---
title: 'Aurora DESeq2 Report'
output:
    html_document:
      highlight: pygments
---

```{r setup, include=FALSE, warning=FALSE, message=FALSE}
knitr::opts_chunk$set(error = TRUE, echo = FALSE)
```

```{css, echo=FALSE}
pre code, pre, code {
  white-space: pre !important;
  overflow-x: scroll !important;
  word-break: keep-all !important;
  word-wrap: initial !important;
}
```

```{r, echo=FALSE}
# to make the css theme to work, <link></link> tags cannot be added directly 
# as <script></script> tags as below.
# it has to be added using a code chunk with the htmltool functions!!!
css_link = tags$link()
css_link$attribs = list(rel="stylesheet", href="vakata-jstree-3.3.5/dist/themes/default/style.min.css")
css_link
```

```{r, eval=FALSE, echo=FALSE}
# this code chunk is purely for adding comments
# below is to add jQuery and jstree javascripts
```

<script src="vakata-jstree-3.3.5/dist/jstree.min.js"></script>


```{r, eval=FALSE, echo=FALSE}
# this code chunk is purely for adding comments
# javascript code below is to build the file tree interface
# see this for how to implement opening hyperlink: https://stackoverflow.com/questions/18611317/how-to-get-i-get-leaf-nodes-in-jstree-to-open-their-hyperlink-when-clicked-when
```
<script>
  jQuery(function () {
    // create an instance when the DOM is ready
    jQuery('#jstree').jstree().bind("select_node.jstree", function (e, data) {
     window.open( data.node.a_attr.href, data.node.a_attr.target )
    });
  });
</script>


```{r, eval=FALSE, echo=FALSE}
---
# ADD YOUR DATA ANALYSIS CODE AND MARKUP TEXT BELOW TO EXTEND THIS R MARKDOWN FILE
---
```

## DESeq2 analysis

```{r echo=FALSE}
# import count data
count_data = read.csv(opt$X_A, row.names = 1, header = TRUE)
# import column data
coldata = read.csv(opt$X_B, row.names = 1, header = TRUE)[colnames(count_data),,drop=FALSE]
```

```{r}
f = gsub('~', '~ 1 +', opt$X_C) # build formula
dds = DESeqDataSetFromMatrix(countData = count_data,
                             colData = coldata,
                             design = formula(f))


# prefiltering
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]

# Run DESeq
if (opt$X_T == 'LRT') {
  reduced_f = gsub(paste0('\\+\\s*', opt$X_D), '', f)
  dds =  DESeq(dds, test=opt$X_T, fitType = opt$X_H, reduced = formula(reduced_f))
} else {
  dds = DESeq(dds, test=opt$X_T, fitType = opt$X_H)
}

## Differential expression test results
res = results(dds, contrast = c(opt$X_D, opt$X_E, opt$X_F), alpha = opt$X_I)
DT::datatable(as.data.frame(res))
```


```{r}
# save all padj sorted res to tool output directory
padj_sorted_res = res[order(res$padj), ]
write.table(padj_sorted_res,
            file = paste0(opt$X_d, '/padj-sorted-genes.txt'),
            quote = FALSE)

# save significant genes to a file in tool output directory
sig_res = res[(res$padj < opt$X_I) & !is.na(res$padj), ]
sig_res_sorted = sig_res[order(sig_res$padj), ]
sig_res_sorted$feature_id = rownames(sig_res_sorted)
n_col = ncol(sig_res_sorted)
sig_res_sorted = sig_res_sorted[, c(n_col, 1:(n_col - 1))]
write.table(sig_res_sorted, 
            file = paste0(opt$X_d, '/padj-sorted-significant-genes.txt'), 
            quote = FALSE, row.names = FALSE)
```

## MA-plot

```{r warning=FALSE}
log_fold_change = res$log2FoldChange
base_mean = res$baseMean
significant = res$padj
significant[significant < 0.1] = 'yes'
significant[significant != 'yes'] = 'no'

maplot_df = data.frame(log_fold_change, base_mean, significant)
maplot_df = maplot_df[!is.na(maplot_df$significant), ]
p = ggplot(data = maplot_df) +
  geom_point(mapping = aes(log(base_mean), log_fold_change, color = significant),
             size = 0.5) +
  scale_color_manual(name = 'Significant',
                     values = c('no' = 'black', 'yes' = 'red'),
                     labels = c('No', 'Yes')) +
  xlab('Log base mean') +
  ylab('Log fold change') +
  theme_classic()

plotly::ggplotly(p)
```


## Heatmap of count matrix

```{r}
ntd <- normTransform(dds)
select <- order(rowMeans(counts(dds,normalized=TRUE)),
                decreasing=TRUE)[1:20]
df <- as.data.frame(colData(dds)[, -ncol(colData(dds))])
pheatmap(assay(ntd)[select,], annotation_col=df)
```


## Principle component analysis plot

```{r}
vsd <- vst(dds, blind=FALSE)
p = plotPCA(vsd, intgroup=c(opt$X_D)) + 
  scale_color_discrete(name = 'Group') +
  theme_classic()
ggplotly(p)
```