CSDE: Corrected Spatial Differential Expression

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).

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.

Refer to the preprint and the project repository for more details.

Installation

pip install csde

By default, this installs JAX with CPU support. To enable GPU support (CUDA), install with the appropriate extra (e.g., for CUDA 12):

pip install "csde[cuda12]"

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?).

Usage

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())

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).

How to construct adata_gt?

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.

These steps can be performed manually or using dedicated tools. Our experimental repository provides an example of how these steps were performed for MERFISH data.

To streamline this process, for MERFISH or other spatial transcriptomics data, we recommend using SpatialData to access the data and perform the manual validation.