---
title: "RNA-Seq Workflow Template" 
author: "Author: First Last"
date: "Last update: `r format(Sys.time(), '%d %B, %Y')`" 
output:
  BiocStyle::html_document:
    toc_float: true
    code_folding: show
package: systemPipeR
vignette: |
  %\VignetteIndexEntry{WF: RNA-Seq Workflow Template}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
fontsize: 14pt
bibliography: bibtex.bib
---

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

<!--
Rscript -e "rmarkdown::render('sprnaseq.Rmd', c('BiocStyle::html_document'), clean=F); knitr::knit('sprnaseq.Rmd', tangle=TRUE)"
-->

```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
options(width=60, max.print=1000)
knitr::opts_chunk$set(
    eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")),
    cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE")), 
    tidy.opts=list(width.cutoff=60), tidy=TRUE)
```

```{r setup_libraries, echo=FALSE, message=FALSE, warning=FALSE}
suppressPackageStartupMessages({
    library(systemPipeR)
    library(BiocParallel)
    library(Biostrings)
    library(Rsamtools)
    library(GenomicRanges)
    library(ggplot2)
    library(GenomicAlignments)
    library(ShortRead)
    library(ape)
    library(batchtools)
})
```

# Introduction

This report describes the analysis of the RNA-Seq data set from 
Howard et al [-@Howard2013-fq]. The corresponding FASTQ files were downloaded from
GEO (Accession: [SRP010938](http://www.ncbi.nlm.nih.gov/sra/?term=SRP010938)).
This data set contains 18 paired-end (PE) read sets from *Arabidposis thaliana*. 
The details about all download steps are provided [here](https://girke.bioinformatics.ucr.edu/GEN242/assignments/projects/project_data/). 


Users want to provide here additional background information about the design of their 
RNA-Seq project.

## Experimental design

Typically, users want to specify here all information relevant for the
analysis of their NGS study. This includes detailed descriptions of
FASTQ files, experimental design, reference genome, gene annotations,
etc.

## Workflow environment

<font color="red">NOTE: this section</font> describes how to set up the proper environment (directory structure) for running 
`systemPipeR` workflows. After mastering this task the workflow run instructions <font color="red">can be deleted</font> since they are not expected
to be included in a final HTML/PDF report of a workflow.

1. If a remote system or cluster is used, then users need to log in to the
   remote system first. The following applies to an HPC cluster (_e.g._ HPCC
   cluster). 

   A terminal application needs to be used to log in to a user's cluster account. Next, one
   can open an interactive session on a computer node with `srun`. More details about
   argument settings for `srun` are available in this [HPCC
   manual](http://hpcc.ucr.edu/manuals_linux-cluster_jobs.html#partitions) or
   the HPCC section of this website
   [here](https://girke.bioinformatics.ucr.edu/GEN242/tutorials/linux/linux/#job-submission-with-sbatch).
   Next, load the R version required for running the workflow with `module load`. Sometimes it may be necessary to
   first unload an active software version before loading another version, _e.g._ `module unload R`.

```sh
srun --x11 --partition=short --mem=8gb --cpus-per-task 4 --ntasks 1 --time 20:00:00 --pty bash -l
module unload R; module load R/4.0.3_gcc-8.3.0
```

2. Load a workflow template with the `genWorkenvir` function. This can be done from the command-line or from within R. 
   However, only one of the two options needs to be used.

From command-line

```sh
$ Rscript -e "systemPipeRdata::genWorkenvir(workflow='rnaseq')"
$ cd rnaseq
```

From R

```{r gen_workflow_envir, eval=FALSE}
library(systemPipeRdata)
genWorkenvir(workflow="rnaseq")
setwd("rnaseq")
```

3. Optional: if the user wishes to use another `Rmd` file than the template instance provided by the `genWorkenvir` function, then it can be copied or downloaded 
   into the root directory of the workflow environment (_e.g._ with `cp` or `wget`).

4. Now one can open from the root directory of the workflow the corresponding R Markdown script (_e.g._ systemPipeChIPseq.Rmd) using an R IDE, such as _nvim-r_, _ESS_ or RStudio. 
   Subsequently, the workflow can be run as outlined below. For learning purposes it is recommended to run workflows for the first time interactively. Once all workflow steps are 
   understood and possibly modified to custom needs, one can run the workflow from start to finish with a single command using `rmarkdown::render()` or `runWF()`.


## Load packages

The `systemPipeR` package needs to be loaded to perform the analysis 
steps shown in this report [@H_Backman2016-bt]. The package allows users
to run the entire analysis workflow interactively or with a single command 
while also generating the corresponding analysis report. For details
see `systemPipeR's` main [vignette](http://www.bioconductor.org/packages/devel/bioc/vignettes/systemPipeR/inst/doc/systemPipeR.html).

```{r load_systempiper, eval=TRUE, message=FALSE}
library(systemPipeR)
```

To apply workflows to custom data, the user needs to modify the _`targets`_ file and if
necessary update the corresponding parameter (_`.cwl`_ and _`.yml`_) files. 
A collection of pre-generated _`.cwl`_ and _`.yml`_ files are provided in the _`param/cwl`_ subdirectory 
of each workflow template. They are also viewable in the GitHub repository of _`systemPipeRdata`_ ([see
here](https://github.com/tgirke/systemPipeRdata/tree/master/inst/extdata/param/cwl)).
For more information of the structure of the *targets* file, please consult the documentation 
[here](http://www.bioconductor.org/packages/release/bioc/vignettes/systemPipeR/inst/doc/systemPipeR.html#25_structure_of_targets_file). More details about the new parameter files from systemPipeR can be found [here](http://www.bioconductor.org/packages/release/bioc/vignettes/systemPipeR/inst/doc/systemPipeR.html#26_structure_of_the_new_param_files_and_construct_sysargs2_container). 

## Import custom functions

Custem functions for the challenge projects can be imported with the source
command from a local R script (here [challengeProject_Fct.R](https://raw.githubusercontent.com/tgirke/GEN242/main/content/en/tutorials/spchipseq/challengeProject_Fct.R)).  Skip this step if such a
script is not available.  Alternatively, these functions can be loaded from a
custom R package. 

```{r load_custom_fct, eval=TRUE, message=FALSE}
source("challengeProject_Fct.R")
```

## Experiment definition provided by `targets` file

The `targets` file defines all FASTQ files and sample comparisons of the analysis workflow.
If needed the tab separated (TSV) version of this file can be downloaded from [here](https://github.com/tgirke/GEN242/tree/main/content/en/assignments/Projects/targets_files)
and the corresponding Google Sheet is [here](https://docs.google.com/spreadsheets/d/1DTgTGlZZscSPjlHOGdJC8QK4vvimN1BORjXKXzd_cfA/edit#gid=472150521).

```{r load_targets_file, eval=TRUE}
targetspath <- "targetsPE.txt"
targets <- read.delim(targetspath, comment.char = "#")
DT::datatable(targets, options = list(scrollX = TRUE, autoWidth = TRUE))
```

# Read preprocessing

## Read quality filtering and trimming

The function `preprocessReads` allows to apply predefined or custom
read preprocessing functions to all FASTQ files referenced in a
`SYSargs2` container, such as quality filtering or adapter trimming
routines. The paths to the resulting output FASTQ files are stored in the
`output` slot of the `SYSargs2` object. The following example performs adapter trimming with
the `trimLRPatterns` function from the `Biostrings` package.
After the trimming step a new targets file is generated (here
`targets_trim.txt`) containing the paths to the trimmed FASTQ files.
The new targets file can be used for the next workflow step with an updated
`SYSargs2` instance, _e.g._ running the NGS alignments using the
trimmed FASTQ files.

Construct _`SYSargs2`_ object from _`cwl`_ and _`yml`_ param and _`targets`_ files.

```{r construct_SYSargs2_trim-se, eval=FALSE}
dir_path <- "param/cwl/preprocessReads/trim-pe"
trim <- loadWorkflow(targets=targetspath, wf_file="trim-pe.cwl", input_file="trim-pe.yml", dir_path=dir_path)
trim <- renderWF(trim, inputvars=c(FileName1="_FASTQ_PATH1_", FileName2="_FASTQ_PATH2_", SampleName="_SampleName_"))
trim
output(trim)[1:2]
```

```{r preprocessing, eval=FALSE}
preprocessReads(args=trim, Fct="trimLRPatterns(Rpattern='GCCCGGGTAA', 
                subject=fq)", batchsize=100000, overwrite=TRUE, compress=TRUE)
writeTargetsout(x=trim, file="targets_trim.txt", step = 1, 
                new_col = c("FileName1", "FileName2"), new_col_output_index = c(1, 2), overwrite = TRUE)
```

## FASTQ quality report

The following `seeFastq` and `seeFastqPlot` functions generate and plot a series of useful 
quality statistics for a set of FASTQ files including per cycle quality box
plots, base proportions, base-level quality trends, relative k-mer
diversity, length and occurrence distribution of reads, number of reads
above quality cutoffs and mean quality distribution. The results are
written to a PDF file named `fastqReport.pdf`.

```{r fastq_report, eval=FALSE}
fqlist <- seeFastq(fastq=infile1(trim), batchsize=10000, klength=8)
png("./results/fastqReport.png", height=18*96, width=4*96*length(fqlist))
seeFastqPlot(fqlist)
dev.off()
``` 

![](./results/fastqReport.png)
<div align="center">Figure 1: FASTQ quality report for 18 samples</div></br>

# Alignments

## Read mapping with `HISAT2`

The following steps will demonstrate how to use the short read aligner `Hisat2`
[@Kim2015-ve] in both interactive job submissions and batch submissions to
queuing systems of clusters using the _`systemPipeR's`_ new CWL command-line interface.

Build `Hisat2` index.

```{r hisat_index, eval=FALSE}
dir_path <- "param/cwl/hisat2/hisat2-idx"
idx <- loadWorkflow(targets=NULL, wf_file="hisat2-index.cwl", input_file="hisat2-index.yml", dir_path=dir_path)
idx <- renderWF(idx)
idx
cmdlist(idx)

# Run 
runCommandline(idx, make_bam = FALSE)
```

The parameter settings of the aligner are defined in the `hisat2-mapping-se.cwl` 
and `hisat2-mapping-se.yml` files. The following shows how to construct the 
corresponding *SYSargs2* object, here *args*.

```{r hisat_SYSargs2_object, eval=TRUE}
dir_path <- "param/cwl/hisat2/hisat2-pe"
args <- loadWorkflow(targets="targets_trim.txt", wf_file="hisat2-mapping-pe.cwl", 
                     input_file="hisat2-mapping-pe.yml", dir_path=dir_path)
args <- renderWF(args, inputvars=c(FileName1="_FASTQ_PATH1_", FileName2="_FASTQ_PATH2_", SampleName="_SampleName_"))
args
cmdlist(args)[1:2]
output(args)[1:2]
```

### Interactive job submissions on a single machine

To simplify the short read alignment execution for the user, the command-line 
can be run with the *`runCommandline`* function.
The execution will be on a single machine without submitting to a queuing system 
of a computer cluster. This way, the input FASTQ files will be processed sequentially. 
By default *`runCommandline`* auto detects SAM file outputs and converts them 
to sorted and indexed BAM files, using internally the `Rsamtools` package.
Besides, *`runCommandline`* allows the user to create a dedicated
results folder for each workflow and a sub-folder for each sample 
defined in the *targets* file. This includes all the output and log files for each 
step. When these options are used, the output location will be updated by default 
and can be assigned to the same object.

```{r hisat_SYSargs2_singleM, eval=FALSE}
# Run single Machine
args <- runCommandline(args)
```

### Parallelization on clusters

Alternatively, the computation can be greatly accelerated by processing many files 
in parallel using several compute nodes of a cluster, where a scheduling/queuing
system is used for load balancing. For this the *`clusterRun`* function submits 
the computing requests to the scheduler using the run specifications
defined by *`runCommandline`*. 

To avoid over-subscription of CPU cores on the compute nodes, the value from 
_`yamlinput(args)['thread']`_ is passed on to the submission command, here _`ncpus`_ 
in the _`resources`_ list object. The number of independent parallel cluster 
processes is defined under the _`Njobs`_ argument. The following example will run 
18 processes in parallel using for each 4 CPU cores. If the resources available
on a cluster allow running all 18 processes at the same time then the shown sample 
submission will utilize in total 72 CPU cores. Note, *`clusterRun`* can be used
with most queueing systems as it is based on utilities from the _`batchtools`_ 
package which supports the use of template files (_`*.tmpl`_) for defining the 
run parameters of different schedulers. To run the following code, one needs to 
have both a conf file (see _`.batchtools.conf.R`_ samples [here](https://mllg.github.io/batchtools/)) 
and a template file (see _`*.tmpl`_ samples [here](https://github.com/mllg/batchtools/tree/master/inst/templates)) 
for the queueing available on a system. The following example uses the sample 
conf and template files for the Slurm scheduler provided by this package.  

```{r hisat2_clusterRun, eval=FALSE}
library(batchtools)
resources <- list(walltime=120, ntasks=1, ncpus=4, memory=1024)
reg <- clusterRun(args, FUN = runCommandline, more.args = list(args=args, make_bam=TRUE, dir=FALSE), 
                  conffile = ".batchtools.conf.R", template = "batchtools.slurm.tmpl", 
                  Njobs=18, runid="01", resourceList=resources)
getStatus(reg=reg)
waitForJobs(reg=reg)
args <- output_update(args, dir=FALSE, replace=TRUE, extension=c(".sam", ".bam")) ## Updates the output(args) to the right location in the subfolders
output(args)
```

Check whether all BAM files have been created.

```{r check_files_exist, eval=FALSE}
outpaths <- subsetWF(args , slot="output", subset=1, index=1)
file.exists(outpaths)
```

## Read and alignment stats

The following provides an overview of the number of reads in each sample
and how many of them aligned to the reference.

```{r align_stats, eval=FALSE}
read_statsDF <- alignStats(args=args) 
write.table(read_statsDF, "results/alignStats.xls", row.names=FALSE, quote=FALSE, sep="\t")
```

The following shows the alignment statistics for a sample file provided by the `systemPipeR` package. 

```{r align_stats_view, eval=TRUE}
read.table("results/alignStats.xls", header=TRUE)[1:4,]
```

## Create symbolic links for viewing BAM files in IGV

The `symLink2bam` function creates symbolic links to view the BAM alignment files in a
genome browser such as IGV. The corresponding URLs are written to a file
with a path specified under `urlfile` in the `results` directory.

```{r bam_urls, eval=FALSE}
symLink2bam(sysargs=args, htmldir=c("~/.html/", "somedir/"), 
            urlbase="http://cluster.hpcc.ucr.edu/~<username>/", 
	        urlfile="./results/IGVurl.txt")
```

# Read quantification

## Read counting with `summarizeOverlaps` in parallel mode using multiple cores

Reads overlapping with annotation ranges of interest are counted for
each sample using the `summarizeOverlaps` function [@Lawrence2013-kt]. The read counting is
preformed for exonic gene regions in a non-strand-specific manner while
ignoring overlaps among different genes. Subsequently, the expression
count values are normalized by *reads per kp per million mapped reads*
(RPKM). The raw read count table (`countDFeByg.xls`) and the corresponding 
RPKM table (`rpkmDFeByg.xls`) are written to separate files in the directory of this project. Parallelization is achieved with the `BiocParallel` package, here using 8 CPU cores.

```{r read_counting1, eval=FALSE}
library("GenomicFeatures"); library(BiocParallel)
txdb <- makeTxDbFromGFF(file="data/tair10.gff", format="gff", dataSource="TAIR", organism="Arabidopsis thaliana")
saveDb(txdb, file="./data/tair10.sqlite")
txdb <- loadDb("./data/tair10.sqlite")
outpaths <- subsetWF(args, slot="output", subset=1, index=1)
# (align <- readGAlignments(outpaths[1])) # Demonstrates how to read bam file into R
eByg <- exonsBy(txdb, by=c("gene"))
bfl <- BamFileList(outpaths, yieldSize=50000, index=character())
multicoreParam <- MulticoreParam(workers=4); register(multicoreParam); registered()
counteByg <- bplapply(bfl, function(x) summarizeOverlaps(eByg, x, mode="Union", 
                                               ignore.strand=TRUE, 
                                               inter.feature=FALSE, 
                                               singleEnd=FALSE)) 
countDFeByg <- sapply(seq(along=counteByg), function(x) assays(counteByg[[x]])$counts)
rownames(countDFeByg) <- names(rowRanges(counteByg[[1]])); colnames(countDFeByg) <- names(bfl)
rpkmDFeByg <- apply(countDFeByg, 2, function(x) returnRPKM(counts=x, ranges=eByg))
write.table(countDFeByg, "results/countDFeByg.xls", col.names=NA, quote=FALSE, sep="\t")
write.table(rpkmDFeByg, "results/rpkmDFeByg.xls", col.names=NA, quote=FALSE, sep="\t")
```

Shows count table generated in previous step (`countDFeByg.xls`). 
To avoid slowdowns of the load time of this page, ony 200 rows of the source 
table are imported into the below `datatable` view .

```{r show_counts_table, eval=TRUE}
countDF <- read.delim("results/countDFeByg.xls", row.names=1, check.names=FALSE)[1:200,]
library(DT)
datatable(countDF, options=list(scrollX=TRUE, autoWidth=TRUE))
```

A data slice of RPKM table (`rpkmDFeByg.xls`) is shown here.

```{r view_rpkm, eval=TRUE}
read.delim("results/rpkmDFeByg.xls", row.names=1, check.names=FALSE)[1:4,1:4]
```

Note, for most statistical differential expression or abundance analysis
methods, such as `edgeR` or `DESeq2`, the raw count values should be used as input. The
usage of RPKM values should be restricted to specialty applications
required by some users, *e.g.* manually comparing the expression levels
among different genes or features.

## Sample-wise correlation analysis

The following computes the sample-wise Spearman correlation coefficients from
the `rlog` transformed expression values generated with the `DESeq2` package. After
transformation to a distance matrix, hierarchical clustering is performed with
the `hclust` function and the result is plotted as a dendrogram
(also see file `sample_tree.pdf`).

```{r sample_tree, eval=FALSE}
library(DESeq2, quietly=TRUE); library(ape,  warn.conflicts=FALSE)
countDF <- as.matrix(read.table("./results/countDFeByg.xls"))
colData <- data.frame(row.names=targets.as.df(targets(args))$SampleName, condition=targets.as.df(targets(args))$Factor)
dds <- DESeqDataSetFromMatrix(countData = countDF, colData = colData, design = ~ condition)
d <- cor(assay(rlog(dds)), method="spearman")
hc <- hclust(dist(1-d))
png("results/sample_tree.png")
plot.phylo(as.phylo(hc), type="p", edge.col="blue", edge.width=2, show.node.label=TRUE, no.margin=TRUE)
dev.off()
```

![](./results/sample_tree.png)
<div align="center">Figure 2: Correlation dendrogram of samples</div></br>

# Analysis of DEGs

The analysis of differentially expressed genes (DEGs) is performed with
the glm method of the `edgeR` package [@Robinson2010-uk]. The sample
comparisons used by this analysis are defined in the header lines of the 
`targets.txt` file starting with `<CMP>`.

## Run `edgeR`

```{r run_edger, eval=FALSE}
library(edgeR)
countDF <- read.delim("results/countDFeByg.xls", row.names=1, check.names=FALSE) 
targets <- read.delim("targetsPE.txt", comment="#")
cmp <- readComp(file="targetsPE.txt", format="matrix", delim="-")
edgeDF <- run_edgeR(countDF=countDF, targets=targets, cmp=cmp[[1]], independent=FALSE, mdsplot="")
```

Add gene descriptions

```{r custom_annot, eval=FALSE}
library("biomaRt")
m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="plants.ensembl.org")
desc <- getBM(attributes=c("tair_locus", "description"), mart=m)
desc <- desc[!duplicated(desc[,1]),]
descv <- as.character(desc[,2]); names(descv) <- as.character(desc[,1])
edgeDF <- data.frame(edgeDF, Desc=descv[rownames(edgeDF)], check.names=FALSE)
write.table(edgeDF, "./results/edgeRglm_allcomp.xls", quote=FALSE, sep="\t", col.names = NA)
```

## Plot DEG results

Filter and plot DEG results for up and down regulated genes. The
definition of *up* and *down* is given in the corresponding help
file. To open it, type `?filterDEGs` in the R console.

```{r filter_degs, eval=FALSE}
edgeDF <- read.delim("results/edgeRglm_allcomp.xls", row.names=1, check.names=FALSE) 
png("results/DEGcounts.png")
DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=20))
dev.off()
write.table(DEG_list$Summary, "./results/DEGcounts.xls", quote=FALSE, sep="\t", row.names=FALSE)
```

![](./results/DEGcounts.png)
<div align="center">Figure 3: Up and down regulated DEGs with FDR of 1%</div></br>

## Venn diagrams of DEG sets

The `overLapper` function can compute Venn intersects for large numbers of sample
sets (up to 20 or more) and plots 2-5 way Venn diagrams. A useful
feature is the possibility to combine the counts from several Venn
comparisons with the same number of sample sets in a single Venn diagram
(here for 4 up and down DEG sets).

```{r venn_diagram, eval=FALSE}
vennsetup <- overLapper(DEG_list$Up[6:9], type="vennsets")
vennsetdown <- overLapper(DEG_list$Down[6:9], type="vennsets")
png("results/vennplot.png")
vennPlot(list(vennsetup, vennsetdown), mymain="", mysub="", colmode=2, ccol=c("blue", "red"))
dev.off()
```

![](./results/vennplot.png)
<div align="center">Figure 4: Venn Diagram for 4 Up and Down DEG Sets</div></br>

# GO term enrichment analysis

## Obtain gene-to-GO mappings

The following shows how to obtain gene-to-GO mappings from `biomaRt` (here for *A.
thaliana*) and how to organize them for the downstream GO term
enrichment analysis. Alternatively, the gene-to-GO mappings can be
obtained for many organisms from Bioconductor’s `*.db` genome annotation
packages or GO annotation files provided by various genome databases.
For each annotation this relatively slow preprocessing step needs to be
performed only once. Subsequently, the preprocessed data can be loaded
with the `load` function as shown in the next subsection.

```{r get_go_annot, eval=FALSE}
library("biomaRt")
listMarts() # To choose BioMart database
listMarts(host="plants.ensembl.org")
m <- useMart("plants_mart", host="plants.ensembl.org")
listDatasets(m)
m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="plants.ensembl.org")
listAttributes(m) # Choose data types you want to download
go <- getBM(attributes=c("go_id", "tair_locus", "namespace_1003"), mart=m)
go <- go[go[,3]!="",]; go[,3] <- as.character(go[,3])
go[go[,3]=="molecular_function", 3] <- "F"; go[go[,3]=="biological_process", 3] <- "P"; go[go[,3]=="cellular_component", 3] <- "C"
go[1:4,]
dir.create("./data/GO")
write.table(go, "data/GO/GOannotationsBiomart_mod.txt", quote=FALSE, row.names=FALSE, col.names=FALSE, sep="\t")
catdb <- makeCATdb(myfile="data/GO/GOannotationsBiomart_mod.txt", lib=NULL, org="", colno=c(1,2,3), idconv=NULL)
save(catdb, file="data/GO/catdb.RData")
```

## Batch GO term enrichment analysis

Apply the enrichment analysis to the DEG sets obtained the above differential
expression analysis. Note, in the following example the `FDR` filter is set
here to an unreasonably high value, simply because of the small size of the toy
data set used in this vignette. Batch enrichment analysis of many gene sets is
performed with the function. When `method=all`, it returns all GO terms passing
the p-value cutoff specified under the `cutoff` arguments. When `method=slim`,
it returns only the GO terms specified under the `myslimv` argument. The given
example shows how a GO slim vector for a specific organism can be obtained from
BioMart.

```{r go_enrich, eval=FALSE}
library("biomaRt")
load("data/GO/catdb.RData")
DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=50), plot=FALSE)
up_down <- DEG_list$UporDown; names(up_down) <- paste(names(up_down), "_up_down", sep="")
up <- DEG_list$Up; names(up) <- paste(names(up), "_up", sep="")
down <- DEG_list$Down; names(down) <- paste(names(down), "_down", sep="")
DEGlist <- c(up_down, up, down)
DEGlist <- DEGlist[sapply(DEGlist, length) > 0]
BatchResult <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="all", id_type="gene", CLSZ=2, cutoff=0.9, gocats=c("MF", "BP", "CC"), recordSpecGO=NULL)
write.table(BatchResult, "results/GOBatchAll.xls", row.names=FALSE, quote=FALSE, sep="\t")
library("biomaRt")
m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="plants.ensembl.org")
goslimvec <- as.character(getBM(attributes=c("goslim_goa_accession"), mart=m)[,1])
BatchResultslim <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="slim", id_type="gene", myslimv=goslimvec, CLSZ=10, cutoff=0.01, gocats=c("MF", "BP", "CC"), recordSpecGO=NULL)
write.table(BatchResultslim, "results/GOBatchSlim.xls", row.names=FALSE, quote=FALSE, sep="\t")
```

Shows GO term enrichment results from previous step. The last gene identifier column (10) 
of this table has been excluded in this viewing instance to minimze the complexity of the
result.
To avoid slowdowns of the load time of this page, ony 200 rows of the source table are loaded
below.

```{r show_GO_table, eval=TRUE}
BatchResult <- read.delim("results/GOBatchAll.xls")[1:200,]
library(DT)
datatable(BatchResult, options=list(scrollX=TRUE, autoWidth=TRUE))
```

## Plot batch GO term results

The `data.frame` generated by `GOCluster` can be plotted with the `goBarplot` function. Because of the
variable size of the sample sets, it may not always be desirable to show
the results from different DEG sets in the same bar plot. Plotting
single sample sets is achieved by subsetting the input data frame as
shown in the first line of the following example.

```{r go_plot, eval=FALSE}
gos <- BatchResultslim
png("results/GOslimbarplotMF.png"); goBarplot(gos, gocat="MF"); dev.off()
png("results/GOslimbarplotBP.png"); goBarplot(gos, gocat="BP"); dev.off()
png("results/GOslimbarplotCC.png"); goBarplot(gos, gocat="CC"); dev.off()
```

![](./results/GOslimbarplotMF.png)
<div align="center">Figure 5: GO Slim Barplot for MF Ontology</div></br>

# Clustering and heat maps

The following example performs hierarchical clustering on the `rlog`
transformed expression matrix subsetted by the DEGs identified in the above
differential expression analysis. It uses a Pearson correlation-based distance
measure and complete linkage for cluster joining.

```{r heatmap, eval=FALSE}
library(pheatmap)
geneids <- unique(as.character(unlist(DEG_list[[1]])))
y <- assay(rlog(dds))[geneids, ]
png("results/heatmap1.png")
pheatmap(y, scale="row", clustering_distance_rows="correlation", clustering_distance_cols="correlation")
dev.off()
```

![](./results/heatmap1.png)
<div align="center">Figure 6: Heat Map with Hierarchical Clustering Dendrograms of DEGs</div></br>

# Version Information

```{r sessionInfo}
sessionInfo()
```

# Funding

This project was supported by funds from the National Institutes of Health (NIH).

# References