{"id":4648,"date":"2026-04-22T13:19:37","date_gmt":"2026-04-22T04:19:37","guid":{"rendered":"https:\/\/bdsl.jbnu.ac.kr\/blog\/?p=4648"},"modified":"2026-04-22T13:19:39","modified_gmt":"2026-04-22T04:19:39","slug":"tutorial-differential-expression-analysis","status":"publish","type":"post","link":"https:\/\/bdsl.jbnu.ac.kr\/blog\/tutorial-differential-expression-analysis\/","title":{"rendered":"Tutorial &#8211; differential expression analysis"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\" id=\"introdcution\">Introdcution<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">RNA sequencing (RNA-seq) measures the abundance of RNA molecules (primarily mRNA) in a biological sample. By mapping sequencing reads to a reference genome, we can estimate gene expression levels based on the number of reads aligned to each gene.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Subsequently, differential expression analysis can be performed to identify genes whose expression levels differ significantly between conditions (e.g., healthy vs. disease), providing biological insights into underlying mechanisms.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"installation-of-star\">Installation of STAR<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">STAR (Spliced Transcripts Alignment to a Reference) is a fast RNA-seq aligner that maps sequencing reads (FASTQ files) to a reference genome.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">You can install STAR using conda.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"python\" class=\"language-python\">conda create -n star_env -c bioconda star\nconda activate star_enva\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"generate-index-file\">Generate index file<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Before alignment, STAR requires a genome index built from:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Reference genome (FASTA) Gene annotation (GTF)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">RNA-seq reads often span exon\u2013exon junctions. STAR addresses this by incorporating splice junction information into the index using the &#8211;sjdbGTFfile and &#8211;sjdbOverhang options.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">sjdbOverhang \u2248 read length \u2212 1 (e.g., for 100 bp reads \u2192 99)<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"python\" class=\"language-python\">STAR --runThreadN 40 \\\n        --runMode genomeGenerate \\\n        --genomeDir \/home\/shared\/genome_index_hg38_l100 \\\n        --genomeFastaFiles \/home\/shared\/ncbi\/GCF_000001405.40_GRCh38.p14_genomic.fna \\\n        --sjdbGTFfile \/home\/shared\/ncbi\/genomic.gtf \\\n        --sjdbOverhang 99\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"align-pairs-to-the-reference-genome\">Align pairs to the reference genome<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">For each sample, paired-end reads are aligned to the reference genome.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">STAR outputs:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Sorted BAM file (alignment)<\/li>\n\n\n\n<li>Gene-level counts (optional)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Although STAR can generate gene counts (&#8211;quantMode GeneCounts), many pipelines prefer using a dedicated counting tool such as featureCounts for better control and consistency.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The following is the codes that handle two samples.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"python\" class=\"language-python\">STAR \\\n  --runThreadN 8 \\\n  --genomeDir \/home\/shared\/genome_index_hg38_l100 \\\n  --readFilesIn inputs\/hc_1_1.fastq inputs\/hc_1_2.fastq \\\n  --outFileNamePrefix results\/hc_ \\\n  --outSAMtype BAM SortedByCoordinate \\\n  --quantMode GeneCounts \\\n  --outSAMattributes NH HI AS nM\n<\/code><\/pre>\n\n\n\n<ul class=\"wp-block-list\">\n<li>output\n<ul class=\"wp-block-list\">\n<li>hc_ReadsPerGene.out.tab<\/li>\n\n\n\n<li>hc_Aligned.sortedByCoord.out.bam<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"python\" class=\"language-python\">STAR \\\n  --runThreadN 8 \\\n  --genomeDir \/home\/shared\/genome_index_hg38_l100 \\\n  --readFilesIn inputs\/db_1_1.fastq inputs\/db_1_2.fastq \\\n  --outFileNamePrefix results\/db_ \\\n  --outSAMtype BAM SortedByCoordinate \\\n  --quantMode GeneCounts \\\n  --outSAMattributes NH HI AS nM\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"gene-level-counting-with-featurecounts\">Gene-level counting with featureCounts<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><code>featureCounts<\/code>&nbsp;assigns aligned reads to genes using the annotation file.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"python\" class=\"language-python\">featureCounts \\\n  -T 8 \\\n  -p \\\n  -s 2 \\\n  -a \/home\/shared\/ncbi\/genomic.gtf \\\n  -o counts.txt \\\n  results\/*.bam\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"differential-expression-analysis-with-deseq2\">Differential expression analysis with DESeq2<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Gene count data can be analyzed using DESeq2 to identify differentially expressed genes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The following is R script for&nbsp;<code>DESeq2<\/code>&nbsp;to detect the differentially aboundant genes, plot expression levels and their statistical significances.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"python\" class=\"language-python\"><em># Load count table<\/em>\ncounts &lt;- read.table(\"counts.txt\", header=TRUE, comment.char=\"#\")\n\n<em># Extract count matrix and set row names<\/em>\ncount_matrix &lt;- counts[,7:ncol(counts)]\nrownames(count_matrix) &lt;- counts$Geneid\n\n<em># Set conditions<\/em>\ncoldata &lt;- data.frame(\n  condition = c(\"Diabetes\", \"Diabetes\", \"Diabetes\", \"Healthy\", \"Healthy\", \"Healthy\" )\n)\nrownames(coldata) &lt;- colnames(count_matrix)\n\n<em># Differential expression analysis with DESeq2<\/em>\nlibrary(DESeq2)\n\ndds &lt;- DESeqDataSetFromMatrix(\n  countData = count_matrix,\n  colData = coldata,\n  design = ~ condition\n)\ndds &lt;- DESeq(dds)\nres &lt;- results(dds)\n\n<em># Ploat MA plot<\/em>\nplotMA(res, ylim=c(-5,5))\n\n<em># Ploat volcano plot<\/em>\nplot(res$log2FoldChange, -log10(res$pvalue))<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Introdcution RNA sequencing (RNA-seq) measures the abundance of RNA molecules (primarily mRNA) in a biological sample. By mapping sequencing reads to a reference genome, we can estimate gene expression levels based on the number of reads aligned to each gene. Subsequently, differential expression analysis can be performed to identify genes whose expression levels differ significantly [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":"","_members_access_role":[],"_members_access_error":""},"categories":[1],"tags":[],"class_list":["post-4648","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Tutorial - differential expression analysis - Biomedical Data Science Laboratory<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/bdsl.jbnu.ac.kr\/blog\/tutorial-differential-expression-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Tutorial - differential expression analysis - Biomedical Data Science Laboratory\" \/>\n<meta property=\"og:description\" content=\"Introdcution RNA sequencing (RNA-seq) measures the abundance of RNA molecules (primarily mRNA) in a biological sample. 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