R语言绘图-CRISPR sgRNA文库质量检测

R语言绘图-CRISPR sgRNA文库质量检测、
cumsum
均一度
分布图

数据如下：

> head(data)
                     sgRNA                  Gene Mock MEM1st
1                 44636017                 YIPF7    5      0
2                 37207246                 SSTR3   18      7
3                 19814803                RNF186   44      0
4                 30645429                SMIM18   69      0
5 Non-Targeting Control552 Non-Targeting Control  111      0
6                 67512306                  DSEL  113      0

完整代码如下：

library(ggplot2)
#setwd("D:\\Coding\\R_gzlab_docu\\CrisprNGS")
# 文件路径
# file = "E:\\big_data\\CRISPR-HHN\\test\\HHN-Mock_Crispr.count.csv"
# file = "E:\\big_data\\CRISPR-HHN\\2024.5.15-CRISPR-2nd\\00.CleanData\\toHHN-CRISPR-2024.5.15-2nd-R-\\mock_vs_top30.count.txt"
# file="E:\\big_data\\CRISPR-MKX\\2024.6.3-MKX-mem\\分析结果\\mock_vs_MEM2nd.count.txt"
file="E:\\big_data\\CRISPR-MKX\\2024.6.3-MKX-mem\\分析结果\\mock_vs_MEM1st.count.txt"
data <- read.csv(file,header = T,sep="\t")
head(data)

#file = "E:\\big_data\\CRISPR-HHN\\test\\count\\mock_vs_top5.count.txt"
#data <- read.table(file,header=T)
#data <- read.csv("aqyISG.count.csv",header = T)

# 定义数据列
col = data$Mock
# col = data$Top10
# col = data$Top30
# col = data$Mock
# col = data$MEM2nd
# col=data$MEM1st

# countsummary = read.delim(file,check.names = FALSE)
# head(countsummary)

p1<-ggplot(data.frame(x = log2(col)), aes(x = x)) + geom_density(fill = "#69b3a2", alpha = 0.8) + labs(title = "Readcounts distribution", x = "log2 normalized sgRNA read counts", y = "Density") + theme_minimal()
p2<-ggplot(data.frame(x = log2(col)), aes(x = x))+stat_ecdf(col = "#13e3a2", linewidth=1.2) + labs(title = "Readcounts distribution", x = "log2 normalized sgRNA read counts", y = "Cumulative Frequence")+theme_minimal()
#ggThemeAssistGadget(p2)
p1
p2
p <- cowplot::plot_grid(p1, p2, nrow = 2, labels = LETTERS[1:2])#将p1-p2组合成一幅图，按照两行排列，标签分别为A、B。（LETTERS[1:4] 意为提取26个大写英文字母的前两个:A、B）
p

结果如下图：

density+stat_ecdf

read.delim

read.delim和read.table的区别为：读取数据速度不同、要求不同、空串不同。

一、读取数据速度不同

1、read.delim：read.delim的读取数据速度比read.table的读取数据速度更快。

2、read.table：read.table的读取数据速度比read.delim的读取数据速度更慢。

二、要求不同

1、read.delim：read.delim不要求所有列都对等，会按最大列，或指定的列数填充。

2、read.table：read.table严格要求所有列都对等。

代码如下：

countsummary = read.delim(file,check.names = FALSE)
head(countsummary)

部分读取结果如下：

> head(countsummary)
                     sgRNA                  Gene Mock MEM1st
1                 44636017                 YIPF7    5      0
2                 37207246                 SSTR3   18      7
3                 19814803                RNF186   44      0
4                 30645429                SMIM18   69      0
5 Non-Targeting Control552 Non-Targeting Control  111      0
6                 67512306                  DSEL  113      0