Research Articles
Solving Overdispersion in PCA Component Selection: Advanced Methods for Biomedical Data
Overdispersion in Principal Component Analysis (PCA) leads to unstable and unreliable component selection, severely impacting the interpretability and validity of models in high-dimensional biomedical research.
Batch Effect Correction for PCA in Genomics: A Comprehensive Guide for Researchers and Clinicians
This article provides a comprehensive guide for researchers and drug development professionals on identifying, correcting, and validating batch effects in genomic studies using Principal Component Analysis (PCA) and advanced methods.
From High-Dimensional Data to Biological Insights: A Practical Guide to PCA for Gene Expression Analysis
This guide provides a comprehensive framework for projecting gene expression data onto principal components, a fundamental technique for exploring high-dimensional transcriptomic data.
Covariance Matrix Calculation for Gene Expression Data: A Guide for Genomic Researchers and Drug Developers
This article provides a comprehensive guide to covariance matrix calculation and analysis for high-dimensional gene expression data.
Highly Variable Gene Selection for scRNA-seq PCA: A Foundational Guide for Robust Single-Cell Analysis
This article provides a comprehensive guide for researchers and bioinformaticians on the critical practice of filtering highly variable genes (HVGs) prior to principal component analysis (PCA) in single-cell RNA sequencing...
Mastering Gene Expression Analysis with MATLAB PCA: A Comprehensive Guide for Biomedical Research
This comprehensive guide explores the application of Principal Component Analysis (PCA) in MATLAB for analyzing high-dimensional gene expression data.
PCA Loading Calculation for Gene Selection in Transcriptomics: A Comprehensive Guide for Biomedical Researchers
This article provides a comprehensive guide to Principal Component Analysis (PCA) loading calculation for effective gene selection in transcriptomic studies.
Variance Stabilizing Transformations for RNA-seq PCA: A Practical Guide for Biomedical Researchers
This article provides a comprehensive guide to variance stabilizing transformations, a critical preprocessing step for Principal Component Analysis (PCA) of RNA-seq data.
Mastering Scree Plots: A Step-by-Step Guide to Selecting the Optimal Number of Principal Components for Biomedical Data
This article provides a comprehensive guide for researchers, scientists, and drug development professionals on using scree plots to determine the optimal number of principal components in Principal Component Analysis (PCA).
A Comprehensive Guide to PCA in R with prcomp for Gene Expression Analysis
This article provides a complete framework for performing Principal Component Analysis (PCA) on gene expression data using R's prcomp function.