Performing Selection on a Monotonic Function in Lieu of Sorting Using Layer-Ordered Heaps.

Area of Science:

• Computational biology
• Bioinformatics
• Statistical analysis

Background:

• Nonparametric statistical tests are crucial in scientific experiments but often bottlenecked by the O(n log n) time complexity of sorting.
• Current methods require full data sorting to partition values based on statistical significance thresholds, which is computationally expensive for large datasets.
• Linear-time selection algorithms are not directly applicable to transformed significance values (e.g., p-values, q-values) computed at an unknown threshold.

Purpose of the Study:

• To develop a novel method for efficient selection and partitioning of transformed statistical significance values.
• To overcome the computational bottleneck associated with sorting in nonparametric statistical tests.
• To accelerate postprocessing algorithms in mass-spectrometry-based proteomics.

Main Methods:

• Introduced a layer-ordering-based method for selection and partitioning on transformed significance values.
• Utilized layer-ordered heaps, constructible in O(n) time, for partial sorting.
• Applied the method to partition peptides using a false discovery rate (FDR) threshold.

Main Results:

• The layer-ordering method enables selection and partitioning on transformed values without full sorting.
• Demonstrated successful application in partitioning peptides based on FDR thresholds.
• Achieved a >70% speedup for the Percolator algorithm on datasets with 100 million peptide-spectrum matches (PSMs).

Conclusions:

• The proposed layer-ordering-based method offers a significant computational advantage over traditional sorting for statistical tests.
• This approach effectively addresses the bottleneck in analyzing large biological datasets, particularly in proteomics.
• The method enhances the efficiency of algorithms like Percolator, enabling faster evaluation of peptide-spectrum match quality.