Ribo测序翻译效率计算及可视化

最新推荐文章于 2025-07-24 22:49:06 发布

Tiaoxiaobai

最新推荐文章于 2025-07-24 22:49:06 发布

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分类专栏：转录和翻译组分析文章标签： python 开发语言笔记

本文链接：https://blog.youkuaiyun.com/Tiaoxiaobai/article/details/149530616

转录和翻译组分析专栏收录该内容

1 篇文章

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一、翻译效率计算

（1）准备文件：

转录组和翻译组测序的表达量表（经过标准化的TPM/FPKM）矩阵如图

（2）实现代码

## get TPM值
import pandas as pd
import numpy as np
import sys 
tpm = sys.argv[1]  ##输入TPM表达量表  如TPM.gene.xls
outfile = sys.argv[2]  ##输出文件名字，如TE.xls

df = pd.read_csv(tpm, index_col=0, sep="\t")
rna_cols = [col for col in df.columns if  'RNA' in col]
ribo_cols = [col for col in df.columns if 'Ribo' in col]

##配对列名去除前缀
def get_suffix(col):
    return "_".join(col.split("_")[1:])

pairs_name = []

for rna_col in rna_cols:
    suffix = get_suffix(rna_col)
    ribo_col = "Ribo_" + suffix
    if ribo_col in ribo_cols:
        pairs_name.append((rna_col, ribo_col))

##创建TE的数据框
te_df = pd.DataFrame(index=df.index)
for rna, ribo in pairs_name:
    te_name = get_suffix(ribo)
    te_df[te_name] = df[ribo]/df[rna].replace(0, np.nan)

##保存结果
te_df.to_csv(outfile, sep="\t", index=True)


def main():
    print("你传入的参数是：", sys.argv)

if __name__ == "__main__":
    main()

（3）翻译表达量与转录表达量相关性分析

library(ggplot2)
library(reshape2)
theme_set(theme_bw())
group <- read.table("sample.txt", header = T)
data <- read.table("TPM.gene.xls", header=T, check.names=F, stringsAsFactors=F, row.names=1)

data2 <- data[which(rowSums(data[, group$RNA])> 0),]
for (i in 1:nrow(group)) {
  samplename <- sub("RNA_", "", group$RNA[i])
  df <- data2[, c(group$RNA[i], group$Ribo[i])]
  df <- log10(df + 1)
  r <- cor(df[,1], df[,2])
  r <- round(r, digits=3)
  x_title <- paste0(group$RNA[i], " log10(TPM+1)")
  y_title <- paste0(group$Ribo[i], " log10(TPM+1)")
  p <- ggplot(df, aes(x=df[,1], y=df[,2])) + 
          geom_point(alpha=0.5, size=0.8)+ 
          geom_smooth(method="lm", colour="red", linewidth=0.8)+
          labs(x=x_title, y= y_title)+ 
          scale_y_continuous(expand=c(0,0),limits=c(0,NA))+
          scale_x_continuous(expand=c(0,0),limits=c(0,NA))+ 
          annotate("text", x=0.2, y=Inf, label=paste0("R = ", r), vjust=3, hjust=0, color="red")
  pngfile <- paste0("cor.rna_ribo.", samplename, ".png")
  pdffile <- paste0("cor.rna_ribo.", samplename, ".pdf")
  ggsave(pngfile, plot=p, width=6, height=6, dpi=400)
  ggsave(pdffile, plot=p, width=6, height=6, dpi=400)
}

展示图结果如下（可以自己设置外观，美化自己的图）