🔬 JCAP_ATAC_SEQ APP

An interactive R Shiny app for analyzing and visualizing ATAC-seq peak data.
Upload BED files, generate consensus peaks, annotate regions, run motif enrichment, perform machine learning, simulate power, and more — no coding required.

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🚀 Features

• 📂 Upload ZIPs of BED files to generate consensus peaks
• 🧬 Peak annotation (nearest gene, distance to TSS, etc)
• 📊 Motif enrichment (using motifmatchr + JASPAR2020)
• 🧪 Differential accessibility (DAA) with DESeq2
• 📈 PCA, UMAP, heatmaps for exploratory analysis
• 🤖 Random Forest classifier (AUC, feature importance)
• 🔬 Power analysis for experimental design
• 💾 Downloadable results for all major outputs
• 🎨 Anime-inspired UI (with fairy_tail.css)
• 🖥️ Runs in RStudio, Shiny Server, or on HPC (Singularity)

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🧪 Sample Data

To test the app’s full workflow, use the provided files:

File	Purpose
focused_promoter_peaks.zip	Consensus peaks (ZIP of BED)
simulated_counts (1).csv	Simulated peak × sample counts
simulated_metadata (1).csv	Sample annotations (incl. group)

How to use:
Upload each file via the corresponding input in the sidebar.

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📦 Requirements

You need R (≥ 4.3.x) and the following R packages:

install.packages(c(
  "shiny", "shinyjs", "plotly", "DT", "markdown",
  "randomForest", "pROC", "uwot", "umap"
))
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(c(
  "GenomicRanges", "GenomicFeatures", "org.Hs.eg.db",
  "TxDb.Hsapiens.UCSC.hg38.knownGene", "TFBSTools",
  "JASPAR2020", "BSgenome.Hsapiens.UCSC.hg38", "motifmatchr"
))


🧬 How to Run the App
Option 1: Local RStudio
Download/clone this repository.

Open RStudio, set your working directory to the project folder:

and  run  this in your console

setwd("/path/to/ATAC_APP")
library(shiny)
runApp(".")
The app will open in your browser (e.g. http://localhost:8787 if using Shiny Server).

HPC / Singularity Deployment
Option 2: HPC (with SLURM & Singularity)

1.Build and run from the bash command line:

singularity build atac-shiny.sif Singularity.def
2.Submit your job with SLURM:

sbatch run_atac_app.sh

Monitor the output and error logs as specified in the batch script.

3.Forward port 8787 to your local machine for browser access:

ssh -L 8787:localhost:8787 user@cluster

Open the app in your browser:
http://localhost:8787
Tip:

Edit run_atac_app.sh to adjust resource requirements or output locations for your cluster.

See example SLURM script (run_atac_app.sh) included in the repo.


🎛️ How to Use the App
Consensus Peaks:
Upload a ZIP of BED files and click Make Consensus Peaks.

Peak Annotation:
Click Run Peak Annotation.

Results: Peak Annotation Table, Pie Chart.

Motif Enrichment:
Select a JASPAR family, click Run Motif Enrichment.

Results: Motif Table, Motif Barplot.

Counts & Metadata:
Upload count matrix (CSV) and metadata (CSV).
Click Run DAA on Uploaded Counts to see DESeq2 results.

Exploratory Visualizations:

PCA: Click Run PCA

UMAP: Click Run UMAP

Heatmap: Click Plot Heatmap

Random Forest Classifier:
Click Run Random Forest Classifier.

Results: AUC, Accuracy, Feature Importance.

Power Analysis:
Set effect size, FDR, and replicates. Click Run Power Analysis.

Downloadable Results:
All major tables have Download buttons.

📊 Tab Overview
Tab Name	Description
README	Usage guide
Peak Annotation Table	Annotated peaks (downloadable)
Annotation Pie Chart	Distribution of peak locations
Motif Enrichment Table	TF motif matches
Motif Enrichment Plot	Barplot of top motifs
Uploaded DAA Results	DESeq2 output
PCA Plot	Principal component analysis
UMAP Plot	UMAP projection
Heatmap	Log-scaled count heatmap
RF Metrics	AUC, accuracy, confusion matrix
Feature Importance	Top peaks by Gini importance
ROC Curve	ROC performance plot
Power Plot	Power vs. replicates
Power Table	Power estimates with CI

🛠️ Developer Notes
Error logging: All errors are appended to error_log.txt.

Logs: Use email_log.R to manage or email logs if desired.
ATAC_APP/
├── app.R                    # Main Shiny app
├── run.sh                   # Launch script (local)
├── run_atac_app.sh          # SLURM batch script for HPC
├── email_log.R              # Log emailer
├── error_log.txt            # Error logs
├── www/
│   └── fairy_tail.css       # Themed UI
├── sample_data/
│   └── focused_promoter_peaks.zip
│   └── simulated_counts (1).csv
│   └── simulated_metadata (1).csv
├── logs/                    # Archived logs
├── Singularity.def          # Singularity definition
└── README.md


🧠 FAQ
Can I use BAM files?
No — only BED/peak-level data are supported. Use MACS2 or similar to call peaks first.

Is this cloud deployable?
Not trivially — Bioconductor packages and large genome data require persistent local storage. HPC, local, or Docker/Singularity are recommended.

How big can my datasets be?
The app is robust for datasets up to ~10k peaks × 100 samples. Larger sets may need more RAM.