Trait MatrixStatTests 

pub trait MatrixStatTests<T>
where
    T: FloatOpsTS,
{
    // Required methods
    fn t_test(
        &self,
        group1_indices: &[usize],
        group2_indices: &[usize],
        test_type: TTestType,
    ) -> Result<Vec<TestResult<f64>>>;
    fn mann_whitney_test(
        &self,
        group1_indices: &[usize],
        group2_indices: &[usize],
        alternative: Alternative,
    ) -> Result<Vec<TestResult<f64>>>;
    fn differential_expression(
        &self,
        group_ids: &[usize],
        test_method: TestMethod,
    ) -> Result<MultipleTestResults<f64>>;
}

Statistical testing methods for sparse matrices, particularly suited for single-cell data.

This trait extends sparse matrix types (like CsrMatrix) with statistical testing capabilities. It provides methods for differential expression analysis and other statistical comparisons optimized for single-cell RNA-seq data.

Matrix Format

The expected matrix format is genes × cells (features × observations), where:

• Rows represent genes/features
• Columns represent cells/observations
• Values represent expression levels (normalized counts, log-transformed, etc.)

Required Methods

fn t_test( &self, group1_indices: &[usize], group2_indices: &[usize], test_type: TTestType, ) -> Result<Vec<TestResult<f64>>>

Perform t-tests comparing two groups of cells for all genes.

Runs Student’s or Welch’s t-test for each gene (row) in the matrix, comparing expression levels between the specified groups of cells (columns).

Arguments

• group1_indices - Column indices for the first group of cells
• group2_indices - Column indices for the second group of cells
• test_type - Type of t-test (Student or Welch)

Returns

Vector of TestResult objects, one per gene, containing test statistics and p-values.

fn mann_whitney_test( &self, group1_indices: &[usize], group2_indices: &[usize], alternative: Alternative, ) -> Result<Vec<TestResult<f64>>>

Perform Mann-Whitney U tests comparing two groups of cells for all genes.

Runs non-parametric Mann-Whitney U (Wilcoxon rank-sum) tests for each gene, comparing the distributions between the specified groups.

Arguments

• group1_indices - Column indices for the first group of cells
• group2_indices - Column indices for the second group of cells
• alternative - Type of alternative hypothesis (two-sided, less, greater)

Returns

Vector of TestResult objects containing U statistics and p-values.

fn differential_expression( &self, group_ids: &[usize], test_method: TestMethod, ) -> Result<MultipleTestResults<f64>>

Comprehensive differential expression analysis with multiple testing correction.

This is the main method for differential expression analysis. It performs the specified statistical test on all genes and applies multiple testing correction to control the false discovery rate.

Arguments

• group_ids - Vector assigning each cell to a group (currently supports 2 groups)
• test_method - Statistical test to perform

Returns

MultipleTestResults containing statistics, p-values, adjusted p-values, and metadata for all genes.

Implementations on Foreign Types

impl<T> MatrixStatTests<T> for CsrMatrix<T>

where T: FloatOpsTS, f64: From<T>,

fn t_test( &self, group1_indices: &[usize], group2_indices: &[usize], test_type: TTestType, ) -> Result<Vec<TestResult<f64>>>

fn mann_whitney_test( &self, group1_indices: &[usize], group2_indices: &[usize], alternative: Alternative, ) -> Result<Vec<TestResult<f64>>>

fn differential_expression( &self, group_ids: &[usize], test_method: TestMethod, ) -> Result<MultipleTestResults<f64>>

Implementors