Data analysis

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.

PLS-DA vs PCA: Key Differences and Use Cases in Omics Analysis

May 27, 2025

Learn the difference between PCA and PLS-DA in omics data analysis. Compare their strengths and decide which method fits your research goals.

GO vs KEGG vs GSEA: How to Choose the Right Enrichment Analysis?

May 21, 2025

GO vs KEGG vs GSEA: Compare gene enrichment methods to decide which suits your study. Understand key differences, use cases, and visualization tips.

Understanding K-means Clustering: A Comprehensive Guide

Mar 26, 2025

Master K-means clustering with this step-by-step guide—learn its algorithm, applications in bioinformatics, visualization techniques, and how to choose the optimal K value.

Correlation Analysis and Correlation Networks: Key Techniques for Exploring Data Relationships

Mar 21, 2025

Unlock the power of correlation analysis and correlation networks to uncover hidden relationships in complex datasets—key techniques in data science, biology, and more.

Canonical Correlation Analysis (CCA) for Multi-Omics Data Integration

Mar 18, 2025

Learn how Canonical Correlation Analysis (CCA) uncovers relationships between variable sets, with applications in bioinformatics, gene expression, and multi-omics data integration.

Comprehensive Guide to ROC Curve: Theory, Applications, and Implementation

Mar 14, 2025

Learn everything about ROC curves, from theory and applications to step-by-step implementation in R. Improve model evaluation, optimize thresholds, and enhance decision-making in machine learning and clinical diagnostics.

Handling Missing Values and Outliers in Bioinformatics

Mar 13, 2025

Learn how to handle missing values and outliers in bioinformatics to improve data quality and ensure accurate analyses. Discover key imputation techniques and outlier detection methods.