Research Articles
Supervised vs. Unsupervised PCA in Genomics: A Practical Guide for Biomedical Researchers
Principal Component Analysis (PCA) is a cornerstone of genomic data analysis, but the choice between its supervised and unsupervised implementations carries significant implications for discovery and interpretation.
PCA on Microarray vs RNA-seq Data: A Practical Guide for Performance and Application in Biomedical Research
This article provides a comprehensive comparison of Principal Component Analysis (PCA) performance on microarray and RNA-seq transcriptomic data.
Sparse PCA vs. Standard PCA for Gene Selection: A Comprehensive Guide for Genomic Research
This article provides a comprehensive framework for researchers and drug development professionals to evaluate and apply sparse Principal Component Analysis (PCA) against standard PCA for gene selection in high-dimensional genomic...
DESeq2 vs edgeR: How Normalization Choice Impacts PCA in Your RNA-Seq Analysis
Principal Component Analysis (PCA) is a cornerstone of RNA-seq data exploration, yet its results are profoundly shaped by the normalization method chosen.
PCA vs. MANOVA: Choosing the Right Tool for High-Dimensional Gene Expression Analysis
This article provides a comprehensive guide for researchers and bioinformaticians on applying Principal Component Analysis (PCA) and Multivariate Analysis of Variance (MANOVA) to high-dimensional gene expression data.
Linear PCA vs. Kernel PCA for Genomic Data: A Comprehensive Guide for Biomedical Researchers
This article provides a thorough comparison of Linear and Kernel Principal Component Analysis (PCA) for analyzing high-dimensional genomic data.
Information Loss in PCA of Gene Expression Data: Assessment, Mitigation, and Best Practices for Biomedical Research
Principal Component Analysis (PCA) is indispensable for analyzing high-dimensional gene expression data, but its improper application can lead to significant loss of biological information.
Beyond the Scatterplot: A Practical Framework for Biologically Validating PCA Results in Biomedical Research
Principal Component Analysis (PCA) is a cornerstone of exploratory data analysis in biology and drug development, but its results can be misleading without rigorous biological validation.
Kaiser-Guttman vs. Scree Test: A Practical Guide for Dimensionality in RNA-Seq Analysis
This article provides a comprehensive guide for researchers and drug development professionals on applying and evaluating the Kaiser-Guttman criterion and the Scree test for dimensionality assessment in RNA-Seq analysis.
Optimizing High-Dimensional Covariance Estimation for Robust Gene Expression Analysis in Biomedical Research
Accurate covariance matrix estimation is fundamental for analyzing high-dimensional gene expression data, enabling critical tasks in drug discovery and disease research such as co-expression network analysis, module identification, and biomarker...