Differential Gene Expression Analysis By R

Rename samples after download using the edit icon to label them as tnbc. Create a paired dataset collection and perform fast qc, setting the read length limit to 30.

perform FastQC quality control analysis to inspect per base quality, phred scores, GC content, sequence length distribution, and duplication, and address adapters and duplicates to ensure clean reads.

Trim, filter, and quality-control sequencing reads with fastp in a single pass for paired data. Use or adjust adapter, trimming, overrepresented sequences, and base-quality options, then run.

Learn to align RNA-seq reads to the human reference genome using hisat2 in Galaxy, with forward-strand, paired-end data. The workflow yields nine BAM files of aligned reads.

Mark and remove duplicates to improve quality, examining sequence- and map-based duplication metrics and BAM outputs, then quantify gene expression with StringTie and Ballgown using a reference GTF.

Merge and quantify transcripts by downloading and renaming ctab data files for Ballgown, creating a phenotype file with disease and normal labels, and running the analysis in R and RStudio.

Install ballgown packages in R, build a ballgown object, and perform differential expression analysis on transcripts and genes, with variance-based filtering and enhanced volcano plots.

This lecture explains the volcano plot, with log2 fold change on the x axis and log10-transformed values on the y axis, highlighting right side upregulated genes in disease versus normal.