RNA-seq_Hierarchical_Clustering

---
title: "R Notebook"
output: html_notebook
---

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```{r}
library(ballgown)
library(ggplot2)
library(genefilter)
library(dplyr)
library(DESeq2)
setwd("G:/BorrisProject/ballgown")

pheno_data=read.csv('borris_project_phenodata.csv')

bg=ballgown(dataDir="G:/BorrisProject/BAM/useonlyref/whole",samplePattern="P",pData=pheno_data)
bg_filt=subset(bg,'rowVars(texpr(bg))>1')

bg_table=texpr(bg_filt,'all')
bg_gene_names=unique(bg_table[,9:10])
bg_table=gexpr(bg_filt)
```
```{r}
library(pheatmap)
pdf('cluster.pdf')
pheatmap(as.matrix(bg_table),scale='row',clustering_distance_rows='correlation',clustering_method = 'complete', main='genes')
dev.off()
```
```{r}
pca_data=prcomp(t(bg_table))
pca_data_perc=round(100*pca_data$sdev^2/sum(pca_data$sdev^2),1)
df_pca_data=data.frame(PC1=pca_data$x[,1],PC2=pca_data$x[,2],sample=rep(c("1","2","3","4","5","6"),each=2))
```
```{r}
ggplot(df_pca_data,aes(PC1,PC2,color=sample))+geom_point()
```

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