minimal_workflow.Rmd

---
title: "Minimal Workflow"
author: "Mark"
date: "15/03/2022"
output:
  word_document: default
  html_document: default
---


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


## Introduction

Introduce the biological conditions being investigated...

## Data Import

Describe how the data are imported. Adjust any file paths etc as required...

```{r}
library(readr)
library(tximport)
library(DESeq2)
library(dplyr)

dirs <- list.files("salmon_quant/")

quant_files <- list.files("salmon_quant/",pattern="quant.sf.gz",recursive = TRUE,full.names = TRUE)
names(quant_files) <- dirs

tx2gene <- read_csv("tx2gene.csv",col_names = FALSE)

txi <- tximport(quant_files,
                type="salmon",
                tx2gene = tx2gene,
                ignoreTxVersion = TRUE)

sampleInfo <- read_tsv("meta_data/sampleInfo_corrected.txt")

dds <- DESeqDataSetFromTximport(txi,
                                colData = sampleInfo,
                                design = ~Treated)
```

## Quality Assessment

Check the number of reads...

```{r}

```



Verify Sample Relationships

```{r}
vsd <- vst(dds)
plotPCA(vsd, intgroup = "Treated")
```

Describe and interpret the plot

## Differential Expression

This will do the differential expression for the design chosen when you created the dds object. The results function may need to change to perform different comparisons.

```{r}
de <- DESeq(dds)
results <- results(de, tidy=TRUE)

```
## Annotation

Use the `org.Hs.eg.db` package to add extra columns to the results. If your dataset is not human, you will need to change the name of this package

```{r}
library(org.Hs.eg.db)
anno <- AnnotationDbi::select(org.Hs.eg.db, keys = rownames(dds),
                              keytype = "ENSEMBL",
                              columns = c("SYMBOL","GENENAME","ENTREZID"))
```

## Pathways analysis

Use the clusterProfiler package to identify biologically-relevant pathways


```{r}
library(clusterProfiler)
universe <- results %>% pull(row)
sigGenes <- results %>% 
  filter(padj < 0.05, !is.na(row)) %>% pull(row)

enrich_go <- enrichGO(
  gene= sigGenes,
  OrgDb = org.Hs.eg.db,
  keyType = "ENSEMBL",
  ont = "BP",
  universe = universe,
  qvalueCutoff = 0.05,
  readable=TRUE
)
```