Welcome
Pick a tab above to view a one-page overview of each app, including what it does, how to start, and links to the code. Each section follows the same clean card layout for quick scanning.
Bulk RNA-Seq Analyzer
Interactive Shiny app for bulk RNA-seq: differential expression, PCA/UMAP, volcano & heatmaps, enrichment, Random Forest, power analysis, and downloadable results.
- 🗂️ Inputs: counts matrix + phenotype CSV
- 🧬 DE: limma-voom workflow
- 🧭 Dims: PCA & UMAP
- 🌋 Plots: volcano, heatmap, interactive tables
- 🧠 ML: Random Forest + ROC/AUC
- 🧪 Pathways: Enrichr (KEGG/GO/Reactome)
- ⚡ Power: sample size/power curves
- 📦 Runs anywhere: Docker/Singularity
ATAC-Seq Peak Annotation & Enrichment
Upload MACS2 .narrowPeak, annotate with ChIPseeker, and run GO/KEGG/Reactome
enrichment with slick visuals and CSV exports.
- 📄 Input: MACS2
.narrowPeakfile - 🏷️ Annotation: ChIPseeker + TxDb
- 📊 Views: pie charts, tables, barplots
- 🧠 Pathways: enrichR (GO/KEGG/Reactome)
- 🚀 Deploy: Docker & Singularity/HPC
miRNA Differential Expression & Enrichment
DESeq2-based miRNA analysis with PCA/UMAP, volcano & heatmaps, Enrichr enrichment, Random Forest classification, power analysis, and exports.
- 🗂️ Inputs: miRNA counts + metadata
- 🧬 DE: DESeq2 pipeline
- 🧭 Dims: PCA & UMAP
- 🌋 Plots: volcano, top-miRNA bars, heatmaps
- 🧠 ML: RF classification + metrics
- 🧪 Pathways: Enrichr (clusterProfiler fallback)
- ⚡ Power: sample size estimates
DNA Methylation App
Explore beta values, run differential methylation, enrichment, PCA/UMAP, Random Forest, power analysis, and download everything — HPC-ready.
- 🗂️ Input: CSV beta matrix (e.g., 450k)
- 🧪 DE: probe-level stats + FDR
- 🧭 Dims: PCA & UMAP
- 🧠 ML: RF + AUC & importance
- 🧪 Pathways: Enrichr KEGG/GO/Reactome
- ⚡ Power: Cohen’s d → n per group
- 📦 Deploy: Singularity/Apptainer
CRISPR Mixscape Pipeline (Perturb-seq)
Single-cell CRISPR screen workflow using Seurat’s Mixscape: QC/normalization, UMAP, perturbation scoring, KO/NP/NT assignment, DE, and rich plots — with HPC support.
- 🧪 Inputs: counts + metadata CSVs
- 🧭 Dims: UMAP visualization
- 🧮 Mixscape: perturbation scores & class labels
- 🧬 DE: KO vs NT + downloads
- 📈 Views: bar/violin/heatmaps, summaries
- 📦 Deploy: Singularity + Slurm script
GWAS Analysis App
A full-stack, no-code Shiny app for Genome-Wide Association Studies (GWAS) using raw VCF files—no PLINK needed. Upload your VCF, phenotype, and covariate table to begin.
- 🧪 QC Filters: MAF, allele frequency, call rate, HWE p-value thresholds
- 🧮 GWAS Engine: Logistic regression with Bonferroni correction support
- 📊 Visualization: PCA, UMAP, QQ plot, Manhattan plot with region zoom
- 🤖 Machine Learning: Random Forest with AUC, importance, ROC
- 🧬 SNP-to-Gene Mapping: Map significant SNPs to nearest genes
- 🧠 Enrichment: KEGG/GO/Reactome via enrichR
- ⚡ Power Analysis: Cohen’s d-based observed power & sample size curve
- 📦 Export Everything: GWAS tables, enrichment results, ML metrics, more
Single-cell RNA-Seq App
Interactive Seurat-based app to explore scRNA-seq data, run DE, pathway enrichment, classification, power analyses, and download publication-ready tables/plots.
- 🗂️ Upload: counts CSV (genes × cells/samples) + metadata CSV (matching names)
- 🧱 Create Seurat Object: load & normalize your data in-app
- 🧭 Dimensionality Reduction: PCA & UMAP for cluster/pattern visualization
- 🧬 Differential Expression: by condition and by cell type; find condition-only DE genes
- 🧠 Pathway Analysis: enrich DE gene sets across multiple databases
- 🌋 Volcano Plot: publication-style volcano for condition-only DE
- 🤖 Feature Selection & Classification: Random Forest markers, ROC, importance
- 📈 Power Analysis: estimate power and minimum sample size for key DE genes
- 🔥 Heatmaps: top features and group differences
- ⬇️ Downloads: export all result tables and figures
Tips: CSV format only; counts & metadata must match by name. For best results, use
quality-filtered data (see insurance_policy_script.R).