Gene Expression Data Analysis

This project uses public dataset (microarray and RNA-seq) to analyze the treatment effect of Dexamethasone on epidermal keratinocytes and A549 cell line.

Dataset

RNA-seq dataset

Control samples:

mRNA-seq on A549 cell line treated with 100 nM dexamethasone for 0 mins with 3 biological replicates.

Test samples:

mRNA-seq on A549 cell line treated with 100 nM dexamethasone for 4 hours with 4 biological replicates.

Microarray dataset

For microarry dataset, we use

control samples:

• GSM651310 Keratinocytes, untreated 4h, rep1
• GSM651320 Keratinocytes, untreated 4h, rep2

test samples:

• GSM651315 Keratinocytes, DEX-treated 4h, rep1
• GSM651325 Keratinocytes, DEX-treated 4h, rep2

The GEO accession of the dataset is GSE26487

Download the dataset

Within the project root directory, run

./script/download_microarray_dataset.sh
./script/download_rnaseq_dataset.sh

It may take a long time to download the RNA-seq data.

We also have to download the transcript fasta file in order to do transcript quantification with the RNA-seq data.

mkdir -p data
bash -c 'cd data && curl -O "ftp://ftp.ensembl.org/pub/release-94/fasta/homo_sapiens/cdna/Homo_sapiens.GRCh38.cdna.all.fa.gz"'
gunzip 'data/Homo_sapiens.GRCh38.cdna.all.fa.gz'

RNA-seq data processing

Build transcriptome index for salmon

salmon index -t data/Homo_sapiens.GRCh38.cdna.all.fa -i data/transriptome_index -p 8

Transcript Quantification

Run the script to do transccript quantification for the samples.

./script/salmon_quant.sh

The results of the quantification have been added to the repository within the branch with_quant_result.

Analysis of the microarray data

https://h3k4me3.github.io/Biol6150ExprAnalysis/microarray_explore.html

Analysis of the RNA-seq data

https://h3k4me3.github.io/Biol6150ExprAnalysis/rnaseq_explore.html

Reference

• New RNA-seq workflows

• An end to end workflow for differential gene expression using Affymetrix microarrays

• Analyze your own microarray data in R/Bioconductor

• From CEL Files to Annotated Gene Lists

• RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR

• RNA-seq workflow: gene-level exploratory analysis and differential expression

• From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline