CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Repository Overview

Multi-project AI research repository for single-cell biology and video understanding. All project code lives under transfer/code/, data under transfer/data/, and the shared Python 3.13 venv in stack_env/.

Primary Projects

1. Stack (transfer/code/stack/) — Large-scale encoder-decoder foundation model for single-cell biology (in-context learning on 150M cells). Package: arc-stack.
2. cell-eval (transfer/code/cell-eval/) — Evaluation metrics suite for single-cell perturbation prediction models. Package: cell-eval.
3. FOCUS (transfer/code/FOCUS/) — Training-free keyframe selection for long video understanding using multi-armed bandits (ICLR 2026).

Secondary Projects

4. scGPT (transfer/code/scGPT/) — Foundation model for single-cell multi-omics. Uses Poetry.
5. scDFM (transfer/code/scDFM/) — Distributional flow matching for single-cell perturbation prediction (ICLR 2026). Uses Conda.
6. ori_scDFM (transfer/code/ori_scDFM/) — Original upstream scDFM (cloned from AI4Science-WestlakeU/scDFM). Uses dedicated venv ori_scDFM_env/ (Python 3.11).
7. LatentForcing (transfer/code/LatentForcing/) — Image generation with reordered diffusion trajectories (arXiv 2602.11401).
8. CCFM (transfer/code/CCFM/) — Cascaded Conditioned Flow Matching: hybrid of scDFM + LatentForcing + scGPT for guided perturbation prediction. In development.
9. adaptive_prompt_selection (transfer/code/adaptive_prompt_selection/) — Bandit-based prompt selection for Stack in-context learning.
10. prompt_selection (transfer/code/prompt_selection/) — Evaluation framework and baselines for prompt selection experiments.

HPC Computing Rules (GENKAI Supercomputer)

• NEVER run ML/DL/LLM model inference or training on the login node. Always submit to compute nodes via pjsub.
• Login node (genkai0002) is only for: editing code, lightweight file operations, pip install, job submission, checking results.
• Lightweight evaluation scripts (e.g., cell-eval metrics, statistical analysis) are acceptable on the login node.

Job Submission (PJM)

pjsub script.sh                                                          # Batch job
pjsub --interact -L rscgrp=b-inter -L gpu=1 -L elapse=1:00:00           # Interactive GPU (1 GPU, 1h)
pjstat                                                                    # Check status
pjdel <jobid>                                                            # Cancel job

See transfer/gpu_batch.sh and transfer/gpu_interactive.sh for job script templates.

Environment & Installation

Most projects share stack_env/ (Python 3.13). Always activate before work:

source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate

ori_scDFM uses its own dedicated venv ori_scDFM_env/ (Python 3.11):

source /home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/bin/activate

Installing Projects

cd transfer/code/stack && pip install -e .         # Entry points: stack-train, stack-finetune, stack-embedding, stack-generation
cd transfer/code/cell-eval && pip install -e .      # Entry point: cell-eval (subcommands: prep, run, baseline, score)
cd transfer/code/FOCUS && pip install -r requirements.txt
cd transfer/code/scGPT && poetry install
cd transfer/code/LatentForcing && pip install -r requirements.txt
# scDFM uses its own conda env: conda env create -f transfer/code/scDFM/environment.yml
# ori_scDFM uses ori_scDFM_env/ (already installed from environment.yml pip deps)
# CCFM bootstraps from scDFM: cd transfer/code/CCFM && python _bootstrap_scdfm.py

Note: uv is available for fast dependency management (used by cell-eval CI).

Common Commands

Stack

stack-train --config configs/training/bc_large.yaml
stack-finetune --config configs/finetuning/ft_parsecg.yaml
stack-embedding --checkpoint <ckpt> --adata <h5ad> --genelist <pkl> --output <out.h5ad>
stack-generation --checkpoint <ckpt> --base-adata <h5ad> --test-adata <h5ad> --genelist <pkl> --output-dir <dir>

cell-eval

cell-eval run -ap pred.h5ad -ar real.h5ad --num-threads 64 --profile full
cell-eval score --user-input agg_results.csv --base-input base_agg_results.csv

FOCUS

cd transfer/code/FOCUS
python select_keyframe.py --dataset_name longvideobench --dataset_path <path> --output_dir <dir> --num_keyframes 64

scDFM

# Uses its own conda env
cd transfer/code/scDFM
python src/script/run.py --data norman --batch_size 48 --model_type origin --d_model 128

LatentForcing

cd transfer/code/LatentForcing
torchrun --nproc_per_node=8 main_jit.py  # Multi-GPU training

Testing & Linting

# Stack (from transfer/code/stack/)
pytest tests/
pytest tests/test_model_core.py -k "test_name"    # Single test

# cell-eval (from transfer/code/cell-eval/)
pytest tests/
ruff check .    # Linting (rules: E, F, ERA; max-line-length=120)
ruff format --check .  # Format check (used in CI)

Architecture

Stack (transfer/code/stack/src/stack/)

Core model uses Tabular Attention — alternating cell-wise and gene-wise attention on cell-by-gene matrix chunks:

• models/core/base.py — StateICLModelBase: gene reduction -> positional embedding -> N x TabularAttentionLayer -> output MLP. Losses: reconstruction + Sliced Wasserstein distance.
• models/core/ — StateICLModel (alias: scShiftAttentionModel) wraps the base with masking and loss computation.
• models/finetune/ — ICL_FinetunedModel with frozen-teacher distillation via LightningFinetunedModel.
• modules/attention.py — MultiHeadAttention, TabularAttentionLayer (cell-attn + gene-attn per layer).
• modules/regularizers.py — SlicedWassersteinDistance.
• training/ — LightningGeneModel (PyTorch Lightning wrapper), DataModule, scheduler utils.
• finetune/ — LightningFinetunedModel (student-teacher EMA), finetuning DataModule.
• data/ — Dataset configs, HVG computation, H5 data management, sparse matrix loading.
• cli/ — Entry points: launch_training, launch_finetuning, embedding, generation.
• Config: YAML files in configs/training/ and configs/finetuning/. CLI args override config values.

cell-eval (transfer/code/cell-eval/src/cell_eval/)

Uses the registry pattern for metrics:

• _evaluator.py — MetricsEvaluator: takes predicted/real AnnData, runs DE (via pdex), computes metrics.
• metrics/_registry.py — MetricRegistry with register() / compute(). Metrics are AnnData-based or DE-based.
• metrics/_impl.py, _de.py, _anndata.py — Metric implementations.
• _pipeline/ — MetricPipeline with named profiles (e.g., full).
• _score.py — Baseline-normalized scoring.
• _cli/ — Subcommands: prep, run, baseline, score.
• _types/ — Typed containers: PerturbationAnndataPair, DEComparison.

FOCUS (transfer/code/FOCUS/)

Two-file architecture:

• focus.py — FOCUS class: pure CPE bandit algorithm (no I/O). Coarse exploration -> fine exploitation using Bernstein confidence bounds.
• select_keyframe.py — Data pipeline: video loading (decord), BLIP similarity scoring (LAVIS), result output.

scDFM (transfer/code/scDFM/src/)

Flow matching for perturbation prediction: flow_matching/ (algorithm), models/ (networks), tokenizer/ (cell/gene tokens), loss/ (custom losses), data_process/ (loading).

LatentForcing (transfer/code/LatentForcing/)

Diffusion with reordered trajectories. Model variants: model_jit.py, model_cot.py, model_repa.py. Training engine: engine_jit.py. Entry: main_jit.py.

CCFM (transfer/code/CCFM/)

Cascaded flow matching combining scDFM + LatentForcing denoiser + scGPT embeddings. Entry: scripts/run_cascaded.py. Config: config/config_cascaded.py. Imports scDFM modules via _scdfm_imports.py bridge.

adaptive_prompt_selection (transfer/code/adaptive_prompt_selection/)

Bandit-based selection of in-context examples for Stack. adaptive_prompt.py (orchestrator), cell_bandit.py (bandit algorithm). Run via run_experiment.py.

Dataset Details

Main dataset (transfer/data/stack_train/20260203_Parse_10M_PBMC_cytokines.h5ad): 9.7M cells x 40K genes, 12 donors, 91 cytokines, 18 cell types. Key obs columns: donor, cytokine, treatment, cell_type, sample. Stack dataset config format: "human:parse_bio:sample:cell_type:false".

All single-cell projects use AnnData (.h5ad) as the standard data format.

CI/CD

• cell-eval: GitHub Actions CI (uv sync, ruff format --check, pytest -v, CLI smoke test) on push/PR. Python 3.12.
• Stack: GitHub Actions publishes to PyPI on release (trusted publishing via setuptools build).
• scGPT: Claude Code integration workflow for PR review.

GPU Job Notes

• Stack batch jobs set PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 to avoid CUDA OOM fragmentation.
• Job templates: transfer/gpu_batch.sh (batch, 1 GPU, 3h), transfer/gpu_interactive.sh (interactive, configurable hours/GPUs).
• Login node and compute nodes share the same filesystem path: /home/hp250092/ku50001222/qian/aivc/lfj/.