Module testing 

Statistical testing framework for single-cell data analysis.

This module provides a comprehensive suite of statistical tests and methods specifically designed for single-cell RNA-seq data analysis. It includes parametric and non-parametric tests, multiple testing correction methods, and effect size calculations.

Key Components

• Core Data Structures: TestResult and MultipleTestResults for storing test outcomes
• Test Methods: TestMethod enum defining available statistical tests
• Matrix Operations: [MatrixStatTests] trait for running tests on sparse matrices

Submodules

• correction: Multiple testing correction methods (FDR, Bonferroni, etc.)
• effect: Effect size calculations (Cohen’s d, etc.)
• inference: Core statistical inference implementations
• utils: Utility functions for data preparation and validation

Usage

Use the MatrixStatTests trait on sparse matrices to perform differential expression analysis with various statistical methods and automatic multiple testing correction.

Modules

correction

Multiple testing correction methods for controlling false positives in differential expression analysis.

effect

inference

utils

Utility functions for statistical testing operations.

Structs

MultipleTestResults

Results from multiple statistical tests, typically for differential expression analysis.

TestResult

Result of a single statistical test.

Enums

Alternative

Alternative hypothesis for statistical tests.

TTestType

Type of t-test to perform, differing in variance assumptions.

TestMethod

Statistical test methods available for differential expression analysis.