mutate(
delta_delta_Ct = delta_Ct - control_mean_delta_Ct,
relative_expression = 2^(-delta_delta_Ct)
)
head(ddct_data)
5. 统计检验¶
# t检验比较实验组和对照组
stat_results <- ddct_data %>%
group_by(Gene) %>%
summarise(
control_mean = mean(relative_expression[Group == "Control"]),
treatment_mean = mean(relative_expression[Group == "Treatment"]),
fold_change = treatment_mean / control_mean,
p_value = t.test(relative_expression ~ Group)$p.value,
.groups = "drop"
) %>%
mutate(
significant = ifelse(p_value < 0.05, "Yes", "No"),
regulation = case_when(
fold_change > 1.5 & p_value < 0.05 ~ "Up",
fold_change < 0.67 & p_value < 0.05 ~ "Down",
TRUE ~ "No change"
)
)
print(stat_results)
6. 数据可视化¶
条形图¶
# 计算均值和标准误
plot_data <- ddct_data %>%
group_by(Gene, Group) %>%
summarise(
mean_expr = mean(relative_expression),
se = sd(relative_expression) / sqrt(n()),
.groups = "drop"
)
# 绘制条形图
ggplot(plot_data, aes(x = Gene, y = mean_expr, fill = Group)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
geom_errorbar(aes(ymin = mean_expr - se, ymax = mean_expr + se),
position = position_dodge(width = 0.9),
width = 0.25) +
geom_hline(yintercept = 1, linetype = "dashed", color = "gray50") +
labs(title = "Relative Gene Expression",
x = "Gene",
y = "Relative Expression (2^-ΔΔCt)",
fill = "Group") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
箱线图¶
ggplot(ddct_data, aes(x = Gene, y = relative_expression, fill = Group)) +
geom_boxplot() +
geom_jitter(position = position_jitterdodge(jitter.width = 0.2),
alpha = 0.5, size = 1) +
geom_hline(yintercept = 1, linetype = "dashed", color = "gray50") +
labs(title = "Relative Gene Expression Distribution",
x = "Gene",
y = "Relative Expression (2^-ΔΔCt)",
fill = "Group") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
火山图（如果有多个基因）¶
ggplot(stat_results, aes(x = log2(fold_change), y = -log10(p_value))) +
geom_point(aes(color = regulation), size = 3) +
scale_color_manual(values = c("Up" = "red", "Down" = "blue", "No change" = "gray")) +
geom_vline(xintercept = c(-log2(1.5), log2(1.5)), linetype = "dashed") +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
geom_text(aes(label = Gene), vjust = -0.5, size = 3) +
labs(title = "Volcano Plot",
x = "log2(Fold Change)",
y = "-log10(p-value)",
color = "Regulation") +
theme_minimal()
📊 结果解释¶
相对表达量¶
- = 1：表达量与对照组相同
- > 1：表达上调
- < 1：表达下调
Fold Change¶
- FC > 1.5 且 p < 0.05：显著上调
- FC < 0.67 且 p < 0.05：显著下调
🔍 注意事项¶
实验设计
- 确保技术重复（至少3次）
- 生物学重复建议≥3个
- 选择合适稳定的内参基因
数据质控
- Ct > 35 通常认为不可靠
- 技术重复的 CV 应 < 2%
- 内参基因在不同组间应稳定
内参基因选择
常用内参基因：GAPDH、β-actin、18S rRNA
内参基因应： - 表达稳定 - 不受实验条件影响 - 表达量适中
📝 作业要求¶
- 数据分析
- 导入并检查数据
- 进行质量控制
-
计算相对表达量
-
统计分析
- 进行 t 检验
- 判断哪些基因显著差异表达
-
计算 fold change
-
结果可视化
- 绘制条形图和箱线图
- 添加误差线和统计显著性标记
-
图表美观、标注清晰
-
结果解释
- 对差异基因的生物学意义进行讨论
- 与已知文献比较
- 提出进一步验证的思路
📁 文件位置¶
Grade4/computational_biology/experiments/Exp4/
├── Exp4.Rmd # R Markdown 报告
├── Exp4.html # 生成的 HTML 报告
└── qPCR_data.csv # qPCR 数据
📚 参考资料¶
-
Livak, K. J., & Schmittgen, T. D. (2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods, 25(4), 402-408.
-
Bustin, S. A., et al. (2009). The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clinical Chemistry, 55(4), 611-622.
💡 提示¶
PCR 效率
本实验假设 PCR 效率为 100%（扩增效率 = 2）。如果效率不是 100%，需要使用修正公式：
\[相对表达量 = E^{-\Delta\Delta Ct}\]
其中 E 是实际的扩增效率。
多重比较校正
如果检测多个基因，考虑进行 Bonferroni 或 FDR 校正。