R 记录

library(pheatmap)

data<- read.table("new 1.txt",header = T, row.names = 1,quote = "")

data1 <- log10(data+1)/max(log10(data+1)) 数据标准化

pheatmap::pheatmap(data,cluster_rows = FALSE)

data<- read.table("new 3.txt",header = T, row.names = 1,quote = "")

pheatmap::pheatmap(data,cluster_rows = FALSE)

`data1<- read.table("6",header = T, row.names = 1,quote = "")

max(data1)

pheatmap::pheatmap(data1)

data1<- read.table("10",header = T, row.names = 1,quote = "")

max(data1)

pheatmap::pheatmap(data1)

data1<- read.table("12.txt",header = T, row.names = 1)

da(data1)

? read.table

data1<- read.table("12.txt",header = T, row.names = 1)

pheatmap::pheatmap(data1)

data<- read.table("5",header = T, row.names = 1,quote = "")

pheatmap::pheatmap(data,show_rownames=FALSE)

dr<- dist(as.matrix(t(data3)),method = "euclidean", diag = T, upper = T)

write.table(as.matrix(dr))

p1 <- pheatmap(data3, main = "heatmap name",

              show_rownames=F,  cluster_rows=T, cluster_cols=T,

              clustering_method = "complete",

              clustering_distance_cols = "euclidean",

              clustering_distance_rows = "euclidean",

              fontsize = 16, fontsize_col = 16, cellwidth = 24,

              cellheight = 2)

setwd("G:/jiaoji")  ##set work path

r <- read.table("5",header=T)  ##read table of expression data, have table header

row.names(r) <- r$NAME  ##set rowname

r1 <- r[,-1]  ##delete the first column

r0<-data.matrix(r1)  ##convert data frame[size=15px] to numeric matrix[/size]

ra <- scale(r0,center = T, scale = T)  ##scaling and centering for per column data, normalization?

library(proxy)          ##upload the proxy package for simil function

dr <- dist(as.matrix(t(ra)), method = "euclidean", diag = T, upper = T)        ##[size=13px]calculate the euclidean distance of columns, export all data [/size]

write.table(as.matrix(dr),"test.ed.txt")    ##export ED matrix data to test.ed.txt file

sr <- simil(as.matrix(t(ra)), method = "correlation", diag = T, upper = T)  ##[size=13px]calculate the correlation coefficient of columns, export all data [/size]

write.table(as.matrix(sr),"test.cc.txt")    ##export [size=13px]correlation coefficient[/size] matrix data to test.ed.txt file

library(pheatmap)

breaks1 <- seq(-10, 10, by = 0.2)  ##sets the minimum (0), the maximum (15), and the increasing steps (+1) for the color scale

breaks2 <- seq(-10,10,length=100)

bk3 = unique(c(seq(-2,0.98, length=50), seq(0.98,1, 50), seq(1, 4, length=50)))

colors = colorRampPalette(rev(c("#D73027", "#FC8D59", "#FEE090", "#FFFFBF", "#E0F3F8", "#91BFDB", "#4575B4")))(length(breaks1))

p1 <- pheatmap(ra, main = "heatmap name", show_rownames=F,  cluster_rows=T, cluster_cols=T, clustering_method = "complete", clustering_distance_cols = "euclidean", clustering_distance_rows = "euclidean", fontsize = 16, fontsize_col = 16, cellwidth = 24, cellheight = 2, breaks = breaks1, color = colors)                                            ##use scale data for drawing heatmap

p2 <- pheatmap(r0, main = "heatmap name", show_rownames=F, scale = "column", cluster_rows=T, cluster_cols=T, clustering_method = "complete", clustering_distance_cols = "euclidean", clustering_distance_rows = "euclidean", fontsize = 16, fontsize_col = 16, cellwidth = 24, cellheight = 2, breaks = breaks1, color = colors)                  ##pheatmap can scale the data and don't need scale data first, the darwing picture

ra <- scale(data3,center = T, scale = T)

pheatmap::pheatmap(ra)

https://www.google.com/search?ei=lZRqWsjoD8ml8AXn8oLIDQ&q=+log++%E5%BD%92%E4%B8%80%E5%8C%96+&oq=+log++%E5%BD%92%E4%B8%80%E5%8C%96+&gs_l=psy-ab.3..0i30k1.64983.77069.0.77263.13.13.0.0.0.0.145.1335.7j6.13.0....0...1c.1.64.psy-ab..0.1.97....0.6TGjuSOsw3A

cluster_cols = TRUE cutree_cols = NA annotation_col = NA

library(DESeq2)  #加载包

setwd("G:/Auxin")

library(DESeq2)

countData <- read.table("7",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","AACC_1"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.1 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_1.cvs",row.names = F)

countData <- read.table("22",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","AACC_2"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.1 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_2.cvs",row.names = F)

countData <- read.table("33",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","AACC_3"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.1 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_3.cvs",row.names = F)

countData <- read.table("66",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","AACC_6"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.1 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_6.cvs",row.names = F)

countData <- read.table("77",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","AACC_7"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.1 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_7.cvs",row.names = F)

countData <- read.table("12345.csv",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_1","AACC_1","AACC_1"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_1.cvs",row.names = F)

countData <- read.table("22",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_2","AACC_2","AACC_2"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_2.cvs",row.names = F)

countData <- read.table("33.csv",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_3","AACC_3","AACC_3"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_3.cvs",row.names = F)

countData <- read.table("55.csv",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_5","AACC_5","AACC_5"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_5.cvs",row.names = F)

countData <- read.table("66.csv",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_6","AACC_6","AACC_6"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_6.cvs",row.names = F)

countData <- read.table("77",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_7","AACC_7","AACC_7"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_7.cvs",row.names = F)

countData <- read.table("88",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_8","AACC_8","AACC_8"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_8.cvs",row.names = F)

library(pheatmap)

data<- read.csv("outfile000.csv",header = T, row.names = 1,quote = "")

pheatmap::pheatmap(data,cluster_rows =FALSE,

                  cluster_cols = FALSE)


?pheatmap

setwd("G:/restance")

countData <- read.csv("AACC_1.CSV",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_1","AACC_1","AACC_1"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_1.cvs",row.names = F)

countData <- read.csv("AACC_2.CSV",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_2","AACC_2","AACC_2"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_2.cvs",row.names = F)

countData <- read.csv("AACC_3.CSV",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_3","AACC_3","AACC_3"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_3.cvs",row.names = F)

countData <- read.csv("AACC_4.CSV",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_4","AACC_4","AACC_4"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_4.cvs",row.names = F)

countData <- read.csv("AACC_5.CSV",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_5","AACC_5","AACC_5"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_5.cvs",row.names = F)

countData <- read.csv("AACC_6.CSV",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_6","AACC_6","AACC_6"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_6.cvs",row.names = F)

countData <- read.csv("AACC_7.CSV",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_7","AACC_7","AACC_7"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/Auxin/mpv_vs_AACC_7.cvs",row.names = F)

countData <- read.csv("AACC_1.CSV",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_1","AACC_1","AACC_1"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/restance/mpv_vs_AACC_1.cvs",row.names = F)

countData <- read.csv("AACC_5.CSV",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_5","AACC_5","AACC_5"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/restance/mpv_vs_AACC_5.cvs",row.names = F)

countData <- read.csv("AACC_7.CSV",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_7","AACC_7","AACC_7"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/restance/mpv_vs_AACC_7.cvs",row.names = F)

countData <- read.csv("AACC_8.CSV",header = T, row.names = 1,quote = "")

countData <- ceiling (countData)

condition <- factor(c("MPV","MPV","MPV","AACC_8","AACC_8","AACC_8"))

colData <- data.frame(row.names=colnames(countData), condition)

dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )

dds2 <- DESeq(dds)

resultsNames(dds2)

res <- results(dds2)

table(res$padj<0.05) #取P值小于0.05的结果

res <- res[order(res$padj),]

diff_gene_deseq2 <-subset(res,padj < 0.01 & (log2FoldChange > 1 | log2FoldChange < -1))

diff_gene_deseq2 <- row.names(diff_gene_deseq2)

resdata <-  merge(as.data.frame(res),as.data.frame(counts(dds2,normalize=TRUE)),by="row.names",sort=FALSE)

write.csv(resdata,file= "G:/restance/mpv_vs_AACC_8.cvs",row.names = F)

library(pheatmap)

data<- read.csv("outfile_last.csv",header = T, row.names = 1,quote = "")

pheatmap::pheatmap(data,cluster_rows = FALSE,cluster_cols = FALSE)

setwd("G:/glugene")

library(pheatmap)

data<- read.csv("outfilelast.csv",header = T, row.names = 1,quote = "")

pheatmap::pheatmap(data,cluster_rows = FALSE,cluster_cols = FALSE)

setwd("G:/Auxin")

data<- read.csv("outfile000.csv",header = T, row.names = 1,quote = "")

pheatmap::pheatmap(data,cluster_rows = FALSE,cluster_cols = FALSE)

setwd("G:/restance")

data<- read.csv("outfile_last.csv",header = T, row.names = 1,quote = "")

pheatmap::pheatmap(data,cluster_rows = FALSE,cluster_cols = FALSE)

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