ccImpute v1.7.1

• Performance Optimizations:
  • Significantly enhanced calculation speed for Pearson and Spearman
    correlation matrices, including weighted versions.
  • Leveraged the Irlba package for efficient truncated Singular Value
    Decomposition (SVD) computation.
  • Optimized imputation by limiting the number of singular components while
    maintaining the accuracy of downstream analysis, with adjustable maximum
    limits based on dataset size.
  • Optimized the identification of dropout events.
  • Introduced a fast dropout calculation method based on non-zero expression
    value means, preserving imputation performance and greatly improving
    runtime speed.
  • Replaced SIMLR with Tracy-Widom Bound for estimating k when not provided,
    resulting in faster calculations and improved empirical performance.
• Expanded Functionality:
  • Added support for sparse matrices in dgCmatrix format, allowing increased memory
    efficiency.
• Documentation Enhancements:
  • Expanded the package manual with detailed guidance and practical examples for
    maximizing the package's value and computational speed.
  • Included comparative benchmarking against previous release in the
    package manual, demonstrating the performance improvements.
• Overall Impact:
  • The ccImpute package is now substantially faster and more efficient.
  • Users can expect a smoother experience with improved documentation and
    expanded functionality.

Full Changelog: https://github.com/khazum/ccImpute/commits/v1.6.1

Full Changelog: v1.6.1...v1.7.1