---
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
weight: 8
type: docs
---
```{r setup_dir, echo=FALSE, include=FALSE, message=FALSE, warning=FALSE}
unlink(".SPRproject/", recursive = TRUE)
```
```{css, echo=FALSE}
pre code {
white-space: pre !important;
overflow-x: scroll !important;
word-break: keep-all !important;
word-wrap: initial !important;
}
```
```{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)
})
```
Source code download:
[ [.Rmd](https://raw.githubusercontent.com/tgirke/GEN242//main/content/en/tutorials/sprnaseq/sprnaseq.Rmd) ]
[ [.R](https://raw.githubusercontent.com/tgirke/GEN242//main/content/en/tutorials/sprnaseq/sprnaseq.R) ]
## 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
NOTE: this section describes how to set up the proper
environment (directory structure) for running `systemPipeR` workflows. After
mastering this task the workflow run instructions can be
deleted 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=gen242 --mem=20gb --cpus-per-task 8 --ntasks 1 --time 20:00:00 --pty bash -l
module unload R; module load R/4.2.2
```
If the above `srun` command denies access to the gen242 parition, please try adding `--account gen242`.
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 `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/devel/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/devel/bioc/vignettes/systemPipeR/inst/doc/systemPipeR.html#26_structure_of_the_new_param_files_and_construct_sysargs2_container).
### Import custom functions
Custom 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))
```
## Workflow steps
This tutorial will demonstrate how to either build and run the workflow automatically,
or in an interactive mode by appending each step with the `appendStep` method. In both
cases the `SYSargsList` object will be populated with the instructions for running each
workflow step, while supporting both command-line steps as well as line-wise R commands
defined in the corresponding code chunks of this or any `Rmd` file that has been properly
formatted.
To create a workflow within _`systemPipeR`_, we can start by defining an empty
`SYSargsList` container. When restarting an existing workflow one
can set `resume=TRUE` under the `SPRproject()` function call.
```{r create_workflow, message=FALSE, eval=TRUE}
library(systemPipeR)
sal <- SPRproject()
sal
```
Next, the `importWF` function will load the entire workflow into the `SYSargsList` object
(here `sal`). Subsequently, the `runWF()` function will run the workflow from start to finish.
If needed, specific workflow steps can be executed by assigning their corresponding
position numbers within the workflow to the `steps` argument (see `?runWF`). After completion of the workflow
one can render a scientific analysis report in HTML format with the `renderReport()`
function that uses R Markdown internally.
```{r run_workflow2, message=FALSE, eval=FALSE}
sal <- importWF(sal, file_path = "systemPipeRNAseq.Rmd") # Populates SYSargsList object with run instructions for all steps
sal
sal <- runWF(sal) # Runs workflow. This may take some time.
sal <- renderReport(sal) # Renders report
rmarkdown::render("systemPipeRNAseq.Rmd", clean = TRUE, output_format = "BiocStyle::html_document") # Alternative report rendering
```
### Required packages and resources
The `systemPipeR` package needs to be loaded [@H_Backman2016-bt].
```{r load_SPR, message=FALSE, eval=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(code = {
library(systemPipeR)
}, step_name = "load_SPR")
```
### 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
`SYSargsList` container, such as quality filtering or adapter trimming
routines. The paths to the resulting output FASTQ files are stored in the
`outfiles` slot of the `SYSargsList` 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.
Here, we are appending this step to the `SYSargsList` object created previously.
All the parameters are defined on the `preprocessReads/preprocessReads-pe.yml` and
`preprocessReads/preprocessReads-pe.cwl` files.
```{r preprocessing, message=FALSE, eval=TRUE, spr=TRUE}
appendStep(sal) <- SYSargsList(
step_name = "preprocessing",
targets = "targetsPE.txt", dir = TRUE,
wf_file = "preprocessReads/preprocessReads-pe.cwl",
input_file = "preprocessReads/preprocessReads-pe.yml",
dir_path = "param/cwl",
inputvars = c(
FileName1 = "_FASTQ_PATH1_",
FileName2 = "_FASTQ_PATH2_",
SampleName = "_SampleName_"
),
dependency = c("load_SPR"))
```
After, we can check the `trimLRPatterns` function in input parameter:
```{r editing_preprocessing, message=FALSE, eval=TRUE}
yamlinput(sal, "preprocessing")$Fct
```
After the preprocessing step, the `outfiles` files can be used to generate the new
targets files containing the paths to the trimmed FASTQ files. The new targets
information can be used for the next workflow step instance, _e.g._ running the
NGS alignments with the trimmed FASTQ files. The `appendStep` function is
automatically handling this connectivity between steps. Please check the `Alignments`
step for more details.
#### 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=TRUE, message=FALSE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
fastq <- getColumn(sal, step = "preprocessing", "targetsWF", column = 1)
fqlist <- seeFastq(fastq = fastq, batchsize = 10000, klength = 8)
pdf("./results/fastqReport.pdf", height = 18, width = 4*length(fqlist))
seeFastqPlot(fqlist)
dev.off()
}, step_name = "fastq_report",
dependency = "preprocessing")
```
Figure 1: FASTQ quality report for 18 samples
### 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.
The following steps will demonstrate how to use the short read aligner `Hisat2`
[@Kim2015-ve]. First, the `Hisat2` index needs to be created.
```{r hisat2_index, eval=TRUE, spr=TRUE}
appendStep(sal) <- SYSargsList(
step_name = "hisat2_index",
dir = FALSE,
targets=NULL,
wf_file = "hisat2/hisat2-index.cwl",
input_file="hisat2/hisat2-index.yml",
dir_path="param/cwl",
dependency = "load_SPR"
)
```
The parameter settings of the aligner are defined in the `workflow_hisat2-pe.cwl`
and `workflow_hisat2-pe.yml` files. The following shows how to construct the
corresponding *SYSargsList* object. Please note that the targets used in this
step are the outfiles from `preprocessing` step.
```{r hisat2_mapping, eval=TRUE, spr=TRUE}
appendStep(sal) <- SYSargsList(
step_name = "hisat2_mapping",
dir = TRUE,
targets ="preprocessing",
wf_file = "workflow-hisat2/workflow_hisat2-pe.cwl",
input_file = "workflow-hisat2/workflow_hisat2-pe.yml",
dir_path = "param/cwl",
inputvars = c(preprocessReads_1 = "_FASTQ_PATH1_", preprocessReads_2 = "_FASTQ_PATH2_",
SampleName = "_SampleName_"),
rm_targets_col = c("FileName1", "FileName2"),
dependency = c("preprocessing", "hisat2_index")
)
```
To double-check the command line for each sample, please use the following:
```{r bowtie2_alignment, eval=TRUE}
cmdlist(sal, step="hisat2_mapping", targets=1)
```
#### 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=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
fqpaths <- getColumn(sal, step = "preprocessing", "targetsWF", column = "FileName1")
bampaths <- getColumn(sal, step = "hisat2_mapping", "outfiles", column = "samtools_sort_bam")
read_statsDF <- alignStats(args = bampaths, fqpaths = fqpaths, pairEnd = TRUE)
write.table(read_statsDF, "results/alignStats.xls", row.names=FALSE, quote=FALSE, sep="\t")
},
step_name = "align_stats",
dependency = "hisat2_mapping")
```
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 without moving these large files to a local
system. The corresponding URLs are written to a file with a path
specified under `urlfile`, here `IGVurl.txt`. To make the following code work, users
need to change the directory name (here `somedir/`) and the user name (here ``)
to the corresponding names on their system.
```{r bam_urls, eval=FALSE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
bampaths <- getColumn(sal, step = "hisat2_mapping", "outfiles",
column = "samtools_sort_bam")
bampaths <- setNames(normalizePath(bampaths), names(bampaths))
symLink2bam(
sysargs = bampaths, htmldir = c("~/.html/", "somedir/"),
urlbase = "https://cluster.hpcc.ucr.edu/~/",
urlfile = "./results/IGVurl.txt")
},
step_name = "bam_urls",
dependency = "hisat2_mapping",
run_step = "optional"
)
```
### Read quantification
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 4 CPU cores.
#### Create a database for gene annotation
```{r create_db, eval=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
library(GenomicFeatures)
txdb <- suppressWarnings(makeTxDbFromGFF(file="data/tair10.gff", format="gff", dataSource="TAIR", organism="Arabidopsis thaliana"))
saveDb(txdb, file="./data/tair10.sqlite")
},
step_name = "create_db",
dependency = "hisat2_mapping")
```
#### Read counting with `summarizeOverlaps` in parallel mode using multiple cores
```{r read_counting, eval=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
library(GenomicFeatures); library(BiocParallel)
txdb <- loadDb("./data/tair10.sqlite")
outpaths <- getColumn(sal, step = "hisat2_mapping", "outfiles", column = "samtools_sort_bam")
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,
BPPARAM = multicoreParam))
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")
## Creating a SummarizedExperiment object
colData <- data.frame(row.names=SampleName(sal, "hisat2_mapping"),
condition=getColumn(sal, "hisat2_mapping", position = "targetsWF", column = "Factor"))
colData$condition <- factor(colData$condition)
countDF_se <- SummarizedExperiment::SummarizedExperiment(assays = countDFeByg,
colData = colData)
## Add results as SummarizedExperiment to the workflow object
SE(sal, "read_counting") <- countDF_se
},
step_name = "read_counting",
dependency = "create_db")
```
When providing a `BamFileList` as in the example above, `summarizeOverlaps` methods
use by default `bplapply` and use the register interface from BiocParallel package.
If the number of workers is not set, `MulticoreParam` will use the number of cores
returned by `parallel::detectCores()`. For more information,
please check `help("summarizeOverlaps")` documentation.
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,]
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=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
library(DESeq2, quietly=TRUE); library(ape, warn.conflicts=FALSE)
## Extracting SummarizedExperiment object
se <- SE(sal, "read_counting")
dds <- DESeqDataSet(se, design = ~ condition)
d <- cor(assay(rlog(dds)), method="spearman")
hc <- hclust(dist(1-d))
pdf("results/sample_tree.pdf")
plot.phylo(as.phylo(hc), type="p", edge.col="blue", edge.width=2, show.node.label=TRUE, no.margin=TRUE)
dev.off()
},
step_name = "sample_tree",
dependency = "read_counting")
```
Figure 2: Correlation dendrogram of samples
### 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 ``.
#### Run `edgeR`
```{r run_edger, eval=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
library(edgeR)
countDF <- read.delim("results/countDFeByg.xls", row.names=1, check.names=FALSE)
cmp <- readComp(stepsWF(sal)[['hisat2_mapping']], format="matrix", delim="-")
edgeDF <- run_edgeR(countDF=countDF, targets=targetsWF(sal)[['hisat2_mapping']], cmp=cmp[[1]], independent=FALSE, mdsplot="")
},
step_name = "run_edger",
dependency = "read_counting")
```
#### Add gene descriptions
```{r custom_annot, eval=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
library("biomaRt")
m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="https://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)
},
step_name = "custom_annot",
dependency = "run_edger")
```
### 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=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
edgeDF <- read.delim("results/edgeRglm_allcomp.xls", row.names=1, check.names=FALSE)
pdf("results/DEGcounts.pdf")
DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=20))
dev.off()
write.table(DEG_list$Summary, "./results/DEGcounts.xls", quote=FALSE, sep="\t", row.names=FALSE)
},
step_name = "filter_degs",
dependency = "custom_annot")
```
Figure 3: Up and down regulated DEGs with FDR of 1%
### Venn diagrams of DEG sets
The `overLapper` function can compute Venn intersects for large numbers of sample
sets (up to 20 or more) and plots 2-5 way Venn diagrams. A useful
feature is the possibility to combine the counts from several Venn
comparisons with the same number of sample sets in a single Venn diagram
(here for 4 up and down DEG sets).
```{r venn_diagram, eval=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
vennsetup <- overLapper(DEG_list$Up[6:9], type="vennsets")
vennsetdown <- overLapper(DEG_list$Down[6:9], type="vennsets")
pdf("results/vennplot.pdf")
vennPlot(list(vennsetup, vennsetdown), mymain="", mysub="", colmode=2, ccol=c("blue", "red"))
dev.off()
},
step_name = "venn_diagram",
dependency = "filter_degs")
```
Figure 4: Venn Diagram for 4 Up and Down DEG Sets
### 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=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
library("biomaRt")
# listMarts() # To choose BioMart database
# listMarts(host="plants.ensembl.org")
m <- useMart("plants_mart", host="https://plants.ensembl.org")
m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="https://plants.ensembl.org")
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,]
if(!dir.exists("./data/GO")) 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")
},
step_name = "get_go_annot",
dependency = "filter_degs")
```
#### 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=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
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)
m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="https://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")
},
step_name = "go_enrich",
dependency = "get_go_annot")
```
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 minimize the complexity of the
result.
To avoid slowdowns of the load time of this page, only 10 rows of the source table are shown
below.
```{r show_GO_table, eval=TRUE}
BatchResult <- read.delim("results/GOBatchAll.xls")[1:10,]
knitr::kable(BatchResult[,-10])
```
#### 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=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
gos <- BatchResultslim[grep("M6-V6_up_down", BatchResultslim$CLID), ]
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()
},
step_name = "go_plot",
dependency = "go_enrich")
```
Figure 5: GO Slim Barplot for MF Ontology
### 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=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
library(pheatmap)
geneids <- unique(as.character(unlist(DEG_list[[1]])))
y <- assay(rlog(dds))[geneids, ]
pdf("results/heatmap1.pdf")
pheatmap(y, scale="row", clustering_distance_rows="correlation", clustering_distance_cols="correlation")
dev.off()
},
step_name = "heatmap",
dependency = "go_enrich")
```
Figure 6: Heat Map with Hierarchical Clustering Dendrograms of DEGs
### Version Information
```{r sessionInfo, eval=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
code = {
sessionInfo()
},
step_name = "sessionInfo",
dependency = "heatmap")
```
## Running workflow
### Interactive job submissions in a single machine
For running the workflow, `runWF` function will execute all the steps store in
the `SYSargsList` workflow container. The execution will be on a single machine without
submitting to a queuing system of a computer cluster. Besides, `runWF` allows the
user to create a dedicated results folder for each workflow. 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 runWF, eval=FALSE}
sal <- runWF(sal)
```
### 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.
The `resources` list object provides the number of independent parallel cluster
processes defined under the `Njobs` element in the list. The following example
will run 18 processes in parallel using 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 a total of 72 CPU cores.
Note, `runWF` 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 `conffile` (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
`conffile` and `template` files for the Slurm scheduler provided by this package.
The resources can be appended when the step is generated, or it is possible to
add these resources later, as the following example using the `addResources`
function:
```{r runWF_cluster, eval=FALSE}
resources <- list(conffile=".batchtools.conf.R",
template="batchtools.slurm.tmpl",
Njobs=18,
walltime=120, ## minutes
ntasks=1,
ncpus=4,
memory=1024, ## Mb
partition = "short"
)
sal <- addResources(sal, step = c("hisat2_mapping"), resources = resources)
sal <- runWF(sal)
```
### Visualize workflow
_`systemPipeR`_ workflows instances can be visualized with the `plotWF` function.
```{r plotWF, eval=FALSE}
plotWF(sal, out_format = "html", out_path = "plotWF.html")
```
```{r plotWF_show, eval=TRUE, echo = FALSE}
plotWF(sal)
```
## Checking workflow status
To check the summary of the workflow, we can use:
```{r statusWF, eval=FALSE}
sal
statusWF(sal)
```
## Technical report
_`systemPipeR`_ compiles all the workflow execution logs in one central location,
making it easier to check any standard output (`stdout`) or standard error
(`stderr`) for any command-line tools used on the workflow or the R code stdout.
```{r logsWF, eval=FALSE}
sal <- renderLogs(sal)
```
## Scientific report
_`systemPipeR`_ auto-generates scientific analysis reports in HTML format.
```{r, eval=FALSE}
sal <- renderReport(sal)
```
Alternatively, scientific reports can be rendered with the `render` function from `rmarkdown`.
```{r, eval=FALSE}
rmarkdown::render("systemPipeRNAseq.Rmd", clean=TRUE, output_format="BiocStyle::html_document")
```
## Funding
This project is funded by NSF award [ABI-1661152](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661152).
## Session Info
```{r session_info, eval=TRUE}
sessionInfo()
```
## References