相关软件（R代码）

# 泊松回归示例：某基因在不同处理组中的表达计数
# 模拟数据
count_data <- data.frame(
  count = c(rpois(10, 5), rpois(10, 10), rpois(10, 15)),
  treatment = factor(rep(c("Control", "Low", "High"), each = 10)),
  size_factor = rep(c(1.0, 1.1, 0.9), each = 10)  # 测序深度
)
# 泊松回归（带偏移量）
model_pois <- glm(count ~ treatment + offset(log(size_factor)), 
                  data = count_data, family = poisson)
summary(model_pois)
# 检验过度离散
dispersion <- sum(residuals(model_pois, type = "pearson")^2) / df.residual(model_pois)
cat("离散参数:", dispersion, "\n")
# 如果过度离散，使用负二项回归
# install.packages("MASS")
library(MASS)
model_nb <- glm.nb(count ~ treatment + offset(log(size_factor)), data = count_data)
summary(model_nb)

2.3.5 Cox比例风险模型