ScaledMatrix

ScaledMatrix provides delayed computation of a matrix of scaled and centered values, avoiding explicit realization of a dense matrix during block processing to improve efficiency in common operations such as matrix multiplication.

Key Features:

• Delayed Computation: Computes scaled and centered values without explicitly realizing a dense matrix in memory.
• Block Processing: Operates on matrix blocks to reduce memory overhead during large-scale computations.
• Equivalent to scale() Function: Produces results identical to the standard scale() function while improving computational efficiency.
• Optimized Matrix Multiplication: Enables greater efficiency in matrix multiplication and other common linear algebra operations.

Scientific Applications:

• Large-Scale Genomic Data Analysis: Processes large matrices efficiently in bioinformatics workflows where dense matrix realization would be memory-prohibitive.
• Single-Cell Transcriptomics: Supports scaling and centering operations on large expression matrices typical in single-cell RNA sequencing analyses.

Methodology:

ScaledMatrix implements delayed computation using block processing to calculate scaled and centered values equivalent to the scale() function, avoiding explicit dense matrix realization.