A comprehensive walkthrough of digital biology workflows in R, covering RNA-seq differential expression analysis with DESeq2, variance stabilization, PCA, volcano plots, heatmaps, regularized logistic regression and random forest for biomarker-based classification, time series modeling of longitudinal biomarker data using
Table of contents
Why R is a Professional Standard in Digital BiologyCore Setup for a Digital Biology Workflow in RImporting Biological and Clinical DataQuality Control and Filtering of Biological FeaturesDifferential Expression Analysis with DESeq2Variance Stabilization and Exploratory Biological PatternsVolcano Plots and Expression HeatmapsFrom Omics to Biomedical PredictionModel Interpretation and Feature ImportanceTime Series Analysis in Digital BiologyGene-Level Temporal AnalysisFunctional Interpretation and Pathway EnrichmentReproducibility, Reporting, and Professional StandardsStrategic Perspective: Why Digital Biology Needs Both Prediction and TimeConclusionSort: