Skip to content

What is Sample Comparison?

Multi-Sample Session Specific

This feature is only available in multi-sample sessions.

Sample Comparison: the statistical comparative framework in CartoScope for evaluating differential expression, spatial factor abundance shifts, and cell-type-specific transcriptional changes between two groups of samples (Group 1 vs. Group 2) in a multi-sample session.

Sample Size Requirements & Limitations

While CartoScope can provide real-time differential metrics between sample groups, we recommend having at least 3 samples per group for a statistically robust comparative study.

Statistical Options

  • Method Options

    Method Model / Rationale Best Suited For Advantages
    Chi-Squared Test (chisq) Contingency test of transcript counts and spatial pixel proportion differences between groups. Fast screening, single-slide contrasts, or large spatial spot/hex datasets. Instant execution, computationally lightweight, effective for spatial proportion shifts.
    DESeq2-inspired Model (deseq2) An in-browser DESeq2-inspired method designed for instant interactive web execution. It implements DESeq2's Median-of-Ratios size factor normalization and Negative Binomial Wald testing directly without requiring an R/Bioconductor backend. See DESeq2-inspired Model for more details. Multi-replicate biological cohorts. Accounts for sample-to-sample variance in browser runtime; prevents false positives from single over-expressed slides.

  • Multiple Testing Corrections Options

    • Benjamini-Hochberg (BH FDR): Controls the False Discovery Rate (FDR). Recommended for discovery workflows and ranking differentially expressed genes.
    • Šidák (FWER): Controls the Family-Wise Error Rate (FWER). Provides strict error rate bounds for high-confidence validation.

  • Analysis Scopes

    • Gene Expression Comparison: Tests overall differential expression of genes between Group 1 and Group 2 across all selected slides.
    • Factor Weight Comparison: Tests differential abundance of spatial factors (e.g. spatial domain or cell-type representation) across slide cohorts.
    • Conditional Differential Expression: Tests gene expression changes conditioned within a specific spatial factor domain (e.g. evaluating gene expression changes in Factor 5 between Control vs. Treated samples).

Why use sample comparison?

  • Identify Differential Features: Discover genes and spatial factors significantly enriched or depleted between sample cohorts.
  • Cell-Type Specific Insights: Separate overall cell-type abundance shifts from cell-type-specific transcriptional responses using conditional DE.
  • Interactive Exploration: Visualize fold-changes (log2 FC) against statistical significance (-log10 FDR) on an interactive Volcano Plot with hover tooltips and filtering.

Related Pages

Concepts

Manual Pages

How-to Guides