2025-03-22 手动绘制scatac footprint plot

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sample_names <- unique(seu$sample_names)
head(sample_names)

# 获取positionEnrichment数据
positionEnrichment <- GetAssayData(object = seu, assay = "peaks", layer = "positionEnrichment")

enrichment.matrix <- positionEnrichment[["TSS"]]
enrichment.matrix <- as.data.frame(as.matrix(enrichment.matrix))

TSS_profile_df <- data.frame()

# 使用提供的sample_names
for (sample in sample_names) {
  # 选择属于当前样本的细胞
  # 假设cell_id的格式是"SAMPLE_NAME_CELLBARCODE"
  sample_cells <- grepl(paste0("^", sample), rownames(enrichment.matrix))
  
  if (sum(sample_cells) > 0) { # 确保有匹配的细胞
    dataset_enrichment.matrix <- enrichment.matrix[sample_cells, ]
    
    # 计算平均值并添加到结果数据框
    sample_df <- data.frame(
      pos = colnames(dataset_enrichment.matrix),
      mean = colMeans(dataset_enrichment.matrix, na.rm = TRUE),
      dataset = sample
    )
    
    TSS_profile_df <- rbind(TSS_profile_df, sample_df)
  } else {
    print(paste("No cells found for sample:", sample))
  }
}

TSS_profile_df$pos <- as.numeric(TSS_profile_df$pos)

TSS_profile_df$dataset <- factor(TSS_profile_df$dataset, levels = sample_names)
head(TSS_profile_df)

# 设置调色
if (length(sample_names) <= 2) {
  newpalette <- c("#F39B7FFF", "#E64B35FF")[1:length(sample_names)]
} else {
  newpalette <- colorRampPalette(c("#F39B7FFF", "#E64B35FF"))(length(sample_names))
}

p1 <- ggplot(data = TSS_profile_df, aes(x = pos, y = mean, color = dataset)) +
  geom_line(stat = "identity", size = 0.2) +
  labs(x = "Distance from xxx motif (bp)", y = "Mean enrichment score") +
  scale_color_manual(values = newpalette) +
  theme_classic() +
  theme(
        axis.title.x = element_text(size = rel(2)),
        axis.title.y = element_text(size = rel(2), vjust = 2),
        axis.text = element_text(color = "black", size = rel(1.8)), 
        axis.line = element_line(colour = "black", size = 1),
        axis.ticks = element_line(),
        plot.margin = unit(c(0, 0.5, 0.5, 0.5), "cm"),
        legend.position = "none")

print(p1)