SBM Lab
시스템 생물학 & 바이오인포매틱스 블로그. 프로테오믹스, 바이오마커, 건강과학에 대한 연구 기반 콘텐츠를 제공합니다.
벤 다이어그램 생성기
2~4개 세트를 비교하고 PNG/SVG로 다운로드하세요. 유전자 비교, DEG overlap, 데이터 분석에 활용 가능합니다.
바로 사용하기최신 글
STRING Database Tutorial: Step-by-Step Guide to Protein Network Analysis (2026)
Comprehensive STRING database tutorial covering web interface, R STRINGdb package, confidence scoring, evidence channels, network visualization, and integration with Cytoscape and downstream functional analysis.
Single-Cell RNA-seq Analysis: Complete Guide from Raw Data to Cell Type Annotation (2026)
End-to-end practical guide to scRNA-seq analysis using Seurat and Scanpy. Covers QC, normalization, batch correction, clustering, marker gene identification, cell type annotation, and trajectory analysis with code examples.
DESeq2 vs edgeR vs limma-voom: Complete Comparison for RNA-seq Differential Expression
In-depth comparison of the three standard RNA-seq differential expression tools: DESeq2, edgeR, and limma-voom. Covers statistical models, when to use each, performance benchmarks, and a complete decision framework with R code examples.
더 보기
Transcription Factor Activity Inference and Biomarker Integration: A Practical Workflow
Complete guide to TF activity inference using DoRothEA, viper, SCENIC, and ChEA3. Learn why TF expression alone is misleading, how to integrate PPI Hub + Pathway + TF results into validated biomarker candidates, and the REMARK/TRIPOD reporting standards.
GO and Pathway Enrichment Analysis: A Complete Practical Guide (clusterProfiler, fgsea, MSigDB)
Comprehensive guide to Gene Ontology and Pathway enrichment analysis. Compare clusterProfiler, topGO, g:Profiler, fgsea, and learn to avoid the background gene set trap that invalidates most analyses. Includes ORA vs GSEA decision framework.
PPI Network Construction and Hub Protein Analysis: A Practical Guide for Researchers
Complete practical guide to building Protein-Protein Interaction networks and identifying Hub proteins. Compare STRING, BioGRID, IntAct databases, learn centrality metrics (Degree, Betweenness, MCC), and avoid common analysis pitfalls.
[Part 3/3] 전사인자 활성 추론과 바이오마커 통합 가이드
DoRothEA, viper, SCENIC, ChEA3로 TF 활성을 추론하고, PPI/Pathway/TF 결과를 통합해 바이오마커 후보를 발굴하는 실전 워크플로.
[Part 2/3] GO Annotation과 Pathway Enrichment 실전 가이드
clusterProfiler, topGO, fgsea, MSigDB로 GO와 Pathway Enrichment를 수행하는 실전 워크플로. ORA vs GSEA 선택 기준과 background 함정.
[Part 1/3] PPI 네트워크 구축과 Hub 단백질 분석 실전 가이드
STRING부터 Cytoscape, R igraph까지 — DEG 리스트를 받았을 때 가장 먼저 그려야 할 것. centrality 지표 비교와 흔한 함정.