scripts/readme.md

# Paper Experiment Scripts

This directory is a complete command plan for producing the data needed by the
paper:

> predictor-guided evaluation of autoregressive and masked-diffusion genomic
> foundation models for controllable DeepSTARR enhancer generation.

All scripts are written to use the shared environment file:

```bash
source /Users/ouzhang/Desktop/genRL/project/scripts/00_setup/env.sh
```

By default, outputs are written to:

```text
/Users/ouzhang/Desktop/genRL/project/paper_runs/
  data/
  models/
  results/
  figures/
```

You can override locations before running:

```bash
export RUN_ROOT=/path/to/paper_runs
export DEEPSTARR_DIR=/path/to/DeepSTARR-enhancer-activity
export GENERATOR_BASE_MODEL=/path/to/GENERATOR-eukaryote-1.2b-base
export GENERANNO_BASE_MODEL=/path/to/GENERanno-eukaryote-0.5b-base
```

## 0. Install Dependencies

```bash
cd /Users/ouzhang/Desktop/genRL/project
bash scripts/00_setup/install_requirements.sh
```

This installs the requirements from `GENERATOR/`, `GENERanno/`, and extra
analysis packages: `datasets`, `huggingface_hub`, `pyarrow`, `scipy`,
`scikit-learn`, `matplotlib`, `seaborn`, and `biopython`.

## 1. Download Data and Models

Download DeepSTARR from HuggingFace and save `train/valid/test.parquet`:

```bash
cd /Users/ouzhang/Desktop/genRL/project
bash scripts/01_data/download_deepstarr.sh
```

Summarize the dataset splits for the paper's dataset table:

```bash
bash scripts/01_data/summarize_deepstarr.sh
```

Prepare conditioned DeepSTARR splits using train-set activity quantiles:

```bash
bash scripts/01_data/prepare_conditioned_deepstarr.sh
```

Create a GC-matched random DNA negative baseline:

```bash
bash scripts/01_data/make_random_gc_baseline.sh
```

This creates:

```text
${CONDITIONED_DEEPSTARR_DIR}/train.parquet
${CONDITIONED_DEEPSTARR_DIR}/valid.parquet
${CONDITIONED_DEEPSTARR_DIR}/test.parquet
${CONDITIONED_DEEPSTARR_DIR}/conditioning_metadata.json
```

Optional: download fresh HuggingFace model snapshots:

```bash
bash scripts/01_data/download_models.sh
```

If you already have local models, set:

```bash
export GENERATOR_BASE_MODEL=/Users/ouzhang/Desktop/genRL/project/GENERATOR-eukaryote-1.2b-base
export GENERANNO_BASE_MODEL=/Users/ouzhang/Desktop/genRL/project/models/GENERanno-eukaryote-0.5b-base
```

## 2. Train Activity Predictor

Train the DeepSTARR activity predictor:

```bash
bash scripts/02_predictor/train_predictor.sh
```

Default output:

```text
${RESULT_ROOT}/deepstarr_regression/
${RESULT_ROOT}/deepstarr_regression/best_model/
${RESULT_ROOT}/deepstarr_regression/test_results.json
```

Plot predictor validation metrics:

```bash
bash scripts/02_predictor/plot_predictor_validation.sh
```

Expected paper metrics:

- Pearson for `label_0`, `label_1`, and overall.
- R2 for `label_0`, `label_1`, and overall.
- MAE/MSE for predictor reliability.

This predictor is the oracle used to score all generated enhancer sequences.

## 3. Train Autoregressive GENERator Baselines

Train unconditional AR SFT:

```bash
bash scripts/03_ar_generation/train_ar_unconditional.sh
```

Train bucket-conditioned AR SFT:

```bash
bash scripts/03_ar_generation/train_ar_conditioned.sh
```

The conditioned model uses:

```text
<sp0> = low activity
<sp1> = mid activity
<sp2> = high activity
```

Generated model paths:

```text
${AR_UNCOND_MODEL}
${AR_COND_MODEL}
```

## 4. Evaluate AR Generation

Evaluate unconditional AR generation:

```bash
bash scripts/03_ar_generation/evaluate_ar_unconditional.sh
```

Evaluate conditioned AR generation:

```bash
bash scripts/03_ar_generation/evaluate_ar_conditioned.sh
```

Outputs:

```text
${RESULT_ROOT}/ar_unconditional_valid/generation_details.jsonl
${RESULT_ROOT}/ar_unconditional_valid/generation_summary.json
${RESULT_ROOT}/ar_conditioned_valid/generation_details.jsonl
${RESULT_ROOT}/ar_conditioned_valid/generation_summary.json
```

These files provide sequence validity, uniqueness, bp accuracy, and generated
sequence details.

## 5. Score AR Samples with the Predictor

Score unconditional AR samples:

```bash
bash scripts/05_scoring/score_ar_unconditional.sh
```

Score conditioned AR samples:

```bash
bash scripts/05_scoring/score_ar_conditioned.sh
```

Outputs:

```text
${RESULT_ROOT}/ar_unconditional_scoring/scoring_details.jsonl
${RESULT_ROOT}/ar_unconditional_scoring/scoring_summary.json
${RESULT_ROOT}/ar_conditioned_scoring/scoring_details.jsonl
${RESULT_ROOT}/ar_conditioned_scoring/scoring_summary.json
```

These are required for controllability analysis:

- predicted `label_0`
- predicted `label_1`
- predicted activity sum
- delta vs matched reference
- positive-delta rate
- bucket-level high/mid/low separation

## 6. Train Masked Discrete Diffusion

Train the bucket-conditioned discrete diffusion model:

```bash
bash scripts/04_diffusion/train_diffusion_conditioned.sh
```

Default output:

```text
${DIFFUSION_MODEL}
```

The diffusion training objective randomly masks A/C/G/T positions and trains a
masked LM to recover the original bases. The condition token is prepended when
`--conditioned` is enabled.

## 7. Evaluate Diffusion

First, run PLL-only evaluation:

```bash
bash scripts/04_diffusion/evaluate_diffusion_pll_only.sh
```

This measures diffusion pseudo-log-likelihood:

```text
${RESULT_ROOT}/diffusion_valid_pll/diffusion_scoring_details.jsonl
${RESULT_ROOT}/diffusion_valid_pll/diffusion_scoring_summary.json
```

Then run predictor-scored diffusion evaluation:

```bash
bash scripts/04_diffusion/evaluate_diffusion_with_predictor.sh
```

This is mandatory for the final paper because PLL is not an activity score.
The output is:

```text
${RESULT_ROOT}/diffusion_valid_predictor/diffusion_scoring_details.jsonl
${RESULT_ROOT}/diffusion_valid_predictor/diffusion_scoring_summary.json
```

Use this to compare diffusion against AR conditioned generation under the same
predictor.

## 8. Compute Sequence Quality and Distribution Metrics

Run:

```bash
bash scripts/06_sequence_metrics/compute_sequence_metrics.sh
```

Outputs:

```text
${RESULT_ROOT}/sequence_metrics/sequence_metrics_rows.csv
${RESULT_ROOT}/sequence_metrics/sequence_metrics_summary.json
```

Metrics include:

- valid DNA rate
- unique rate
- sequence length
- GC content
- max homopolymer length
- pairwise Hamming distance
- nearest-reference Hamming distance
- 3-mer and 4-mer Jensen-Shannon divergence to reference
- predictor score when available
- diffusion PLL when available

These metrics are the core data for the generator comparison table.

## 9. Optional Motif Analysis

Download a JASPAR motif file manually, then set:

```bash
export JASPAR_MOTIFS=/path/to/JASPAR2024_CORE_non-redundant_pfms_jaspar.txt
```

Run motif scan:

```bash
bash scripts/07_motif_analysis/run_motif_scan.sh
```

Output:

```text
${RESULT_ROOT}/motif_analysis/motif_scan_summary.csv
```

Use this for motif enrichment and regulatory grammar figures.

If no JASPAR file is available, skip this step and report motif analysis as a
future extension.

## 10. Build Paper Figures

Run:

```bash
bash scripts/08_visualization/make_paper_figures.sh
```

Outputs:

```text
${FIGURE_ROOT}/paper/fig_generation_gc_content.png
${FIGURE_ROOT}/paper/fig_generation_homopolymer.png
${FIGURE_ROOT}/paper/fig_nearest_reference_distance.png
${FIGURE_ROOT}/paper/fig_predicted_activity_by_method.png
${FIGURE_ROOT}/paper/fig_activity_2d_scatter.png
${FIGURE_ROOT}/paper/fig_diffusion_pll.png
${FIGURE_ROOT}/paper/fig_generator_quality_summary.png
${FIGURE_ROOT}/paper/table_generator_comparison.csv
```

If motif analysis was run, it also creates:

```text
${FIGURE_ROOT}/paper/fig_motif_hit_rate_heatmap.png
```

## 11. One-Command Runs

Core paper experiments, excluding motif analysis:

```bash
cd /Users/ouzhang/Desktop/genRL/project
bash scripts/09_all/run_all_core.sh
```

Core experiments plus motif analysis:

```bash
export JASPAR_MOTIFS=/path/to/JASPAR2024_CORE_non-redundant_pfms_jaspar.txt
bash scripts/09_all/run_all_with_optional_motifs.sh
```

## 12. Recommended Paper Tables and Figures

### Tables

1. Dataset split statistics:
   - from `${RESULT_ROOT}/data/deepstarr_split_summary.json`

2. Predictor performance:
   - from `${PREDICTOR_DIR}/test_results.json`

3. Generator comparison:
   - from `${RESULT_ROOT}/sequence_metrics/sequence_metrics_summary.json`
   - from `${FIGURE_ROOT}/paper/table_generator_comparison.csv`

4. Controllability by bucket:
- AR: `${RESULT_ROOT}/ar_conditioned_scoring/scoring_summary.json`
- diffusion: `${RESULT_ROOT}/diffusion_valid_predictor/diffusion_scoring_summary.json`
- negative control: `${RESULT_ROOT}/controls/random_gc_matched.jsonl`

### Figures

1. Pipeline figure:
   - manually draw from data -> predictor -> AR/diffusion -> evaluation.

2. Predictor validation:
   - `${FIGURE_ROOT}/predictor/predictor_metrics.png`

3. Generation quality:
   - `${FIGURE_ROOT}/paper/fig_generation_gc_content.png`
   - `${FIGURE_ROOT}/paper/fig_generation_homopolymer.png`
   - `${FIGURE_ROOT}/paper/fig_nearest_reference_distance.png`

4. Controllability:
   - `${FIGURE_ROOT}/paper/fig_predicted_activity_by_method.png`
   - `${FIGURE_ROOT}/paper/fig_activity_2d_scatter.png`

5. Diffusion naturalness:
   - `${FIGURE_ROOT}/paper/fig_diffusion_pll.png`

6. Motif/regulatory grammar:
   - `${FIGURE_ROOT}/paper/fig_motif_hit_rate_heatmap.png`

## 13. Minimum Experiments Needed for a Defensible Paper

Do not submit the paper until these are complete:

1. Predictor trained and tested.
2. AR unconditional generated and scored.
3. AR conditioned generated and scored.
4. Diffusion conditioned generated and scored with the same predictor.
5. Sequence quality metrics computed for all methods.
6. At least one negative/control baseline included:
   - reference sequences
   - random GC-matched sequences
   - shuffled enhancers
   - or unconditional generator

The current project already has many pieces, but the most important missing
piece is diffusion evaluation with `--predictor_model`. Without that, diffusion
can only be discussed as naturalness/PLL analysis, not enhancer activity
control.

## 14. Paper Strengthening Experiments

After `01_data`, `02_predictor`, and the first conditioned diffusion model in
`04_diffusion` are complete, run the extra experiments under:

```text
scripts/10_paper_strengthening/
```

These scripts are ordered by priority.

### P0: Minimum paper closure

This finishes the core paper comparison:

```bash
nohup bash scripts/10_paper_strengthening/p0_minimum_paper_closure.sh > p0_minimum_paper_closure.log 2>&1 &
tail -f p0_minimum_paper_closure.log
```

It runs:

- predictor validation plot
- AR unconditional training/evaluation/scoring
- AR conditioned training/evaluation/scoring
- diffusion predictor evaluation
- sequence metrics
- paper figure generation

### P1: Core ablations

Mask-ratio ablation:

```bash
nohup bash scripts/10_paper_strengthening/p1_mask_ratio_ablation.sh > p1_mask_ratio_ablation.log 2>&1 &
tail -f p1_mask_ratio_ablation.log
```

Conditioning ablation:

```bash
nohup bash scripts/10_paper_strengthening/p1_conditioning_ablation.sh > p1_conditioning_ablation.log 2>&1 &
tail -f p1_conditioning_ablation.log
```

These are the most important extra experiments for the mutation-budget claim:

- lower mask ratios should preserve reference-like sequence structure
- higher mask ratios should increase novelty and diversity
- conditioned diffusion should show stronger low/mid/high activity separation
  than unconditioned diffusion

### P2: Test split and denoising step ablation

Test split evaluation:

```bash
nohup bash scripts/10_paper_strengthening/p2_test_split_diffusion.sh > p2_test_split_diffusion.log 2>&1 &
tail -f p2_test_split_diffusion.log
```

Denoising step ablation:

```bash
nohup bash scripts/10_paper_strengthening/p2_diffusion_steps_ablation.sh > p2_diffusion_steps_ablation.log 2>&1 &
tail -f p2_diffusion_steps_ablation.log
```

Use this to report final held-out results and the quality/runtime trade-off for
16, 32, 64, and 128 denoising steps.

### P3: Seed robustness

```bash
nohup bash scripts/10_paper_strengthening/p3_seed_repeats_diffusion.sh > p3_seed_repeats_diffusion.log 2>&1 &
tail -f p3_seed_repeats_diffusion.log
```

Use the resulting summaries to report mean and standard deviation over seeds.

### P4: Paired refinement / evolutionary editing

This is the key experiment for the "diffusion as refiner" claim. It compares
the same input sequence before and after a small masked-diffusion edit:

```text
delta = predictor(edited_sequence) - predictor(reference_sequence)
```

It covers:

- paired delta distributions across `rho = 0.01, 0.03, 0.05, 0.10, 0.20`
- random mask vs entropy-guided vs logit-gap-guided position selection
- best-of-N refinement curves

Recommended full run:

```bash
PROJECT_ROOT=$(pwd) \
RUN_ROOT=$(pwd)/paper_runs \
DEEPSTARR_DIR=$(pwd)/datas/DeepSTARR-enhancer-activity \
GENERANNO_BASE_MODEL=$(pwd)/models/GENERanno-eukaryote-0.5b-base \
DIFFUSION_MODEL=$(pwd)/saved_model/deepstarr_discrete_diffusion \
PREDICTOR_MODEL=$(pwd)/paper_runs/results/deepstarr_regression/best_model \
REFINEMENT_SPLIT=test \
REFINEMENT_NUM_SEQUENCES=1024 \
REFINEMENT_NUM_SAMPLES=8 \
REFINEMENT_MASK_RATIOS="0.01 0.03 0.05 0.10 0.20" \
REFINEMENT_STRATEGIES="random entropy logit_gap" \
REFINEMENT_DIFFUSION_STEPS=32 \
REFINEMENT_BATCH_SIZE=16 \
PREDICTOR_SCORE_BATCH_SIZE=256 \
TRANSFORMERS_NO_TF=1 \
USE_TF=0 \
TOKENIZERS_PARALLELISM=false \
nohup bash scripts/11_refinement/run_paired_refinement.sh > paired_refinement.log 2>&1 &
tail -f paired_refinement.log
```

Outputs are written to:

```text
paper_runs/results/refinement/paired_refinement_test
```

### Recommended server environment

On the GPU server, set project-local data/model paths before these scripts:

```bash
cd /inspire/hdd/project/intelligentcreativedesign/dangshengqi-253114050252/z-anna/genrl-enhancer-diffusion

export PROJECT_ROOT=$(pwd)
export RUN_ROOT=${PROJECT_ROOT}/paper_runs
export HF_ENDPOINT=https://hf-mirror.com
export TRANSFORMERS_NO_TF=1
export USE_TF=0
export TOKENIZERS_PARALLELISM=false

source scripts/00_setup/env.sh

export DEEPSTARR_DIR=${PROJECT_ROOT}/datas/DeepSTARR-enhancer-activity
export DEEPSTARR_DATASET_ID=${DEEPSTARR_DIR}
export GENERATOR_BASE_MODEL=${PROJECT_ROOT}/models/GENERator-eukaryote-1.2b-base
export GENERANNO_BASE_MODEL=${PROJECT_ROOT}/models/GENERanno-eukaryote-0.5b-base
export PREDICTOR_DIR=${PROJECT_ROOT}/paper_runs/results/deepstarr_regression
export PREDICTOR_MODEL=${PREDICTOR_DIR}/best_model
export DIFFUSION_MODEL=${PROJECT_ROOT}/saved_model/deepstarr_discrete_diffusion
```