0228

1.some refernce material
R Cookbook
R in Action
ggplot2
Advanced R
2.Installing and Loading Package
installing:install.packages('ggplot2')
loading:library(ggplot2)
updating:update.packages()
3.R language basics
create a vector: v=(1,4,4,3,2,2,3) or w=c("apple","banana","orange")
return certain elements: v【c(2,3,4)】 or v【2:4】 v【c(2,4,3)】
Delete certain element:v=【-2】删除第二个元素 or v=【-2:-4】删除2到4个元素
Extract element: v【v<3】提取所有小于3的元素
Find elements: which(v==3) NOTE:the returns are the indices of elements，“=”是赋值，“==”是相等。
which.max(v)最大值
which.min(v)最小值

0229

4.Numbers随机数
Repeatable Random Numbers:set.seed(250)【12:00】使电脑重复产生的随机数相同
Random Number：a=runif(3,min=0,max=100)
Rounding of Numbers:floor(2)向下取整 ceiling（a）向上取整 round（a，4）括弧中的数字为保留的小数位数
Random Numbers from Other Distributions:rnorm()正态分布,rexp()指数分布,rbinom(),rgeom()几何分布,rnbinom()负二项分布
？round 查询命令
？？round 产生与查询关键词相关内容
5.Data Input
loading local data:?read.csv;read.csv(file="……"); or read.table(file"……")
loading online data: read.csv("http://……")
attach:attach()
6.Graphs
plot:plot()
histograms:hist()直方图
density plot:plot(density())密度图
scatter plot:plot()散点图
box plot:boxplot(time~)箱线图
Q-Q plot:qqnorm(),qqline()and qqplot() quantiles & quanbles
par设定绘图环境
hist(x,breaks=20,col="blue")绘制直方图，竖条20根，填充为蓝色
plot(density(x))绘制曲线图
plot(x，type=“”)绘制散点图，形式为*
boxplot(x,y)箱线图 boxplot(time~***)区分性别

0330

a=c(1,2,3,4,5,6)
b=c("one","two","three")
c=c(TRUE,TRUE,FALSE)
x=matrix(1:20,nrow=5,ncol=4,byrow=TRUE)
y=matrix(1:20,nrow=5,ncol=4,byrow=FALSE)
x[2,]
x[,2]
x[2,c(2,4)]
x=[3:5,2]
rnames=c("apple","banana","orange","melon","corn")
cnanes=v("cat","dog","bird","pig")
rownames(x)=rnames
colnames(x)=cnames

0302

调成markdown编辑模式

一级标题

二级标题

三级标题

*斜体
我来自湖南
**加粗
黄鹤一去不复返

代码引用

大家好

图片引用

[图片上传失败...(image-cdfa6e-1587743770549)]
引用下文章叭

一片孤城万仞山

0303

1.putty安装

从官网下载putty，在putty中输入ip地址，再点open,再输入用户名和密码

2.在putty上操作Linux系统

思维导图：

TIM图片20200303211602.png

0304

下载miniconda

1.搜索conda官网，找到下载链接
2.ctrl+c复制链接
3.打开putty,cd biosoft
4.wget+右键粘贴链接
5.bash+刚下的文件
6.enter跳过加yes即可安装
7.最后激活：source ~/.bashrc
8.添加清华镜像：conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/
conda config --set show_channel_urls yes

运行miniconda

1.用conda list来查看
2.用conda search fastqc来搜索fastqc软件
3.conda install fastqc -y来安装
4.conda remove fastqc -y来卸载

备注：

我在添加清华镜像的时候出现了错误，这时可尝试rm ~/.condarc来删除之前的配置，这样就可以继续添加了！！

0305

R语言的基础

1.安装R和Rstudio

1.百度搜索R和Rstudio,在相应的网站中下好安装
要确保自己的电脑用户名是英文的哦

2.用R语言进行一些画图

-50个正态分布随机数的圆点图：plot(rnorm(50))

TIM图片20200305193010.png

箱型图： boxplot()

TIM截图20200305193635.png

3.R语言的基本操作

1.dir(目录)到达路径
2.<-赋值
3.删除变量：rm(x)
4.列出历史命令：history()
5.清空控制台：ctrl+l

0306

R语言数据结构

赋值

1.一个值：v<-8
2.多个不连续：v<-c(3,5,7)
3.多个连续值（1到10）：v<-[1:10]
4.数列：v<-seq(1:10,by=0.5)
5.重复：v<-rep(1:3,time=3)

取值

1.v[2]
2.v[1:3] 选第1到第3
3.v[c(1,3,4)] 选第1第3第4、
4.v[-4] 除了第四个数
5.v[v==10] v中等于10的数
6.v[v<4] v中小于4的数
7.v[v %in% c(1,3,4,5,5)] v中存在于向量中的数

读取本地数据和对数据的操作

1.读取数据：read.csv()文件应放置工作目录
2.导出数据：write.table()
3.设置行列名称
列名：colnames(v)[1]<-"xxxxx"改第一列的名称为xxxxx
行名：rownames(v)[1]<-"xxxxx"
4.数据保存：save.image(file="xxxx.Rdata")
单个变量保存：save.image(x,file="xxxx.Rdata")
5.提取数据框中元素：
x[x,y] 第x行第y列
x[x,] 第x行
x[,y] 第y列
6.将数据框名添加到搜索环境：attach(x)

问题

save(X,file="test.RData")这句代码如果报错X not found,是为什么，应该怎么解决？

答：是环境变量中没有X这个变量，解决的话应该找找是不是变量名弄错了，比如把小写x弄成大写

0307

image.png

0309

array

dim1=c("A1","A2")
dim2=c("B1","B2","B3")
dim3=c("C1","C2","C3","C4")
dim4=c("D1","D2","D3")
z=array(1:72, c(2,3,4,3),dimnames=list(dim1,dim2,dim3,dim4))
z[1,2,3,]

dataframe

attach(mtcars)
par(mfrow=c(1,4))#设置图的个数
plot(rnorm(50),pch=17)#pch点的形状
plot(rnorm(20),type = "l",lty=5)#lty线是形状
plot(rnorm(100),cex=3)#cex点的大小
plot(rnorm(200),lwd=2)#lwd线的大小
?pch
title(main = "normal list")
axis()
legend()
attach(mtcars)#加入R搜索途径
layout(matrix(c(1,1,2,3),2,2,byrow=TRUE))#矩阵

0311

对图形的设置

par(mfrow=c(1,4))#设置图的个数，partion
plot(rnorm(50),pch=17)#pch点的形状
plot(rnorm(20),type = "l",lty=5)#lty线是形状
plot(rnorm(100),cex=3)#cex点的大小
plot(rnorm(200),lwd=2)#lwd线的大小
?pch
title(main = "normal list")
axis()
legend()
attach(mtcars)#加入R搜索途径
layout(matrix(c(1,1,2,3),2,2,byrow=TRUE))#矩阵
hist(wt)#直方图
hist(mpg)
hist(disp)
hist(mtcars)
?pch

for和while的循环语句

for (i in 1:10) {#for loop循环，遍历
print(i)
i=i+1
}
i=1
while(i <= 10){#while loop循环
print(i)
i=i+1
}

0312

if条件和switch条件

i=1
if(i==1){
print("hello world")
}else{
print("goodbye eorld")
}
i=3
if(i==1){#if的条件语句
print("hello")
}else if (i==3) {#多个条件可一直else if
print("goodbye")
}else{
print("good game")
}
feelings=c("sad","afraid")
for(i in feelings){
print(
switch(i,#swich的作用相当于else if，转换
happy="i am glad",
afraid="something to fear",
sad="cheer up",
angry="calm dowm"
)
)
}

0313

R语言中的user-defined function

myfunction=function(x){#R语言中的user defined function
for(i in feelings){
print(
switch(i,
happy="i am glad",
afraid="something to fear",
sad="cheer up",
angry="calm dowm"
)
)
}
}
myfunction=function(x,a,b,c){
return(asin(x)^2-bx+c)
}
curve(myfunction(x,20,3,4),xlim=c(1,20))#画出刚刚定义的函数图像
myfeeling=function(x){
for(i in feelings){
print(
switch(i,
happy="i am glad",
afraid="something to fear",
sad="cheer up",
angry="calm dowm"
)
)
}
}
feelings=c("sad","afraid")
myfeeling(feelings)

0314

bar plot

library(vcd)
counts <- table(Arthritis$Improved)

counts

image.png

barplot(counts,
main="Simple Bar Plot",
xlab="Improvement", ylab="Frequency")
barplot(counts,
main="Horizontal Bar Plot",
xlab="Frequency", ylab="Improvement",
horiz=TRUE)

image.png

counts <- table(Arthritis Treatment)
barplot(counts,
main="Stacked Bar Plot",
xlab="Treatment", ylab="Frequency",
col=c("red", "yellow","green"),
legend=rownames(counts),
beside = TRUE)

image.png

pie plot

install.packages("plotrix")
library(plotrix)
slices <- c(10,12,4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie(slices, labels = lbls,main="Simple Pie Chart",edges=300,radius=1)

image.png

0315

fan plot

slices <- c(10,12,4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
fan.plot(slices,labels=lbls,main = "fan plot")

image.png

dot chart

dotchart(mtcars$mpg,
labels=row.names(mtcars),cex=0.7,
main="Gas Mileage for Car Models",

xlab="Miles Per G

image.png

allon")

对数据的基本操作

head(mtcars)#展示前面六行
summary(mtcars)
attach(mtcars)
table(cyl)#统计该列数据的频数
table(cut(mpg,seq(10,34,by=2)))#统计该列数据特定区间的频数

0317

x = rnorm(100, mean = 10, sd = 1)
y = rnorm(100, mean = 30, sd = 10)
t.test(x, y, alt = "two.sided",paired=TRUE)#双边检验
set.seed(123)
A = matrix(sample(100,15), nrow=5, ncol=3)

image.png

t(A)#置换行列

image.png

A+2
A-2
A2
A/2
set.seed(234)
B = matrix(sample(100,15), nrow=5, ncol=3)
t(A)
t(A) %% B#共有区域矩阵相乘
colMeans(A)#列的平均数
colSums(A)#列的和
crossprod(A,B)#A的置换,乘以B

0318

FACTOR

factor=factor(rep(c(1:3),times=5))#对变量做标记
x=sample(100,15)
tapply(x,factor,mean)#用factor来对x标记
rbind(x,factor)
boo=rbind(x,factor)[2,]==2
which(boo)
rbind(x,factor)[1,which(boo)]
mean(rbind(x,factor)[1,which(boo)])

bilibili：AV5625356

柱状图：0319

单样品柱状图

file1="Anr.lib.stat.txt"
dat2=read.table(file=file1,check.names=F,header=T,sep="\t",comment.char = "")

对数据进行排序处理

dat2=dat2[order(dat2[,2],decreasing=T),]
head(dat2)

画图

bar1=ggplot(dat2,aes(x=Species_Name,y=Homologous_Number))+
geom_bar(stat = "identity",position = "dodge",width = 0.8)
bar1
ggsave(bar1,filename = "hello.png",width = 12,height = 9)
dat2
?read.table

修改排序

dat2[,1]=factor(dat2[,1],levels = dat2[,1],order=T)

Other最后

ending=c("Other")
level=as.character(dat2[!dat2[,1]==ending,1])
level=unique(c(level,ending))
dat2[,1]=factor(dat2[,1],levels=level,order=T)
bar1=ggplot(dat2,aes(x=Species_Name,y=Homologous_Number))+
geom_bar(stat = "identity",position = "dodge",width = 0.8)

0320

设定和映射的差别

p_bar=ggplot(dat2,aes(x=dat2[,1],y=dat2[,2],fill=dat2[,1]))+
geom_bar(stat="identity",position ="dodge",width = 0.8)+
scale_fill_brewer(palette="Paired",direction=-1)
p_bar

调用R中的颜色包

RColorBrewer::display.brewer.all()

文字标记

p_bar=p_bar+
geom_text(aes(label=paste(as.character(dat2[,3]*100),"%",sep="")),vjust = 1,size=3)
p_bar

标签文字的调整

p_bar=p_bar+labs(x="species",y="helo",title = "nothing")
p_bar
p_bar=p_bar+ggtitle(label="nothing",subtitle = "someshing")
p_bar

theme修改title,legend及背景

p_bar=p_bar+theme(
plot.title = element_text(size = 25,face = "bold", vjust = 0.5, hjust = 0.5),##title位置
axis.text.x=element_text(size = 10,face = "bold", vjust = 1, hjust = 1,angle = 45),##x轴文本位置
panel.background = element_rect(fill = "transparent",color = "black"),##表格内背景
plot.background = element_rect(fill = "lightblue",colour = "red"),##图样背景
axis.ticks.x=element_blank(),
panel.grid.minor = element_blank(), ##表格内格子
panel.grid.major = element_blank())
p_bar

321

多样品柱状图

file2="nr.lib.stat.txt"

读取数据

dat2=read.table(file2,sep="\t",check.names = F,header = T,comment.char = "")
head(dat2)
dat2=dat2[order(dat2[,3],decreasing = T),]
head(dat2)

固定顺序

level=as.character(dat2[!dat2[,1]==ending,1])
level=unique(c(level,ending))

dat2[,1]=factor(dat2[,1],levels=level,order=T)

dat2[,2]=factor(dat2[,2],order=T)

基础做图

library(ggplot2)
p_bar2=ggplot(dat2,aes(x=dat2[,1],y=dat2[,3],fill=dat2[,2]))+
geom_bar(stat="identity",width=0.7,position ="dodge",color="darkgrey")
p_bar2

p_bar2=ggplot(dat2,aes(x=dat2[,1],y=dat2[,3],fill=dat2[,2]))+
geom_bar(stat="identity",width=0.7,position =position_dodge(width=0.9),color="darkgrey")
p_bar2

标签修改，颜色修改#设置有aes(fill)生成的图例

p_bar2=p_bar2+labs(x=xlab,y=ylab)+
ggtitle(label=title,subtitle = subtitle)
p_bar2

fill="Cultivar"
p_bar2=p_bar2+labs(x=xlab,y=ylab,fill=fill)+
ggtitle(label=title,subtitle = subtitle)
p_bar2

322 多样品柱状图

file2="nr.lib.stat.txt"

读取数据

dat2=read.table(file2,sep="\t",check.names = F,header = T,comment.char = "")
head(dat2)
dat2=dat2[order(dat2[,3],decreasing = T),]
head(dat2)

固定顺序

level=as.character(dat2[!dat2[,1]==ending,1])
level=unique(c(level,ending))

dat2[,1]=factor(dat2[,1],levels=level,order=T)

dat2[,2]=factor(dat2[,2],order=T)

基础做图

library(ggplot2)
p_bar2=ggplot(dat2,aes(x=dat2[,1],y=dat2[,3],fill=dat2[,2]))+
geom_bar(stat="identity",width=0.7,position ="dodge",color="darkgrey")
p_bar2

p_bar2=ggplot(dat2,aes(x=dat2[,1],y=dat2[,3],fill=dat2[,2]))+
geom_bar(stat="identity",width=0.7,position =position_dodge(width=0.9),color="darkgrey")
p_bar2

标签修改，颜色修改#设置有aes(fill)生成的图例

p_bar2=p_bar2+labs(x=xlab,y=ylab)+
ggtitle(label=title,subtitle = subtitle)
p_bar2

fill="Cultivar"
p_bar2=p_bar2+labs(x=xlab,y=ylab,fill=fill)+
ggtitle(label=title,subtitle = subtitle)
p_bar2

控制顺序，基于建立的有序因子

p_bar2=p_bar2+scale_fill_brewer(palette="Set3",direction=1)
p_bar2
p_bar2=p_bar2+scale_fill_manual(values=c("red","turquoise"))
p_bar2

标记文字

p_bar2=p_bar2+
geom_text(aes(label=paste(as.character(dat2[,4]*100),"%",sep="")),position=position_dodge(width=0.9),vjust = -0.5,size=2)
p_bar2

细节调整

p_bar2=p_bar2+
theme(
plot.title = element_text(size = 25,face = "bold", vjust = 0.5, hjust = 0.5),
legend.title = element_text(size = 15,face = "bold", vjust = 0.5, hjust = 0.5),
legend.text = element_text(size = 10, face = "bold"),
legend.position = 'right',
legend.key.size=unit(0.5,'cm'),
axis.text.x=element_text(size = 10,face = "bold", vjust = 1, hjust = 1,angle = 45),
axis.text.y=element_text(size = 10,face = "bold", vjust = 0.5, hjust = 0.5),

axis.title.x = element_text(size = 15,face = "bold", vjust = 0.5, hjust = 0.5),
axis.title.y = element_text(size = 15,face = "bold", vjust = 0.5, hjust = 0.5),

panel.background = element_rect(fill = "transparent",colour = "black"), 
panel.grid.minor = element_blank(), 
panel.grid.major = element_blank(),
plot.background = element_rect(fill = "transparent",colour = "black"))

p_bar2

堆叠柱状图

p_barS=ggplot(dat2,aes(x=dat2[,1],y=dat2[,3],fill=dat2[,2]))+
geom_bar(stat="identity",width=0.8,position ="stack")
p_barS

反向填充

p_barS=ggplot(dat2,aes(x=dat2[,1],y=dat2[,3],fill=dat2[,2]))+
geom_bar(stat="identity",width=0.8,position =position_stack(reverse = T))
p_barS

文字标签，图例反向等

p_barS=ggplot(dat2,aes(x=dat2[,1],y=dat2[,3],fill=dat2[,2]))+
geom_bar(stat="identity",width=0.8,position =position_stack(reverse = T))+
labs(x=xlab,y=ylab,fill=fill)+
ggtitle(label=title,subtitle = subtitle)+
scale_fill_manual(values=c("red","turquoise"))+
guides(fill=guide_legend(reverse=F))
p_barS
p_barS=p_barS+
geom_text(aes(label=paste(as.character(dat2[,4]*100),"%",sep="")),position=position_stack(vjust = 0.5,reverse = T),size=3)
p_barS

323 基础饼状图

读取数据

file1="Anr.lib.stat.txt"
dat1=read.table(file=file1,check.names=F,header=T,sep="\t",comment.char = "")
head(dat1,3)
dat1=dat1[order(dat1[,2],decreasing = T),]
head(dat1)

排序处理

ending="Other"
fill="Species"
title="Nr"
subtitle="Homologous_Number"
level=as.character(dat1[!dat1[,1]==ending,1])
level=unique(c(level,ending))
dat1[,1]=factor(dat1[,1],levels=level,order=T)

柱状垛叠

p_pie=ggplot(dat1,aes(x="",y=dat1[,2],fill=dat1[,1]))+
geom_bar(stat="identity",width=1,position = position_stack(reverse = T))
p_pie

设置y轴极坐标，方向

p_pie=p_pie+
coord_polar(theta="y",direction=-1)
p_pie

颜色填充设置

p_pie=p_pie+
scale_fill_brewer(palette ="Set3",direction = 1)
p_pie

图例标题修改

p_pie=p_pie+
labs(x="",y="",fill=fill)+
ggtitle(label =title,subtitle=subtitle)
p_pie

文字标签

p_pie=p_pie+geom_text(aes(label=paste(as.character(dat1[,3]*100),"%",sep="")),position =position_stack(vjust=0.5,reverse = T),size=3)
p_pie

刻度背景调整

p_pie=p_pie+
theme(
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.title = element_text(hjust = 0.5),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
panel.background = element_rect(fill = "transparent",colour = NA),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
plot.background = element_rect(fill = "transparent",colour = NA)
)
p_pie

数据分布——密度图，箱线图，直方图

325

读取数据

file="gene_fpkm.xls"
demo_fpkm=read.table(file,header = F,sep = "\t",check.names = F)

对数据进行处理

fpkm=melt(demo_fpkm,variable.name = "Sample",value.name = "FPKM")
head(fpkm,10)

画密度图

p_density=ggplot(fpkm,aes(x=log(fpkm[,3]),color=fpkm[,2],fill=fpkm[,2]))+
geom_density(alpha=0.25,size=0.5)
p_density

修改标度

p_density=p_density+xlim(-3,5)
p_density

标题主题修改

p_density=p_density+
ggtitle("Gene expression density")+
labs(x="log10FPKM",color="Samples",fill="Samples")+
theme_bw()
p_density

箱线图

p_box=ggplot(fpkm,aes(x=fpkm[,2],y=log10(fpkm[,3]),fill=fpkm[,2]))+
geom_boxplot(size=0.5,width=0.8,notch=T,outlier.shape = NA)
p_box

y轴范围限制

p_box=p_box+ylim(-3,5)
p_box

标题主题修改

p_box=p_box+
ggtitle("Gene expression distribution")+
labs(y="log10FPKM",x="",fill="Samples")+
theme_bw()
p_box

直方图

326

画点样品直方图

p_histogram=ggplot(fpkm,aes(x=log10(fpkm[,3]),fill=fpkm[,2]))+
geom_histogram(binwidth = 1,alpha=0.5,size=1,stat="bin")+
xlim(-3,5)
p_histogram

多样品直方图

默认stack模式

p_histogram=ggplot(fpkm,aes(x=log10(fpkm[,3]),fill=fpkm[,2]))+
geom_histogram(binwidth = 1,alpha=0.5,size=1,stat="bin")+
xlim(-3,5)
p_histogram

分区

p_histogram=p_histogram+
facet_grid(.~fpkm[,2],scales = "free")
p_histogram

频率型直方图

p_histogram=ggplot(fpkm,aes(x=log10(fpkm[,3]),y=..density..))+
geom_histogram(aes(fill=fpkm[,2]),binwidth = 1,alpha=0.5,size=1,stat="bin")+
xlim(-3,5)
p_histogram=p_histogram+
facet_grid(.~fpkm[,2],scales = "free")
p_histogram

加上密度曲线

p_freqpoly=ggplot(fpkm,aes(x=log10(fpkm[,3]),color=fpkm[,2]))+
geom_freqpoly(binwidth = 1,alpha=0.5,size=1,stat="bin")+
xlim(-3,5)
p_freqpoly

频数折线图

p_h_f=p_histogram+
geom_freqpoly(aes(color=fpkm[,2]),binwidth = 1,alpha=0.5,size=1,stat="bin")
p_h_f

327

读取数据

file="CK-WT_vs_T-WT.xls"
demo_DEG=read.table(file,check.names = F,header = T,sep = "\t")
head(demo_DEG)

设置阈值信息

line_FC=2
line_FDR=0.01
col=c("red","blue","grey")
AFPKM=c(2:4)
BFPKM=c(5:7)

通过阈值上下调信息

demo_DEG[demo_DEG[,"FDR"] = log2(line_FC),ncol(demo_DEG)+1]="Up"
demo_DEG[demo_DEG[,"FDR"] = demo_DEG[,"log2FC"],ncol(demo_DEG)]="Down"
demo_DEG[demo_DEG[,"FDR"] >=line_FDR | log2(line_FC) > abs(demo_DEG[,"log2FC"]),ncol(demo_DEG)]="Normal"
colnames(demo_DEG)[ncol(demo_DEG)]="Regulate"
head(demo_DEG)

火山图

volcano=demo_DEG

有序因子的创建以便于颜色修改

volcanoRegulate,levels = c("Up","Down","Normal"),order=T)

初步画火山图

p_volcano=ggplot(volcano,aes(x=log2FC,y=-log10(FDR)))+
geom_point(aes(color=Regulate),alpha=0.5)
p_volcano

颜色的改变

p_volcano=p_volcano + scale_color_manual(values =col)
p_volcano

在基础图上加上阈值线

p_volcano=p_volcano +
geom_hline(yintercept=c(-log10(line_FDR)),linetype=4)+
geom_vline(xintercept=c(-log2(line_FC),log2(line_FC)),linetype=4)
p_volcano

主题修改

p_volcano=p_volcano+theme_bw()
p_volcano

保存

ggsave(p_volcano,filename = "six.png")

six.png

MA图

328

读取数据

head(demo_DEG,10)

设置xy轴名称

MA=demo_DEG[,c("ID","log2FC","Regulate")]
head(MA,2)
MA[,4]=1/2log2(rowMeans(demo_DEG[,AFPKM])rowMeans(demo_DEG[,BFPKM]))
colnames(MA)[4]="1/2log2FPKM"
head(MA,3)

有序因子设置

MARegulate,levels = c("Up","Down","Normal"),order=T)

MA图绘制

p_MA=ggplot(MA,aes(x=1/2log2FPKM,y=log2FC))+
geom_point(aes(color=Regulate),alpha=0.5)
p_MA

设置x轴范围

p_MA=p_MA+scale_x_continuous(limits = c(-5,15))
p_MA

颜色，阈值

加主题
p_MA=p_MA+
scale_color_manual(values =c("red","darkgreen", "darkgrey"))+
geom_hline(yintercept=c(-log2(line_FC),log2(line_FC)),linetype=4)+
theme_bw()
p_MA

保存

ggsave(p_MA,filename = "six(2).png")

six(2).png

329 柱状图（富集分析结果可视化）

library(ggplot2)

读取数据

enrich="GO.enrich.txt"
demo_go=read.table(enrich,check.names = F,sep = "\t",header = T,comment.char = "")
head(demo_go)

对p值进行排序,并取P值前20小的数据

go=demo_go[order(demo_go[demo_go$Pvalue head(go)
if(nrow(go)>=20){
go=go[1:20,]
}
head(go)

对二级范围进行分别排序

go=go[order(go$Term_type,decreasing = F),]
head(go)

显著性高的排在前面

goDescription,levels = rev(go$Description),ordered = T)

筛选出P值为0的数，并转换为对数

go[go$Pvalue==0,"Pvalue"]=1e-15

画图，对x轴和y轴进行反转

go_bar=ggplot(go,aes(x=Description,y=-log10(Pvalue),fill=Term_type))+
geom_bar(stat="identity",width = 0.8)
go_bar
go_bar=go_bar+
coord_flip()
go_bar

对文字标签的修改,主题修改

go_bar=go_bar+
geom_text(aes(label=as.character(DEGs)),position = "stack",vjust=0,hjust=0,size=3)+
theme_bw()
go_bar

保存

ggsave(go_bar,filename = "seven(1).png")

seven(1).png

330

气泡图

设置阈值

enrich=0.01
minPvalue=1e-15

读取数据

demo_go=read.table(file,header = T,check.names = F,comment.char = "",sep = "\t")
head(demo_go)

对p值进行排序,并取P值前20小的数据

go=demo_go[order(demo_go[demo_go$Pvalue head(go)
if(nrow(go)>=20){
go=go[1:20,]
}
head(go)

筛选出P值为0的数，并转换为对数

go[go$Pvalue==0,"Pvalue"]=1e-15

画气泡图

go_point=ggplot(go,aes(x=Description,y=Rich_factor))+
geom_point(aes(color=-log10(Pvalue),size=DEGs),alpha=0.8)+coord_flip()
go_point

对颜色进行修改(渐变)

go_point=go_point+
scale_color_gradient(low = "green",high = "red")
go_point

保存

ggsave(go_point,filename = "seven(2).png")

seven(2).png

331 用pheatmap来绘制

install.packages("pheatmap")
library(pheatmap)

读取数据

file="All.DEG_final_3000.xls"
mat=read.table(file,check.names = F,header = T,sep = "\t",comment.char = "")
head(mat,3)
dim(mat)

pheatmap简单画图

pheatmap(mat)
options(stringsAsFactors = TRUE)

对基因结果标准化（行）

pheatmap(mat,scale = "row")

隐藏行名

pheatmap(mat,scale="row",show_rownames=F)

改变颜色

library(RColorBrewer)
RColorBrewer::display.brewer.all()
col=c("blue","white","red")
color=colorRampPalette(col)(100)
pheatmap(mat2,scale="row",show_rownames=F,color=color)

单元格大小cell

cellheight=300/nrow(mat)
cellwidth=300/ncol(mat)
cellwidth=10
cellheight=10
pheatmap(mat,scale="row",show_rownames=F,color=color,cellwidth = cellwidth,cellheight = cellheight,border_color="black")

image.png

424

给热图添加注释

col_file="annotation_col1.xls"
annotation_col=read.table(col_file,header = T,row.names=1,sep="\t",check.names = F,comment.char = "")
annotation_col
pheatmap(mat,scale="row",show_rownames=F,annotation_col = annotation_col)
pheatmap(mat,scale="row",show_rownames=F,annotation_col = annotation_col,annotation_colors = ann_colors)

离散分类取色

brewer.pal.info
qual=rownames(brewer.pal.info[brewer.pal.info[,"category"]=="qual",])
qualColor=c()
for(i in qual){
qualColor=c(qualColor,brewer.pal(brewer.pal.info[i,"maxcolors"], i))
}
length(qualColor)
length(unique(qualColor))
qualColor=unique(qualColor)
qualColor

seq为数值分类

seqColor=list(Blues=c("#F7FBFF","#08306B"),Reds=c("#FFF5F0","#67000D"),
Greys=c("#FFFFFF","#000000"))
seqColor

设置注释颜色

annotation_color=list()

类型

char=1
num=1
for(i in colnames(annotation_col)){

if(is.numeric(annotation_col[,i])){
annotation_color[[i]]=seqColor[[num]]
num=num+1

}else{
n=length(table(annotation_col[,i]))
annotation_color[[i]]=qualColor[char:(char+n-1)]
names(annotation_color[[i]])=names(table(annotation_col[,i]))
char=char+n
}

}

查看

annotation_color

画图

pdf(file=paste(workdir,"/gene_heatmap.pdf",sep=""),width = 9,height = 9)
heatmap=pheatmap(mat,color=color,cellwidth = cellwidth,cellheight = cellheight,scale="row",
annotation_col = annotation_col,annotation_colors = annotation_color,
show_rownames=F,fontsize_col=8,fontsize=7)
dev.off()

TIM图片20200424173038.jpg

行重排顺序

newOrder=mat[heatmaporder,]

添加cluster

cluster=10
row_cluster=cutree(heatmap$tree_row,k=cluster)
newOrder[,ncol(newOrder)+1]=row_cluster[match(rownames(newOrder),names(row_cluster))]
colnames(newOrder)[ncol(newOrder)]="Cluster"
head(newOrder,2)
write.table(newOrder,file = paste(workdir,"/gene_newOrder_withCluster.xls",sep = ""),sep="\t",row.names = T,col.names = T,quote = F)

R

0228

0229

0330

0302

一级标题

二级标题

三级标题

代码引用

图片引用

0303

1.putty安装

2.在putty上操作Linux系统

思维导图： TIM图片20200303211602.png

0304

下载miniconda

运行miniconda

备注：

0305

R语言的基础

1.安装R和Rstudio

2.用R语言进行一些画图

3.R语言的基本操作

0306

R语言数据结构

赋值

取值

读取本地数据和对数据的操作

问题

0307

0309

array

dataframe

0311

对图形的设置

for和while的循环语句

0312

if条件和switch条件

0313

R语言中的user-defined function

0314

bar plot

pie plot

0315

fan plot

dot chart

对数据的基本操作

0317

0318

FACTOR

bilibili：AV5625356

柱状图：0319

单样品柱状图

对数据进行排序处理

画图

修改排序

Other最后

0320

设定和映射的差别

调用R中的颜色包

文字标记

标签文字的调整

theme修改title,legend及背景

321

多样品柱状图

读取数据

相关标签设置

固定顺序

基础做图

标签修改，颜色修改#设置有aes(fill)生成的图例

322

多样品柱状图

读取数据

相关标签设置

固定顺序

基础做图

标签修改，颜色修改#设置有aes(fill)生成的图例

控制顺序，基于建立的有序因子

标记文字

细节调整

思维导图：

TIM图片20200303211602.png