Data analysis

Statistical Tests for Differential Protein Expression in Proteomics

Apr 17, 2026

Learn when to use t-test, Wilcoxon, ANOVA, or Kruskal-Wallis for differential protein expression in proteomics, with practical selection guidance.

T-Test vs Welch's T-Test vs Mann–Whitney U: Which Test Should You Use in Omics?

Apr 16, 2026

Learn how to choose between Student's t-test, Welch's t-test, and Mann–Whitney U for omics data based on variance, sample size, outliers, and biological meaning.

KEGG vs GO vs COG/KOG: Choosing the Right Functional Annotation Strategies for Multi-Omics Analysis

Apr 16, 2026

Understand the differences between COG/KOG, GO, and KEGG, and learn how to choose the right functional annotation strategy for multi-omics data analysis.

COG vs KOG Functional Annotation: Differences, Workflow, and Multi-Omics Applications

Apr 14, 2026

Learn the difference between COG and KOG, how functional annotation works, and how to apply enrichment analysis in multi-omics research.

Differential Feature Screening in Omics: Why the Best Candidate Is Not Always the One with the Smallest p-Value

Apr 09, 2026

Learn how to evaluate fold change, p-value, FDR, and VIP in omics studies to build more reliable candidate lists for biomarker screening and differential analysis.

How to Interpret KEGG Enrichment Analysis Results

Apr 08, 2026

Learn how to interpret KEGG enrichment analysis results using Rich Factor, Gene Count, FDR, pathway significance, and KEGG pathway maps for clearer biological insights.

Correlation Heatmaps: How to Create, Interpret, and Compare Pearson vs Spearman

Apr 03, 2026

Learn how to create and interpret correlation heatmaps, choose Pearson vs Spearman, use clustering correctly, and read heatmap patterns with confidence.

PLS-DA vs OPLS-DA: Choosing the Right Multivariate Analysis Method for Omics Data

Apr 03, 2026

Comprehensive comparison of PLS-DA and OPLS-DA for omics data analysis. Learn when to use each method, key differences, and practical applications in metabolomics research.

A Complete Guide to Spearman Rank Correlation in Multi-Omics Research

Mar 26, 2026

Learn what Spearman correlation is, how it differs from Pearson, and why it is ideal for non-normal, non-linear multi-omics data analysis.

Pearson vs Spearman Correlation: How to Choose the Right Method for Multi-Omics Data Analysis

Mar 25, 2026

Learn when to use Pearson vs Spearman correlation in multi-omics data analysis, and avoid common mistakes in genomics, proteomics, and metabolomics.

PLS-DA in Metabolomics: Principles, Workflow, Interpretation, and Best Practices

Mar 24, 2026

Learn what PLS-DA is, how it works in metabolomics, how to interpret score plots and VIP values, and how to avoid overfitting in biomarker discovery.

A Practical Guide to OPLS-DA: Principles, Workflow, and Result Interpretation in Omics Data Analysis

Mar 13, 2026

Learn what OPLS-DA is, how to perform it step by step, and how to interpret score plots, VIP scores, and validation results correctly in omics data analysis.

PCoA vs. NMDS in Omics: Choosing the Appropriate Ordination Method

Dec 19, 2025

PCoA vs NMDS in omics: learn key assumptions, how to interpret inertia vs stress, and a practical workflow to choose the right ordination method.

Principal Coordinates Analysis (PCoA): Principles, Applications, and a Comparison with PCA

Dec 16, 2025

Learn how PCoA works, how to choose distance metrics (Bray–Curtis/UniFrac), how to interpret plots, and when to use PCoA vs PCA—plus an R workflow.

Comprehensive Guide to the Top Clustering Methods for Omics Data Analysis

Nov 11, 2025

Choose the right clustering for omics data—hierarchical, k-means, DBSCAN—plus distance metrics and validation (silhouette, ARI) with practical examples.

t-SNE vs UMAP: A Comprehensive Guide for Visualizing High-Dimensional Omics Data

Nov 03, 2025

Compare t-SNE vs UMAP for high-dimensional omics—when to use each, key parameters, pros/cons, and tips for scRNA-seq, bulk, and spatial data.

Volcano Plots in Metabolomics & Proteomics: Interpretation, Cutoffs, and Best Practices

Oct 28, 2025

Discover how to read volcano plots, set fold-change/q-value cutoffs, avoid pitfalls, and turn results into pathway insights for omics studies.

Normality Tests in Statistics: Top Methods and Tools for Reliable Data Analysis

Sep 29, 2025

Learn how to check normality fast: Q–Q/P–P plots, Shapiro–Wilk, K–S, Anderson–Darling. Choose by sample size and run in Python, R, or SPSS.

Non-negative Matrix Factorization (NMF) for Omics: A Practical, Interpretable Guide

Sep 03, 2025

Hands-on NMF for omics: choose rank k, interpret W/H, stabilize with cNMF, run pathway enrichment, validate across cohorts—plus a 10-line Python starter.

Why You Must Correct Batch Effects in Transcriptomics Data?

Aug 11, 2025

Learn how to detect and correct batch effects in transcriptomics to avoid false discoveries and improve data accuracy in RNA-seq studies.