Update and Major fixes to the Model and Pathway Loss

README.md

@@ -62,11 +62,11 @@ stf-download --species "Homo sapiens" --local_dir hest_data
 We provide presets for baseline models and scaled versions of the SpatialTranscriptFormer.
 
 ```bash
-# Recommended: Run the Interaction model with 4 transformer layers
-python scripts/run_preset.py --preset stf_interaction_l4
+# Recommended: Run the Interaction model (Small)
+python scripts/run_preset.py --preset stf_small
 
-# Run the lightweight 2-layer version
-python scripts/run_preset.py --preset stf_interaction_l2
+# Run the lightweight Tiny version
+python scripts/run_preset.py --preset stf_tiny
 
 # Run baselines
 python scripts/run_preset.py --preset he2rna_baseline

docs/MODELS.md

@@ -34,12 +34,8 @@ The SpatialTranscriptFormer models the **interaction between biological pathways
 By default, the model operates in **Full Interaction** mode where all four information flows are active. Users can selectively disable any combination using the `--interactions` flag to explore architectural variants:
 
 ```bash
-# Default: Full Interaction (all quadrants enabled)
---interactions p2p p2h h2p h2h
-
-# Pathway Bottleneck: block H↔H to force all inter-patch
-# communication through the pathway bottleneck
---interactions p2p p2h h2p
+# Default: Small Interaction (CTransPath, 4 layers)
+python scripts/run_preset.py --preset stf_small
 ```
 
 > [!TIP]
@@ -53,7 +49,7 @@ Three additional design principles support these interactions:
 
 - **Biological Initialisation** — The gene reconstruction weights are initialised from MSigDB Hallmark gene sets, providing a biologically-grounded starting point that the model refines during training.
 
-### 2.2 Spatial Learning
+## 2.2 Spatial Learning
 
 The spatial relationships of gene expression are central to this model. It is not sufficient to predict correct expression magnitudes at each spot independently — the model must capture **where** on the tissue pathways are active and how that spatial pattern varies across the slide. Two mechanisms enforce this:
 
@@ -218,14 +214,19 @@ The model outputs these parameters, and the loss computes the negative log-likel
 
 To prevent bottleneck collapse and provide a direct gradient signal to the pathway tokens, we use the `AuxiliaryPathwayLoss`. This loss compares the model's internal pathway scores against "ground truth" pathway activations computed from the gene expression targets via MSigDB membership.
 
+To prevent highly-expressed housekeeping genes from dominating the pathway's spatial pattern, the ground-truth targets are computed using **Z-score spatial normalization**:
+
+1. Every gene's spatial expression pattern is standardized (mean=0, variance=1) across the tissue slide.
+2. The normalized genes are projected onto the binary MSigDB pathway matrix.
+3. The resulting pathway scores are **mean-aggregated** (divided by the number of known member genes in each pathway) rather than raw-summed.
+
+This ensures every gene—including critical but lowly-expressed transcription factors—gets an equal vote in determining where a pathway is active.
+
 The total objective becomes:
 $$\mathcal{L} = \mathcal{L}_{gene} + \lambda_{aux} (1 - \text{PCC}(\text{pathway\_scores}, \text{target\_pathways}))$$
 
 The `--log-transform` flag applies `log1p` to targets, mitigating the heavy-tailed gene expression distribution where housekeeping genes dominate MSE.
 
-The full training objective with pathway sparsity regularisation:
-$$\mathcal{L} = \mathcal{L}_{task} + \lambda \|W_{recon}\|_1$$
-
 ---
 
 ## 5. CLI Flags (Model Configuration)
@@ -239,7 +240,6 @@ $$\mathcal{L} = \mathcal{L}_{task} + \lambda \|W_{recon}\|_1$$
 | `--n-layers` | 2 | Transformer layers (minimum 2) |
 | `--num-pathways` | 50 | Number of pathway bottleneck tokens |
 | `--pathway-init` | off | Initialize gene_reconstructor from MSigDB |
-| `--sparsity-lambda` | 0.0 | L1 regularisation on reconstruction weights |
 | `--loss mse_pcc` | `mse` | Loss function (`mse`, `pcc`, `mse_pcc`, `zinb`) |
 | `--pcc-weight` | 1.0 | Weight for PCC term in composite loss |
 | `--pathway-loss-weight` | 0.0 | Weight for auxiliary pathway loss ($\lambda_{aux}$) |

docs/PATHWAY_MAPPING.md

@@ -35,17 +35,11 @@ In this mode, the network receives direct supervision on its pathway tokens, gui
     1. **Interaction**: Learnable pathway tokens $P$ interact with Histology patch features $H$ via self-attention (e.g., $p2h$, $h2p$).
     2. **Activation**: Pathway scores $S \in \mathbb{R}^P$ are computed using a learnable temperature-scaled cosine similarity between the pathway tokens and image patch tokens.
     3. **Gene Reconstruction**: $\hat{y} = S \cdot \mathbf{W}_{recon} + b$, where $\mathbf{W}_{recon}$ is initialized using the binary pathway membership matrix $M$.
-- **MTL Auxiliary Loss**: To prevent standard bottleneck collapse, an explicit auxiliary loss bridges the spatial representations directly to biological data. The pathway scores $S$ are supervised against a pathway ground truth ($Y_{genes} \cdot M^T$) using a Pearson Correlation Coefficient (PCC) loss.
-  $$L_{total} = L_{gene} + \lambda_{pathway} (1 - PCC(S, Y_{genes} \cdot M^T))$$
-- **Benefit**: The model is forced to explicitly align its internal interaction tokens with concrete biological pathways, granting direct interpretability.
-
-#### 2. Data-Driven Discovery (Latent Projection)
-
-In the absence of a biological prior, the model can learn its own "latent pathways".
-
-- **Implementation**: $\mathbf{W}_{recon}$ is randomly initialized and the auxiliary pathway loss is disabled.
-- **Sparsity Constraint**: We apply an L1 penalty to force the model to identify "canonical" sparse gene sets: $L_{total} = L_{gene} + \lambda_{sparsity} \|\mathbf{W}_{recon}\|_1$.
-- **Benefit**: Can discover novel spatial-transcriptomic relationships that aren't yet captured in curated databases.
+- **MTL Auxiliary Loss**: To prevent standard bottleneck collapse, an explicit auxiliary loss bridges the spatial representations directly to biological data. The pathway scores $S$ are supervised against a pathway ground truth using a Pearson Correlation Coefficient (PCC) loss.
+  - To prevent highly expressed housekeeping genes dominating the signal, the raw spatial gene counts ($Y_{genes}$) are first **spatially Z-score normalized** ($Z_{genes}$).
+  - These are then projected onto the pathway matrix and mean-aggregated by member count ($C$):
+  $$L_{total} = L_{gene} + \lambda_{pathway} (1 - PCC(S, \frac{Z_{genes} \cdot M^T}{C}))$$
+- **Benefit**: The model is forced to explicitly align its internal interaction tokens with concrete biological pathways, granting direct interpretability where every gene gets an equal vote.
 
 ## 3. Generalizing to HEST1k Tissues
 
@@ -73,18 +67,12 @@ By supplying these functional groupings via `--custom-gmt`, the model's MTL proc
   - GMT file cached in `.cache/` after first download.
 - **Custom Pathway Definitions** (`--custom-gmt` flag): Users can override the default Hallmarks by providing a URL or local path to a `.gmt` file, enabling custom database integrations (e.g., KEGG, Reactome, or highly specific tissue masks).
 
-- **Sparsity Regularization** (`--sparsity-lambda` flag): L1 penalty on `gene_reconstructor` weights to encourage pathway-like groupings when using data-driven (random) initialization.
-
 ### Usage
 
 ```bash
 # With biological initialization (50 MSigDB Hallmarks)
 python -m spatial_transcript_former.train \
     --model interaction --pathway-init ...
-
-# With data-driven pathways + sparsity
-python -m spatial_transcript_former.train \
-    --model interaction --num-pathways 50 --sparsity-lambda 0.01 ...
 ```
 
 - **Spatial Pathway Maps**: Visualize pathway activations as spatial heatmaps overlaid on histology using `stf-predict`. See the [README](../README.md) for inference instructions.

docs/TRAINING_GUIDE.md

@@ -126,44 +126,22 @@ python -m spatial_transcript_former.train \
 
 > **Note**: `--pathway-init` overrides `--num-pathways` to 50 (the number of Hallmark gene sets). The GMT file is cached in `.cache/` after first download.
 
-### Data-Driven Discovery (Latent Pathways)
+### Recommended: Using Presets
 
-To allow the model to discover its own spatial-transcriptomic relationships without biological priors, omit `--pathway-init` and apply sparsity regularization (`--sparsity-lambda`). This aims to force the model to identify "canonical" sparse gene sets.
+For most cases, it is recommended to use the provided presets:
 
 ```bash
-python -m spatial_transcript_former.train \
-    --data-dir A:\hest_data \
-    --model interaction \
-    --backbone ctranspath \
-    --use-nystrom \
-    --num-pathways 50 \
-    --sparsity-lambda 0.01 \
-    --precomputed \
-    --whole-slide \
-    --use-amp \
-    --log-transform \
-    --epochs 100
-```
-
-> **Note**: Without `--pathway-init`, the model disables the `AuxiliaryPathwayLoss` and relies entirely on the main reconstruction objectives and the L1 sparsity penalty. (I am yet to obtain results with this method)...
+# Tiny (2 layers, 256 dim)
+python scripts/run_preset.py --preset stf_tiny
 
-### Robust Counting: ZINB + Auxiliary Loss
+# Small (4 layers, 384 dim) - Recommended
+python scripts/run_preset.py --preset stf_small
 
-For raw count data with high sparsity, using the ZINB distribution and auxiliary pathway supervision is recommended.
+# Medium (6 layers, 512 dim)
+python scripts/run_preset.py --preset stf_medium
 
-```bash
-python -m spatial_transcript_former.train \
-    --data-dir A:\hest_data \
-    --model interaction \
-    --backbone ctranspath \
-    --pathway-init \
-    --loss zinb \
-    --pathway-loss-weight 0.5 \
-    --lr 5e-5 \
-    --batch-size 4 \
-    --whole-slide \
-    --precomputed \
-    --epochs 200
+# Large (12 layers, 768 dim)
+python scripts/run_preset.py --preset stf_large
 ```
 
 ### Choosing Interaction Modes
@@ -201,7 +179,7 @@ Submit with:
 sbatch hpc/array_train.slurm
 ```
 
-### Collecting Results
+### Collecting Results (Currently broken!)
 
 After experiments complete, aggregate all `results_summary.json` files into a comparison table:
 
@@ -243,8 +221,8 @@ python -m spatial_transcript_former.train --resume --output-dir runs/my_experime
 | `--feature-dir` | Explicit path to precomputed features directory. | Overrides auto-detection. |
 | `--loss` | Loss function: `mse`, `pcc`, `mse_pcc`, `zinb`. | `mse_pcc` or `zinb` recommended. |
 | `--pathway-loss-weight` | Weight ($\lambda$) for auxiliary pathway supervision. | Set `0.5` or `1.0` with `interaction` model. |
-| `--sparsity-lambda` | L1 regularization weight for discovering latent pathways. | Use `0.01` when `--pathway-init` is NOT used. |
 | `--interactions` | Enabled attention quadrants: `p2p`, `p2h`, `h2p`, `h2h`. | Default: `all` (Full Interaction). |
+| `--plot-pathways-list` | Names of explicitly requested pathways to visualize as heatmaps during periodic validation. | Use with `--plot-pathways`. e.g. `HYPOXIA ANGIOGENESIS` |
 | `--log-transform` | Apply log1p to gene expression targets. | Recommended for raw count data. |
 | `--num-genes` | Number of HVGs to predict (default: 1000). | Match your `global_genes.json`. |
 | `--mask-radius` | Euclidean distance for spatial attention gating. | Usually between 200 and 800. |

scripts/analyze_expression_variance.py

@@ -0,0 +1,95 @@
+import os
+import argparse
+import numpy as np
+import h5py
+import matplotlib.pyplot as plt
+import pandas as pd
+import json
+
+
+def analyze_sample(h5ad_path):
+    print(f"Analyzing {h5ad_path}...")
+
+    with h5py.File(h5ad_path, "r") as f:
+        # Check standard AnnData structure
+        if "X" in f:
+            if isinstance(f["X"], h5py.Group):
+                # Sparse format (CSR/CSC)
+                data_group = f["X"]["data"][:]
+                n_cells = (
+                    f["obs"]["_index"].shape[0]
+                    if "_index" in f["obs"]
+                    else len(f["obs"])
+                )
+                n_genes = (
+                    f["var"]["_index"].shape[0]
+                    if "_index" in f["var"]
+                    else len(f["var"])
+                )
+
+                print(f"Data is sparse, shape: ({n_cells}, {n_genes})")
+                print(f"Non-zero elements: {len(data_group)}")
+
+                # Analyze non-zero elements
+                mean_val = np.mean(data_group)
+                max_val = np.max(data_group)
+                min_val = np.min(data_group)
+
+                print(f"Non-zero Mean: {mean_val:.4f}")
+                print(f"Max Expression: {max_val:.4f}")
+                print(f"Min Expression: {min_val:.4f}")
+
+            else:
+                # Dense array
+                X = f["X"][:]
+                print(f"Data is dense, shape: {X.shape}")
+
+                # Basic stats
+                mean_exp = np.mean(X, axis=0)  # per gene mean
+                var_exp = np.var(X, axis=0)  # per gene variance
+                max_exp = np.max(X, axis=0)
+
+                sparsity = np.sum(X == 0) / X.size
+                print(f"Overall Sparsity (zeros): {sparsity:.2%}")
+
+                print(
+                    f"Gene Mean Range: {np.min(mean_exp):.4f} to {np.max(mean_exp):.4f}"
+                )
+                print(f"Gene Var Range: {np.min(var_exp):.4f} to {np.max(var_exp):.4f}")
+                print(f"Overall Max Expression: {np.max(max_exp):.4f}")
+
+                # Check for extreme differences in variance
+                var_ratio = np.max(var_exp) / (np.min(var_exp) + 1e-8)
+                print(f"Ratio of max/min gene variance: {var_ratio:.4e}")
+
+                return {
+                    "sparsity": sparsity,
+                    "var_ratio": var_ratio,
+                    "max_exp": np.max(max_exp),
+                }
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--data-dir",
+        type=str,
+        default="A:\\hest_data",
+        help="Path to HEST data directory",
+    )
+    args = parser.parse_args()
+
+    st_dir = os.path.join(args.data_dir, "st")
+    if not os.path.exists(st_dir):
+        print(f"Error: Directory not found: {st_dir}")
+        exit(1)
+
+    # Get a few random samples
+    samples = [f for f in os.listdir(st_dir) if f.endswith(".h5ad")]
+    if not samples:
+        print(f"No .h5ad files found in {st_dir}")
+
+    # Analyze the first couple of samples
+    for sample in samples[:3]:
+        analyze_sample(os.path.join(st_dir, sample))
+        print("-" * 50)