same-same

same-same estimates experiment-specific inter-replicate quantitative variability from reference sample replicates and derives Q value cut-offs to adjust multiple testing corrections for high-throughput quantitative proteomics experiments.

Key Features:

• Inter-Replicate Variability Estimation: Uses permutation analysis on six biological replicates of a reference sample with non-redundant triplet pairwise comparisons to quantify experimental quantitative noise.
• Integration with Multiple Testing Corrections: Modulates multiple testing corrections such as the Benjamini-Hochberg ordered Q value test by incorporating experimentally derived variability measures.
• Experiment-Specific Q Value Cut-off: Produces experiment-specific Q value cut-offs to achieve specified false discovery rates at the quantitation level (for example 1%).
• Applicability: Applies to any experimental set that includes six replicates of a reference sample, including but not limited to quantitative proteomics studies.
• Implementation: Implemented as an R module.

Scientific Applications:

• Refinement of statistical analyses: Incorporates measured inter-replicate variability to improve detection of true biological signals amid experimental noise in high-throughput experiments.
• Quantitation-level thresholding: Determines significance thresholds in quantitative proteomics to control false discovery rates at the quantitation level.
• Cross-domain applicability: Can be applied in other high-throughput biological studies such as genomics when six reference replicates are available.

Methodology:

Performs permutation analysis on six biological replicates of a reference sample using non-redundant triplet pairwise comparisons to identify quantitative noise and adjusts multiple testing corrections (e.g., Benjamini-Hochberg ordered Q value test) to derive an experiment-specific Q value cut-off that attains the specified false discovery rate.