tutorial_complex_Heatmap.Rmd

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

```{r}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("DESeq2")
```

```{r}
library(DESeq2)
library(ggplot2)
```

```{bash}
ls
```


```{r}
Counts <- read.delim("count_table.csv", header = TRUE, row.names = 1, sep = ",")
```

```{r}
Counts
```

```{r}
Counts <- Counts[which(rowSums(Counts) > 0),]
```


```{r}
Counts
```

```{r}
condition <- factor(c("C","C","C","C", "S","S","S","S"))
```

```{r}
coldata <- data.frame(row.names = colnames(Counts), condition)
```

```{r}
coldata
```

```{r}
dds <- DESeqDataSetFromMatrix(countData = Counts, colData = coldata, design = ~condition)
```

```{r}
dds <- DESeq(dds)
```

```{r}
vsdata <- vst(dds, blind=FALSE)
```

```{r}
plotPCA(vsdata, intgroup = "condition")
```

```{r}
plotDispEsts(dds)
```

```{r}
res <- results(dds, contrast = c("condition", "S", "C"))
```

```{r}
res
```


```{r}
sigs <- na.omit(res)
```

```{r}
sigs <- sigs[sigs$padj < 0.05,]
```

```{r}
sigs
```

```{r}
write.csv(sigs, file = "deseq_results.csv")
```


```{bash}
ls
```

```{r}
sigs
```

```{r}
df <- as.data.frame(sigs)
df
```



```{r}
ensembl_map <- read.csv('ensemble_key_mapper.csv', header = FALSE)


keys <- ensembl_map$V1
values <- ensembl_map$V2

l <- list()
for (i in 1:length(keys)){
  l[keys[i]] <- values[i]
}


```





```{r}
#for non-mapped labels
no_values <- setdiff(rownames(df), keys)
for (i in 1:length(no_values)){
  l[no_values[i]] <- 'NA'
}
```


```{r}
df$symbol <- unlist(l[rownames(df)], use.names = FALSE)
```



```{r}
df.top <- df[ (df$baseMean > 50) & (abs(df$log2FoldChange) > 2),]
df.top
```

```{r}
df.top <- df.top[order(df.top$log2FoldChange, decreasing = TRUE),]
```


```{r}
rlog_out <- rlog(dds, blind=FALSE) #get normalized count data from dds object
mat<-assay(rlog_out)[rownames(df.top), rownames(coldata)] #sig genes x samples
colnames(mat) <- rownames(coldata)
base_mean <- rowMeans(mat)
mat.scaled <- t(apply(mat, 1, scale)) #center and scale each column (Z-score) then transpose
colnames(mat.scaled)<-colnames(mat)
```


```{r}
num_keep <- 25
#1 to num_keep len-num_keep to len
rows_keep <- c(seq(1:num_keep), seq((nrow(mat.scaled)-num_keep), nrow(mat.scaled)) )
```



```{r}
l2_val <- as.matrix(df.top[rows_keep,]$log2FoldChange) #getting log2 value for each gene we are keeping
colnames(l2_val)<-"logFC"

mean <- as.matrix(df.top[rows_keep,]$baseMean) #getting mean value for each gene we are keeping
colnames(mean)<-"AveExpr"
```





```{r}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("ComplexHeatmap")
```



```{r}
library(ComplexHeatmap)
library(RColorBrewer)
library(circlize)
```



```{r}
#maps values between b/w/r for min and max l2 values
col_logFC <- colorRamp2(c(min(l2_val),0, max(l2_val)), c("blue", "white", "red")) 

#maps between 0% quantile, and 75% quantile of mean values --- 0, 25, 50, 75, 100
col_AveExpr <- colorRamp2(c(quantile(mean)[1], quantile(mean)[4]), c("white", "red"))
```



```{r}

ha <- HeatmapAnnotation(summary = anno_summary(gp = gpar(fill = 2), 
                                               height = unit(2, "cm")))

h1 <- Heatmap(mat.scaled[rows_keep,], cluster_rows = F, 
            column_labels = colnames(mat.scaled), name="Z-score",
            cluster_columns = T)
h2 <- Heatmap(l2_val, row_labels = df.top$symbol[rows_keep], 
            cluster_rows = F, name="logFC", top_annotation = ha, col = col_logFC,
            cell_fun = function(j, i, x, y, w, h, col) { # add text to each grid
              grid.text(round(l2_val[i, j],2), x, y)
            })
h3 <- Heatmap(mean, row_labels = df.top$symbol[rows_keep], 
            cluster_rows = F, name = "AveExpr", col=col_AveExpr,
            cell_fun = function(j, i, x, y, w, h, col) { # add text to each grid
              grid.text(round(mean[i, j],2), x, y)
            })

h<-h1+h2+h3
h
```


```{r}
png("./heatmap_v1.png", res = 300, width = 3000, height = 5500)
print(h)
dev.off()
```