Bulk RNA-seq standard workflow

参考资源：

1.RNA-seq(4)：Hisat2+FeatureCounts+DESeq2流程+作图！

https://pzweuj.github.io/2018/07/18/rna-seq-4.html

2.一个植物转录组项目的实战

http://www.bio-info-trainee.com/2809.html

3.RNA_seq（1）植物转录组差异基因分析简单练习

https://www.jianshu.com/p/7146d5c41294

1.Volcano DE 绘图

2.PCA 绘图：

3.Heatmap:实现基因表达模式可视化的需求。 从这里可以看到这4个样本的表达差异，基本是一致的pattern，因为都是拟南芥的rna-seq 样本，no more difference.

Rscript:

library(pheatmap)

select <- order(rowMeans(counts(dds, normalized=T)), decreasing=T)[1:1000]

nt <-normTransform(dds)log2.norm.counts <- assay(nt)[select,]df <- as.data.frame(colData(dds)[, c("name", "condition")])pheatmap(log2.norm.counts, cluster_rows=T, show_rownames=F, cluster_cols=T, annotation_col=df, fontsize=6)