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Table of Contents

Aim:

Identify statistically significant (FDR < 0.05) differentially expressed genes. Visualise results with PCA plots, heatmaps and volcano plots.

Requirements

Run your samples (FASTQ) using the nextflow nf-core/RNA-seq pipeline using ‘star_salmon’ (Task 3 session) or an alternative pipeline that generates feature counts.

Installing R and Rstudio

The analysis scripts in this guide are written in R script. We will be using RStudio, a front-end gui for R, to run the analysis scripts.

You have three main options for running this analysis in RStudio:

...

Use QUTs rVDI virtual desktop machines

Table of Contents

Aim:

Identify statistically significant (FDR < 0.05) differentially expressed genes. Visualise results with PCA plots, heatmaps and volcano plots.

Requirements

Run your samples (FASTQ) using the nextflow nf-core/RNA-seq pipeline using ‘star_salmon’ (Task 3 session) or an alternative pipeline that generates feature counts.

Installing R and Rstudio

The analysis scripts in this guide are written in R script. We will be using RStudio, a front-end gui for R, to run the analysis scripts.

You have three main options for running this analysis in RStudio:

  1. Use QUTs rVDI virtual desktop machines

  2. Install R and RStudio on your own PC

  3. Use the provided PCs in the QUT computer labs

Option1: Use QUTs rVDI virtual desktop machines

This is the preferred method, as R and RStudio are already installed, as are all the required R packages needed for analysis. Installing all of these can take over 30 minutes on your own PC, so using an rVDI machine saves time.

rVDI provides a virtual Windows desktop that can be run in your web browser.

To access and run an rVDI virtual desktop:

Go to https://rvdi.qut.edu.au/

Click on ‘VMware Horizon HTML Access’

Log on with your QUT username and password

*NOTE: you need to be connected to the QUT network first, either being on campus or connecting remotely via VPN.

Option2: Install R and RStudio on your own PC

...

Use the provided PCs in the QUT computer labs

Option1: Use QUTs rVDI virtual desktop machines

This is the preferred method, as R and RStudio are already installed, as are all the required R packages needed for analysis. Installing all of these can take over 30 minutes on your own PC, so using an rVDI machine saves time.

rVDI provides a virtual Windows desktop that can be run in your web browser.

To access and run an rVDI virtual desktop:

Go to https://rvdi.qut.edu.au/

Click on ‘VMware Horizon HTML Access’

Log on with your QUT username and password

*NOTE: you need to be connected to the QUT network first, either being on campus or connecting remotely via VPN.

Option2: Install R and RStudio on your own PC

Go to the following page https://posit.co/download/rstudio-desktop/ and follow instructions provided to install first R and then Rstudio.

Download and install R, following the default prompts:

https://cran.r-project.org/bin/windows/base/

Download and install RStudio, following the default prompts:

https://posit.co/download/rstudio-desktop/

Option3: Use the provided PCs in the QUT computer labs

The PCs in the computer labs already have R and RStudio installed. If using this option, you will need to install the required R packages (unlike rVDI). The code for installing these packages is in the analysis section below.

Download your count table

The basis for the differential expression analysis is a count table of sequence reads mapped to defined gene regions per sample. There are a variety of methods to generate this count table, but for this exercise we will be using the output from the Nextflow nfcore/rnaseq analysis you completed in the previous workshop sessions.

To access this count table:

...

Code Block
├── results
│   ├── fastqc
│   ├── multiqc
│   ├── pipeline_info
│   ├── star_salmon
│   └── trimgalore

The count table can be found in the /results/star_salmon/ folder. Let’s access the folder (i.e., cd /run3/results/star_salmon). A list of files and folders in the star_salmon folder will look like this:

Code Block
.
├── bigwig
├── CD49fmNGFRm_rep1
├── CD49fmNGFRm_rep1.markdup.sorted.bam
├── CD49fmNGFRm_rep1.markdup.sorted.bam.bai
├── CD49fmNGFRm_rep2
├── CD49fmNGFRm_rep2.markdup.sorted.bam
├── CD49fmNGFRm_rep2.markdup.sorted.bam.bai
├── CD49fmNGFRm_rep3
├── CD49fmNGFRm_rep3.markdup.sorted.bam
├── CD49fmNGFRm_rep3.markdup.sorted.bam.bai
├── CD49fpNGFRp_rep1
├── CD49fpNGFRp_rep1.markdup.sorted.bam
├── CD49fpNGFRp_rep1.markdup.sorted.bam.bai
├── CD49fpNGFRp_rep2
├── CD49fpNGFRp_rep2.markdup.sorted.bam
├── CD49fpNGFRp_rep2.markdup.sorted.bam.bai
├── CD49fpNGFRp_rep3
├── CD49fpNGFRp_rep3.markdup.sorted.bam
├── CD49fpNGFRp_rep3.markdup.sorted.bam.bai
├── deseq2_qc
├── dupradar
├── featurecounts
├── log
├── MTEC_rep1
├── MTEC_rep1.markdup.sorted.bam
├── MTEC_rep1.markdup.sorted.bam.bai
├── MTEC_rep2
├── MTEC_rep2.markdup.sorted.bam
├── MTEC_rep2.markdup.sorted.bam.bai
├── MTEC_rep3
├── MTEC_rep3.markdup.sorted.bam
├── MTEC_rep3.markdup.sorted.bam.bai
├── picard_metrics
├── qualimap
├── rseqc
├── salmon.merged.gene_counts_length_scaled.rds
├── salmon.merged.gene_counts_length_scaled.tsv
├── salmon.merged.gene_counts.rds
├── salmon.merged.gene_counts_scaled.rds
├── salmon.merged.gene_counts_scaled.tsv
├── salmon.merged.gene_counts.tsv  <---- We will use this feature counts file for DESeq2 expression analysis. 
├── salmon.merged.gene_tpm.tsv
├── salmon.merged.transcript_counts.rds
├── salmon.merged.transcript_counts.tsv
├── salmon.merged.transcript_tpm.tsv
├── salmon_tx2gene.tsv
├── samtools_stats
└── stringtie

The expression count file that we are interested is salmon.merged.gene_counts.tsv Let's see the content of the file by printing the top lines using the following command (in PuTTy):

Code Block
head salmon.merged.gene_counts.tsv

the above command will print:

Code Block
gene_id gene_name       CD49fmNGFRm_rep1        CD49fmNGFRm_rep2        CD49fmNGFRm_rep3        CD49fpNGFRp_rep1        CD49fpNGFRp_rep2        CD49fpNGFRp_rep3     MTEC_rep1       MTEC_rep2       MTEC_rep3
ENSMUSG00000000001      Gnai3   2460    2395    2749    2686    3972    4419    7095    4484    6414
ENSMUSG00000000003      Pbsn    0       0       0       0       0       0       0       0       0
ENSMUSG00000000028      Cdc45   43      57      55      79      87.999  89      1241    830     1041.999
ENSMUSG00000000031      H19     2       0       1       17.082  24      16.077  200     139     145.604
ENSMUSG00000000037      Scml2   8       8       16      23      29.001  29      69      57      67
ENSMUSG00000000049      Apoh    1       0       2       1       2       0       0       0       2
ENSMUSG00000000056      Narf    522     496     539     368     457     538     1939    1483    1734
ENSMUSG00000000058      Cav2    1352.999        1349    1371.999        2684.001        4370    4386    6018.999        3429    5501
ENSMUSG00000000078      Klf6    4411    3492    4500    3221    3989    4637    3812    2741    3558

Now let’s copy the ‘salmon.merged.gene_counts.tsv’ file to your laptop/desktop using the file finder.

  • Windows - in the file finder type: \\hpc-fs\work\

  • Mac - open ‘Finder’ → press ‘command + k’ → type: ‘smb://hpc-fs/work/’

Now let’s find the full path to the ‘salmon.merged.gene_counts.tsv’ file:

...

Windows:

...

Mac:

  • cd /folder/that/contains/feature_counts/

  • pwd

Rstudio:

...

Open Rstudio, go to the top bar a click on “Session” → “Select working directory: → “Choose directory”

...

Go to the following page https://posit.co/download/rstudio-desktop/ and follow instructions provided to install first R and then Rstudio.

Download and install R, following the default prompts:

https://cran.r-project.org/bin/windows/base/

Download and install RStudio, following the default prompts:

https://posit.co/download/rstudio-desktop/

Option3: Use the provided PCs in the QUT computer labs

The PCs in the computer labs already have R and RStudio installed. If using this option, you will need to install the required R packages (unlike rVDI). The code for installing these packages is in the analysis section below.

Download your count table

The basis for the differential expression analysis is a count table of sequence reads mapped to defined gene regions per sample. There are a variety of methods to generate this count table, but for this exercise we will be using the output from the Nextflow nfcore/rnaseq analysis you completed in the previous workshop sessions.

To access this count table:

Go to the W:\training\rnaseq\runs\run3_RNAseq\results folder that contains the results from running the nfcore/rnaseq pipeline. The output folders from task 3 look like this:

Code Block
├── results
│   ├── fastqc
│   ├── multiqc
│   ├── pipeline_info
│   ├── star_salmon
│   └── trimgalore

The count table can be found in the /results/star_salmon/ folder. Let’s access the folder (i.e., cd /run3/results/star_salmon). A list of files and folders in the star_salmon folder will look like this:

Code Block
.
├── bigwig
├── CD49fmNGFRm_rep1
├── CD49fmNGFRm_rep1.markdup.sorted.bam
├── CD49fmNGFRm_rep1.markdup.sorted.bam.bai
├── CD49fmNGFRm_rep2
├── CD49fmNGFRm_rep2.markdup.sorted.bam
├── CD49fmNGFRm_rep2.markdup.sorted.bam.bai
├── CD49fmNGFRm_rep3
├── CD49fmNGFRm_rep3.markdup.sorted.bam
├── CD49fmNGFRm_rep3.markdup.sorted.bam.bai
├── CD49fpNGFRp_rep1
├── CD49fpNGFRp_rep1.markdup.sorted.bam
├── CD49fpNGFRp_rep1.markdup.sorted.bam.bai
├── CD49fpNGFRp_rep2
├── CD49fpNGFRp_rep2.markdup.sorted.bam
├── CD49fpNGFRp_rep2.markdup.sorted.bam.bai
├── CD49fpNGFRp_rep3
├── CD49fpNGFRp_rep3.markdup.sorted.bam
├── CD49fpNGFRp_rep3.markdup.sorted.bam.bai
├── deseq2_qc
├── dupradar
├── featurecounts
├── log
├── MTEC_rep1
├── MTEC_rep1.markdup.sorted.bam
├── MTEC_rep1.markdup.sorted.bam.bai
├── MTEC_rep2
├── MTEC_rep2.markdup.sorted.bam
├── MTEC_rep2.markdup.sorted.bam.bai
├── MTEC_rep3
├── MTEC_rep3.markdup.sorted.bam
├── MTEC_rep3.markdup.sorted.bam.bai
├── picard_metrics
├── qualimap
├── rseqc
├── salmon.merged.gene_counts_length_scaled.rds
├── salmon.merged.gene_counts_length_scaled.tsv
├── salmon.merged.gene_counts.rds
├── salmon.merged.gene_counts_scaled.rds
├── salmon.merged.gene_counts_scaled.tsv
├── salmon.merged.gene_counts.tsv  <---- We will use this feature counts file for DESeq2 expression analysis. 
├── salmon.merged.gene_tpm.tsv
├── salmon.merged.transcript_counts.rds
├── salmon.merged.transcript_counts.tsv
├── salmon.merged.transcript_tpm.tsv
├── salmon_tx2gene.tsv
├── samtools_stats
└── stringtie

The expression count file that we are interested is salmon.merged.gene_counts.tsv

Code Block
head salmon.merged.gene_counts.tsv

Differential Expression Analysis using DESeq2

...

a. First create a new folder , on your desktop, Documents, etcin H:\workshop\RNAseq . Call it something informativesuitable, such as ‘DE_analysis_workshop’

b. Create a sub folder here called ‘data’. This is where your two data files will be stored

...