---
license: mit
language:
- en
tags:
- biology
- single-cell
- scRNA-seq
- macrophage
- IBD
- inflammatory-bowel-disease
- perturbation
- gene-knockout
- scGPT
- transformers
metrics:
- pearsonr
- roc_auc
---

# MacroPert — IBD Macrophage Perturbation Model

A fine-tuned scGPT model for predicting macrophage polarization state and IBD disease status from single-cell RNA-seq, combined with in silico gene knockout predictions for 13 IBD target genes.

## Model Description

Macrophages in inflammatory bowel disease (IBD) exist along a continuous spectrum between pro-inflammatory (M1) and anti-inflammatory (M2) states, rather than in discrete classes. **MacroPert** captures this continuous polarization spectrum and predicts how individual gene knockouts shift macrophage state.

The repository contains:

| File | Description |
|------|-------------|
| `scgpt_ibd_v2/best_model.pt` | scGPT v2 fine-tuned weights — continuous polarization model |
| `scgpt_ibd_v2/test_metrics.json` | Test set performance metrics |
| `scgpt_ibd_v1/best_model.pt` | scGPT v1 weights — discrete 4-class baseline (archived) |
| `scgpt_ibd_v1/test_metrics.json` | v1 test metrics |
| `results/ibd_ko_predictions_combined.json` | In silico KO predictions for 8 IBD target genes |

## Performance

| Metric | scGPT v1 (4-class) | scGPT v2 (continuous) |
|--------|--------------------|-----------------------|
| IBD AUROC | 0.915 | **0.989** |
| Polarization Pearson r | — | **0.909** |
| Classification Accuracy | 0.754 | — |

## Training Data

Fine-tuned on four IBD macrophage scRNA-seq datasets:

| GEO Accession | Cells | Condition |
|---------------|-------|-----------|
| GSE134809 | 13,794 | IBD macrophages (primary) |
| GSE116222 | ~8,000 | Ulcerative colitis macrophages |
| GSE182270 | ~6,000 | IBD macrophage substates |
| GSE148810 | ~5,000 | Crohn's disease macrophages |

## Model Architecture

- **Base:** scGPT pretrained transformer
- **Fine-tuning objective:**
  ```
  L = 0.5 × L_soft_contrastive + 0.3 × L_MSE(pol) + 0.2 × L_BCE(ibd)
  ```
- **Polarization score:** `pol_score = z-score(M1_score − M2_score)` using literature-based gene signatures
  - M1 genes: TNF, IL1B, IL6, CXCL10, NOS2, CD80, CD86, CCL5, CXCL9, PTGS2, IRF5, HIF1A, CXCL8, IL12A
  - M2 genes: CD163, MRC1, ARG1, IL10, TGFB1, CCL18, CD209, FOLR2, SOCS3, HMOX1, CLEC7A
- **Soft contrastive loss:** Gaussian kernel `exp(−d²/2σ²)` over polarization score distances — shapes embedding space continuously without discrete cluster boundaries
- **Output heads:** polarization regression + IBD binary classification

## In Silico KO Predictions

KO effects for 13 IBD target genes were predicted using two methods:

| Gene | Method | Key Predicted Effect |
|------|--------|---------------------|
| HIF1A | CellOracle (GRN) | PTGS2↓, CXCL8↓, CD74↓ |
| IRF5 | CellOracle (GRN) | ISG15↓, IFI6↓, IFIT3↓, IRF7↓ |
| IL6 | OmniPath propagation | JAK2↓, IL6ST↓, JAK1↓, IL6R↓, TYK2↓ |
| SOCS3 | OmniPath propagation | JAK2↓, STAT5A↓, STAT1↓, STAT3↓; AKT1↑ |
| TNF | OmniPath propagation | TNFRSF1A↓, TNFRSF1B↓, PIK3CG↓, AKT1↓ |
| TGFB1 | OmniPath propagation | PIK3R1↓, RAC1↓, TGFBR1↓, GRB2↓ |
| IL1B | OmniPath propagation | IL1R2↓, MYD88↓, STAT3↓, NR3C1↓ |
| PTGS2 | OmniPath propagation | IL4R↓, IL2RG↓, NFE2L2↓ |

Full predictions (top 15 up/down per gene) are in `results/ibd_ko_predictions_combined.json`.

## Usage

```python
import torch
import json

# Load model weights
checkpoint = torch.load("scgpt_ibd_v2/best_model.pt", map_location="cpu")

# Load KO predictions
with open("results/ibd_ko_predictions_combined.json") as f:
    ko_predictions = json.load(f)

# Example: top downregulated genes after IL6 KO
il6_ko = ko_predictions["IL6"]
print(il6_ko["top_downregulated"][:5])
# [['JAK2', -0.207], ['IL6ST', ...], ['JAK1', ...], ...]
```

## Citation

If you use this model, please cite the underlying datasets and tools:

- **scGPT:** Cui et al., *Nature Methods* 2024
- **CellOracle:** Kamimoto et al., *Nature* 2023
- **OmniPath:** Türei et al., *Nature Methods* 2021
- **GSE134809:** Smillie et al., *Cell* 2019