supplementary.Rmd

---
title: "Supplementary Material"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Supplementary}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r message=FALSE, warning=FALSE}
# load libraries
library(tibble)
library(dplyr)
library(tidyr)
library(readr)
library(stringr)
library(ggplot2)
library(tidyHeatmap)
library(tidybulk)
library(ggrepel)
library(airway)
```

## How to start from tables

```{r}
# create some example tables to use
data(airway)

# counts table
counts <- assay(airway) %>%
  as_tibble(rownames = "geneID")

# sample information table
sampleinfo <- colData(airway) %>%
  as_tibble(rownames = "sample")

# data preprocessing

counts_tt <- 
	# convert to tidy format
	pivot_longer(counts, cols = starts_with("SRR"), names_to = "sample", values_to = "counts") %>%

	# get gene symbols
	ensembl_to_symbol(geneID) %>%

	# order the columns for tidybulk
	select(sample, geneID, counts, transcript) %>%

	# add the sample info
	left_join(sampleinfo) %>%

	# shorten sample name
	mutate(sample=str_remove(sample, "SRR1039")) %>%

	# convert to tidybulk tibble
	tidybulk(.sample=sample, .transcript=geneID, .abundance=counts)
```

## How to count reads per sample

```{r}
counts_tt %>% 
	group_by(sample) %>% 
	summarise(total_reads=sum(counts))
```

We can also check how many counts we have for each sample by making a bar plot. This helps us see whether there are any major discrepancies between the samples more easily.

```{r out.width = "70%"}
ggplot(counts_tt, aes(x=sample, weight=counts, fill=sample)) + 
	geom_bar() +
	theme_bw()
```

As we are using ggplot2, we can also easily view by any other variable that's a column in our dataset, such as cell line, simply by changing `fill`.

We can colour by dex treatment.

```{r out.width = "70%"}
ggplot(counts_tt, aes(x=sample, weight=counts, fill=dex)) + 
	geom_bar() +
	theme_bw()
```
We can colour by cell line.

```{r out.width = "70%"}
ggplot(counts_tt, aes(x=sample, weight=counts, fill=cell)) + 
	geom_bar() +
	theme_bw()
```


## How to examine normalised counts with boxplots

```{r out.width = "70%"}
# scale counts
counts_scaled <- counts_tt %>% scale_abundance(factor_of_interest = dex)

# create box plots
counts_scaled %>%
	filter(!lowly_abundant) %>%
	pivot_longer(cols = c("counts", "counts_scaled"), names_to = "source", values_to = "abundance") %>%
	ggplot(aes(x=sample, y=abundance + 1, fill=dex)) +
	geom_boxplot() +
	geom_hline(aes(yintercept = median(abundance + 1)), colour="red") +
	facet_wrap(~source) +
	scale_y_log10() +
	theme_bw()
```

## How to create MDS plot

```{r out.width = "70%"}
airway %>%
	tidybulk() %>%
	scale_abundance(factor_of_interest=dex) %>%
	reduce_dimensions(method="MDS", scale = F) %>%
	pivot_sample() %>%
	ggplot(aes(Dim1, Dim2, color = dex)) +
	geom_point()
```


## How to create MA plot

MA plots enable us to visualise amount of expression (logCPM) versus logFC. Highly expressed genes are towards the right of the plot. We can also colour significant genes (e.g. genes with FDR < 0.05) 

```{r out.width = "70%"}
# perform differential testing
counts_de <- 
	counts_tt %>%
	test_differential_abundance(
		.formula = ~ 0 + dex + cell,
		.contrasts = c("dextrt - dexuntrt"),
		omit_contrast_in_colnames = TRUE
	)

# maplot, minimal
counts_de %>%
	pivot_transcript() %>%
	filter(!lowly_abundant) %>%
	ggplot(aes(x=logCPM, y=-logFC, colour=significant)) +
	geom_point()+
	theme_bw()
```

A more informative MA plot, integrating some of the packages in tidyverse.

```{r out.width = "70%", warning=FALSE}
counts_de %>%
	pivot_transcript() %>%
	
	# Subset data
	filter(!lowly_abundant) %>%
	mutate(significant = FDR<0.05 & abs(logFC) >=2) %>%
	mutate(transcript = ifelse(abs(logFC) >=5, as.character(transcript), "")) %>%
	
	# Plot
	ggplot(aes(x = logCPM, y = logFC, label=transcript)) +
	geom_point(aes(color = significant, size = significant, alpha=significant)) +
	geom_text_repel() +
	scale_color_manual(values=c("black", "#e11f28")) +
	scale_size_discrete(range = c(0, 2)) +
	theme_bw()
```


## How to perform gene enrichment analysis

To run below you'll need the `clusterProfiler` and `org.Hs.eg.db` packages. This is just one suggestion, adapted from [here](https://simon-anders.github.io/data_analysis_course/lecture9.html). If you have other suggestions for how to do a 'tidy' pathway analysis feel free to [let us know](https://github.com/stemangiola/bioc_2020_tidytranscriptomics/blob/master/CONTRIBUTING.md).

```{r eval=FALSE}
library(clusterProfiler)
library(org.Hs.eg.db)

# extract all genes tested for DE
res <- counts_de %>% 
    pivot_transcript() %>% 
    filter(!lowly_abundant)

# GO terms
egoCC <- res %>%
    filter(FDR < 0.1 & logFC > 0 ) %>%
    pull( "transcript" ) %>%
    enrichGO(
      OrgDb = org.Hs.eg.db,
      keyType = 'SYMBOL',
      ont = "BP",
      universe = (res %>% pull( "transcript" ) ) )

dotplot(egoCC)
goplot(egoCC)
emapplot(egoCC)


# MSigDB Hallmark
gmtH <- read.gmt( "https://data.broadinstitute.org/gsea-msigdb/msigdb/release/6.2/h.all.v6.2.symbols.gmt" )
enrH <- enricher(
   gene = ( res %>% filter(FDR < 0.1 & logFC > 0) %>%
    pull( "transcript" ) ),
   TERM2GENE = gmtH,
   universe = ( res %>% pull( "transcript" ) ) )

dotplot( enrH )
emapplot(enrH)
```