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---
title: 'DESeq2 analysis'
output:
    html_document:
      highlight: pygments
---

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


```{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 echo=FALSE}
dds = DESeqDataSetFromMatrix(countData = count_data,
                             colData = coldata,
                             design = formula(opt$X_C))
dds = DESeq(dds, test = opt$X_G, 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)
res
```


```{r echo=FALSE}
# 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), ]
write.table(sig_res_sorted, 
            file = paste0(opt$X_d, '/padj-sorted-significant-genes.txt'), 
            quote = FALSE)
```