tidybulk: An R tidy framework for modular transcriptomic data analysis1 months ago
Why tidybulk? | Functions/utilities available | Abundance Normalization Functions | Filtering and Selection Functions | Dimensionality Reduction Functions | Clustering Functions | Differential Analysis Functions | Cellularity Analysis Functions | Gene Enrichment Functions | Utility Functions | Validation and Utility Functions | Scientific Citation | Full vignette | Installation Guide | Comprehensive Example Pipeline | Data Overview | Prepare Data for Analysis | Visualize Raw Counts | Step 1: Data Preprocessing | Aggregate Duplicated Transcripts (optional) | Abundance Filtering | Run multiple methods | Compare methods | Category-Aware Abundance Filtering | Why this matters | Run per-category filtering | Using CPM threshold instead of raw counts | Require abundance in multiple categories | Mark features without filtering | Compare global vs. category-aware filtering | Remove Redundant Transcripts | Filter Variable Transcripts | Visualize After Variable Filtering Variable Transcripts (optional) | Scale Abundance | Visualize After Scaling | Step 2: Exploratory Data Analysis | Remove Zero-Variance Features (required for PCA) | Dimensionality Reduction | Visualize Dimensionality Reduction Results | Clustering Analysis | Step 3: Differential Expression Analysis | Basic Differential Expression | Quality Control of the Fit | For edgeR | For DESeq2 | Histograms of p-values across methods | Compare Results Across Methods | Pairplot of pvalues across methods (GGpairs) | Pairplot of effect sizes across methods (GGpairs) | Volcano Plots for Each Method | Differential Expression with Contrasts | For limma-voom | Differential Expression with minimum fold change (TREAT method) | Mixed Models for Complex Designs | Gene Description | Step 4: Batch Effect Correction | Step 5: Gene Enrichment Analysis | Visualize enrichment | Step 6: Cellularity Analysis | Available Deconvolution Methods | Example Usage | Plotting Results | Bibliography