Merge pull request #2 from YosefLab/importance_weights

Add support for importance weights in run_csde and InterceptRegression.

Author: PierreBoyeau

README.md

@@ -2,85 +2,173 @@
 
 [![Tests](https://github.com/YosefLab/csde/actions/workflows/test.yml/badge.svg)](https://github.com/YosefLab/csde/actions/workflows/test.yml)
 
-`csde` (Corrected Spatial Differential Expression) is a Python package designed to **identify differentially expressed (DE) genes between spatially-resolved cell populations** (e.g., T-cells inside vs. outside a tumor).
+Automated pipelines for spatial transcriptomics produce cell quantifications (cell-by-gene expression matrices and label assignments) that contain systematic errors, e.g., due to mis-segmentation of cell boundaries.
+These errors can propagate into downstream analyses of differential expression, leading to false discoveries or missed signals
 
-Standard analysis relies on cell population assignments (e.g., "infiltrating" vs. "non-infiltrating") obtained automatically from clustering/ML that are often prone to errors. `csde` corrects for these inaccuracies by leveraging a small subset of validated "ground-truth" data, providing rigorous statistical guarantees for spatially-resolved DE analyses.
+CSDE corrects for these errors by combining the large automated dataset with a small set of manually validated cells, using prediction-powered inference to recover unbiased estimates with valid confidence intervals.
 
-Refer to the preprint and the [project repository](https://github.com/YosefLab/csde) for more details.
+The current codebase focuses on the comparison of a given cell type across two spatial regions.
+It allows users to
+1. export per-cell annotation panels for a small subset of cells (e.g. 600)
+2. manually validate the segmentation and type assignment for these cells
+3. run the CSDE model to get corrected DE estimates for all genes
+
+Refer to the [preprint](https://www.biorxiv.org/content/10.64898/2026.01.15.699786v1) for details on the method. Reproducibility code is available [here](https://github.com/PierreBoyeau/csde_experiments).
+
+### Input requirements
+
+The workflow takes a [SpatialData](https://spatialdata.scverse.org/) zarr as input.
+
+Its `"table"` AnnData must contain:
+
+- **raw expression counts** in `.X` or a named layer
+- **the following `obs` columns:**
+
+| obs column | content |
+| --- | --- |
+| `cell_type` (configurable) | cell-type label for each cell |
+| `spatial_group` (configurable) | binary spatial region label (e.g. `0` = outside tumour, `1` = inside tumour) |
+| `center_x`, `center_y` | cell centroid in microns |
+
+The zarr must also expose the following SpatialData elements, used to render the per-cell annotation panels (Step 1):
+
+| element | requirement |
+| --- | --- |
+| `images` | at least one image with a named **fluorescence channel** (e.g. `"DAPI"`, `"Cellbound2"`) |
+| `shapes` | at least one element holding the **cell-boundary polygons** |
+| `points` | at least one element holding **transcript locations**, with a `gene` column |
+
+The cell-boundary `shapes` must carry a transformation to the `global` coordinate system: it converts the micron `center_x`/`center_y` centroids into the image's pixel space. This conversion assumes a pure scale-and-translation transform (as produced for MERSCOPE); transforms with rotation or shear are not handled.
 
 ## Installation
 
 ```bash
 pip install csde
+pip install "csde[cuda12]"          # GPU (CUDA 12)
+pip install "csde[annotate]"        # annotation UI (Step 2, requires streamlit)
+pip install "csde[cuda12,annotate]" # both
 ```
 
-By default, this installs JAX with CPU support. To enable GPU support (CUDA), install with the appropriate extra (e.g., for CUDA 12):
+## Workflow overview
+
+CSDE runs as three scripts executed in sequence, each consuming the previous one's output: `export.py` samples a small set of cells and renders an annotation panel for each, `annotate.py` lets you manually mark those cells as correct or incorrect, and `differential_expression.py` feeds those validated labels into the CSDE model to produce corrected DE estimates. All three share a single annotation directory.
+
+```
+SpatialData zarr
+      │
+      ▼
+1. Export annotation panels   ←─ scripts/export.py
+   (importance-sampled cells,
+    one image per cell)
+      │
+      ▼
+2. Manual validation          ←─ scripts/annotate.py
+   (annotator marks each cell
+    as correctly / incorrectly labelled)
+      │
+      ▼
+3. Run CSDE                   ←─ scripts/differential_expression.py
+   (corrected DE estimates)
+```
+
+---
+
+## Step 1 — Export annotation panels (`scripts/export.py`)
+
+Before running the statistical model, a small subset of cells must be manually validated. `csde` provides tooling to generate the per-cell images needed for that step.
+
 ```bash
-pip install "csde[cuda12]"
+python scripts/export.py \
+--sdata  /path/to/region.zarr \
+--out    /path/to/annotation_dir \
+--cell-type-key cell_type \
+--cell-type-of-interest macrophages \
+--target-proportion 0.4 \
+--gene-colors scripts/gene_colors_file.json \
+--image-channel Cellbound2 \
+--n-cells 600 \
+--layer counts
 ```
 
-## Data Requirements
-
-`csde` requires two `AnnData` objects containing gene expression counts. Typically, these are obtained by splitting your full dataset into two groups:
-
-### 1. `adata_pred`: The dataset to analyze
-This object contains the bulk of your cells (e.g., the majority of the tissue) where only standard (predicted) cell population assignments are available.
-
-**Requirements:**
-*   A column in `.obs` (e.g., `"cell_population"`) containing cell population labels (e.g., "T cell (infiltrating)" vs. "T cell (non-infiltrating)"). These labels can be derived from heuristics (e.g., distance to tumor) and/or computational classifiers.
-
-### 2. `adata_gt`: The correction set
-This object contains a small subset of randomly sampled cells whose cell population assignments have been **validated** to serve as a ground truth. This set allows `csde` to estimate the error rate of the standard predictions.
-
-**Requirements:**
-*   **Prediction column:** The same column name as in `adata_pred` (e.g., `"cell_population"`), containing the automated labels.
-*   **Validation column:** A **boolean** column in `.obs` (e.g., `"is_correct"`) indicating if the automated label matches the validation ground truth (see [How to construct `adata_gt`?](#how-to-construct-adata_gt)).
-
-## Usage
-
-```python
-from csde import run_csde
-
-results = run_csde(
-    # `AnnData` datasets to analyze
-    adata_pred=adata_pred,
-    adata_gt=adata_gt,
-    
-    # Column containing the predicted labels (in BOTH datasets)
-    pred_cell_pop_key="cell_population",
-    
-    # The two populations to compare
-    cell_pop_a="T-cell (infiltrating)",       # Reference group
-    cell_pop_b="T-cell (non-infiltrating)",   # Target group
-    
-    # Boolean column in adata_gt verifying the prediction
-    gt_key="is_correct",
-    
-    # Optional: Use a specific layer for counts (default uses .X)
-    layer_name="counts"
-)
-
-# Returns a DataFrame with log_fold_change, p_value, and adjusted p_value
-print(results.head())
+`--target-proportion` controls the fraction of cells of interest in the subsample. Cells of interest are upweighted accordingly (importance sampling); the unnormalized weight for each sampled cell is stored in `metadata.csv` for downstream use.
+
+`--layer` selects which expression matrix to read: the named `.layers` entry holding the raw counts (e.g. `counts`), or `.X` when omitted. The value is saved to `config.json` and reused throughout the workflow — the same layer feeds the top-gene panels here in Step 1 and the CSDE model in Step 3, so set it once at export time. **It must point at raw counts**, since the noise model (Poisson / negative binomial) assumes integer counts; pointing it at normalised or log-transformed values will produce invalid results.
+
+The script writes:
+
+```
+/path/to/annotation_dir/
+├── images/
+│   ├── cell_<id>.png   # one panel per cell
+│   └── ...
+├── config.json         # all export arguments (read by annotate.py)
+├── metadata.csv        # cell_id, cell_type, image_path, sampling_weight, center_x, center_y
+└── annotations.json    # {cell_id: true/false} — written by annotate.py
 ```
 
-### Output Columns
-The returned DataFrame is indexed by gene name and contains:
-*   `log_fold_change`: The estimated log-fold change of expression (Target vs. Reference). Positive values indicate upregulation in `cell_pop_b`.
-*   `p_value`: The raw p-value from the hypothesis test (two-sided).
-*   `p_value_adj`: The p-value adjusted for multiple testing (Benjamini-Hochberg FDR).
+Each panel contains:
+- **Left** — fluorescence image crop + cell boundaries + transcript dots for genes listed in `gene_colors`
+- **Right** — top expressed genes (bar chart); genes in `gene_colors` use their assigned colour, others are grey
 
-## How to construct `adata_gt`?
+### Gene color file
+
+A simple JSON mapping gene names to colours:
+
+```json
+{
+    "CD68":   "#e41a1c",
+    "MRC1":   "#377eb8",
+    "C1QA":   "#4daf4a",
+    "FCGR3A": "#ff7f00"
+}
+```
+
+---
+
+## Step 2 — Manual validation (`scripts/annotate.py`)
+
+For each exported image, an annotator decides whether the cell is **correct** — meaning it is both properly **segmented** and properly **labelled**. A cell should be rejected (marked incorrect) when either check fails:
+
+- **Segmentation** — the cell boundary (left panel) is not consistent with the nuclei / membrane staining, e.g. it merges two cells or clips part of one.
+- **Cell-type label** — the top expressed genes (right panel) include genes unlikely to be expressed by the cell type of interest, suggesting the automated label is wrong.
+
+The result is a boolean column `is_correct` added to `metadata.csv`, which becomes `adata_gt` in Step 3.
+
+```bash
+streamlit run scripts/annotate.py -- --dir /path/to/annotation_dir
+```
+
+The `--` is required: it tells Streamlit to pass everything after it to the script rather than interpreting it as Streamlit's own options.
+
+VS Code Remote forwards the Streamlit port automatically. Open the URL printed in the terminal, then use:
+
+- **`1`** — label as correct
+- **`2`** — label as incorrect
+
+Progress is saved after every keypress to `annotations.json`. Re-running the command resumes from where you left off. You can also start annotating while `export.py` is still running — the UI picks up newly exported cells automatically.
+
+---
+
+## Step 3 — Differential expression (`scripts/differential_expression.py`)
+
+```bash
+python scripts/differential_expression.py --dir /path/to/annotation_dir
+```
 
-Constructing `adata_gt` requires validating the cell population labels for a small subset of cells (e.g., random sample). This involves:
-1.  **Sampling**: Select a small random subset of cells from your dataset.
-2.  **Data Access**: Extract the relevant data for these cells: their gene expression profile, their spatial coordinates, and importantly, a **high-resolution image crop** of the cell (with segmentation boundaries if available) to assess morphology.
-3.  **Validation**: Visually inspect these data points to determine the true cell identity.
-4.  **Annotation**: Create the `is_correct` boolean column based on your assessment.
+Reads all export settings from `config.json` and writes gene-level results to `<dir>/results.csv`.
 
-These steps can be performed manually or using dedicated tools.
-Our [experimental repository](https://github.com/YosefLab/csde/blob/main/csde_experiments)
-provides an example of how these steps were performed for MERFISH data.
+| option | default | description |
+|---|---|---|
+| `--dir` | *(required)* | annotation directory (output of steps 1 & 2) |
+| `--out` | `<dir>/results.csv` | output CSV path |
+| `--spatial-group-key` | `spatial_group` | obs column encoding the two spatial populations |
+| `--n-cells-expressed-threshold` | `10` | min annotated cells expressing a gene for it to be tested |
+| `--noise-model` | `poisson` | `poisson` or `nb` (negative binomial) |
 
-To streamline this process, for MERFISH or other spatial transcriptomics data, we recommend using **[SpatialData](https://spatialdata.scverse.org/)** to access the data and perform the manual validation.
+### Output columns
 
+| column | description |
+|---|---|
+| `log_fold_change` | estimated LFC (positive = upregulated in target population) |
+| `p_value` | raw two-sided p-value |
+| `p_value_adj` | Benjamini-Hochberg adjusted p-value |

pyproject.toml

@@ -33,6 +33,7 @@ dev = [
     "isort",
     "flake8",
 ]
+annotate = ["streamlit"]
 cuda12 = ["jax[cuda12]"]
 cuda13 = ["jax[cuda13]"]
 

scripts/annotate.py

@@ -0,0 +1,153 @@
+"""
+Streamlit annotation UI for per-cell panels.
+
+Usage
+-----
+streamlit run scripts/annotate.py -- --dir /path/to/annotations/R2_macrophages
+"""
+
+import argparse
+import json
+from pathlib import Path
+
+import pandas as pd
+import streamlit as st
+import streamlit.components.v1 as components
+
+
+def parse_args():
+    p = argparse.ArgumentParser()
+    p.add_argument("--dir", required=True, help="Annotation directory (output of export.py).")
+    return p.parse_args()
+
+
+def load_annotations(annotation_dir: Path) -> dict:
+    ann_path = annotation_dir / "annotations.json"
+    if ann_path.exists():
+        with open(ann_path) as f:
+            return json.load(f)
+    return {}
+
+
+def save_annotations(annotations: dict, annotation_dir: Path) -> None:
+    with open(annotation_dir / "annotations.json", "w") as f:
+        json.dump(annotations, f, indent=2)
+
+
+def main():
+    args = parse_args()
+    annotation_dir = Path(args.dir)
+
+    cell_type_of_interest = "cell of interest"
+    config_path = annotation_dir / "config.json"
+    if config_path.exists():
+        with open(config_path) as f:
+            config = json.load(f)
+        cell_type_of_interest = config.get("cell_type_of_interest", cell_type_of_interest)
+
+    st.set_page_config(layout="wide", page_title="Cell Annotator")
+    st.title(f"Cell Annotation — {cell_type_of_interest}")
+
+    metadata_path = annotation_dir / "metadata.csv"
+    if not metadata_path.exists():
+        st.warning("metadata.csv not found — waiting for export.py to write the first cell.")
+        st.stop()
+
+    metadata = pd.read_csv(metadata_path)
+    metadata["cell_id"] = metadata["cell_id"].astype(str)
+    n_total = len(metadata)
+
+    annotations = load_annotations(annotation_dir)
+    n_done = len(annotations)
+
+    st.progress(n_done / n_total, text=f"{n_done} / {n_total} annotated")
+
+    # Initialize navigation index to first unannotated cell
+    if "current_idx" not in st.session_state:
+        unannotated_mask = ~metadata["cell_id"].isin(annotations)
+        first_unannotated = unannotated_mask.idxmax() if unannotated_mask.any() else 0
+        st.session_state.current_idx = int(first_unannotated)
+
+    idx = st.session_state.current_idx
+
+    # Jump-to input (form so Enter submits without looping)
+    jump_col, nav_col = st.columns([2, 3])
+    with jump_col:
+        with st.form("jump_form", clear_on_submit=True):
+            fc1, fc2 = st.columns([4, 1])
+            with fc1:
+                jump_id = st.text_input("Jump to cell ID", placeholder="paste cell_id here", label_visibility="collapsed")
+            with fc2:
+                submitted = st.form_submit_button("Go")
+        if submitted and jump_id.strip():
+            matches = metadata.index[metadata["cell_id"] == jump_id.strip()].tolist()
+            if matches:
+                st.session_state.current_idx = matches[0]
+                idx = matches[0]
+            else:
+                st.warning(f"Cell ID `{jump_id.strip()}` not found.")
+
+    with nav_col:
+        nav1, nav2, nav3 = st.columns([1, 3, 1])
+        with nav1:
+            if st.button("← Prev", use_container_width=True, disabled=(idx == 0)):
+                st.session_state.current_idx = idx - 1
+                st.rerun()
+        with nav2:
+            st.markdown(f"<div style='text-align:center; padding-top:6px'>{idx + 1} / {n_total}</div>", unsafe_allow_html=True)
+        with nav3:
+            if st.button("Next →", use_container_width=True, disabled=(idx == n_total - 1)):
+                st.session_state.current_idx = idx + 1
+                st.rerun()
+
+    row = metadata.iloc[idx]
+    cell_id = row["cell_id"]
+    existing = annotations.get(cell_id)
+    status = "✓ correct" if existing is True else ("✗ incorrect" if existing is False else "not annotated")
+
+    st.subheader(f"Cell `{cell_id}` — predicted: **{row['cell_type']}** — {status}")
+    st.image(str(row["image_path"]), use_container_width=True)
+
+    def annotate(is_correct: bool) -> None:
+        annotations[cell_id] = is_correct
+        save_annotations(annotations, annotation_dir)
+        # Advance to next unannotated after annotating
+        remaining = metadata.index[~metadata["cell_id"].isin(annotations)]
+        next_idx = next((i for i in remaining if i > idx), None)
+        if next_idx is not None:
+            st.session_state.current_idx = int(next_idx)
+        elif remaining.any():
+            st.session_state.current_idx = int(remaining[0])
+
+    col1, col2, _ = st.columns([1, 1, 4])
+    with col1:
+        if st.button("✓ Correct [1]", type="primary", use_container_width=True):
+            annotate(True)
+            st.rerun()
+    with col2:
+        if st.button("✗ Incorrect [2]", use_container_width=True):
+            annotate(False)
+            st.rerun()
+
+    # Keyboard shortcuts: 1/2 annotate, ←/→ navigate
+    components.html("""
+    <script>
+    (function() {
+        const doc = window.parent.document;
+        doc.addEventListener('keydown', function(e) {
+            if (e.target.tagName === 'INPUT' || e.target.tagName === 'TEXTAREA') return;
+            doc.querySelectorAll('button').forEach(function(btn) {
+                const t = btn.textContent.trim();
+                if (e.key === '1' && t.startsWith('✓')) btn.click();
+                if (e.key === '2' && t.startsWith('✗')) btn.click();
+                if (e.key === 'ArrowLeft'  && t.startsWith('←')) btn.click();
+                if (e.key === 'ArrowRight' && t.startsWith('Next')) btn.click();
+            });
+        }, true);
+    })();
+    </script>
+    """, height=0)
+
+
+if __name__ == "__main__":
+    main()