Omics Data Normalization via Dual Matrix Constraints
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Solution Overview
Problem
Existing normalization methods in omics techniques, such as mass spectrometry and NMR, are insufficient in correcting for systematic errors induced by sample handling and measurement protocols, leading to obscured biological information.
Innovation Solution
A data-driven normalization method that applies scaling and normalization constraints to signal intensities, ensuring the mean of rows and columns in a matrix equals 1/m, allowing for efficient correction of systematic errors and accurate quantification of chemical compounds in pooled samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing normalization methods are used in omics techniques, then the measurement process can be completed, but systematic errors induced by sample handling and measurement protocols are not sufficiently corrected, leading to obscured biological information
Solution Approach 1:
The patent changes the normalization parameters by applying dual constraints (row mean = 1/m and column mean = 1/m) to the signal intensity matrix. This transforms the normalization process from using existing methods to using a mathematically constrained optimization approach that simultaneously corrects systematic errors while preserving biological information through the dual-constraint framework
Solution Approach 2:
The patent implements a feedback mechanism where the normalization process iteratively adjusts the signal intensities based on the deviation from the target means (1/m). The method continuously monitors and corrects systematic errors by comparing the actual row and column means against the target values, applying corrections until the constraints are satisfied, thereby eliminating systematic biases while maintaining biological signal integrity
2Productivity
If samples are pooled for simultaneous measurement, then productivity increases, but systematic errors from sample handling and measurement protocols affect all samples, requiring sophisticated normalization
Solution Approach 1:
The patent merges multiple samples into a single pooled sample for simultaneous measurement, achieving multiplexing and increased productivity. The dual-constraint normalization method then processes the combined signal intensities from all pooled samples, applying row constraints for each compound across samples and column constraints for each sample across compounds, thereby correcting systematic errors that affected all samples during pooling and measurement while maintaining accurate relative quantification
Solution Approach 2:
The normalization method serves multiple functions simultaneously: it corrects systematic errors from sample handling, corrects systematic errors from measurement protocols, enables inter-experimental comparison, and preserves biological information. The dual-constraint framework (row and column means both equal to 1/m) provides a universal solution that handles both intra-experimental and inter-experimental variability in a single mathematical framework
Data Source
AI summary
A method is for the simultaneous identification of one or more chemical compounds contained in a sample, from an analytical measurement of a pool of two or more of the samples. A measured intensity of a first and second signal is representative of an abundance of respectively the first and second chemical compound in the first sample, and a measured intensity of a third and fourth second signal is representative of an abundance of respectively a third and fourth chemical compound in the second sample. The first and third, and the second and fourth compound may be the same or different. The signal intensities are organized in a matrix aij of m columns and n rows, in which n is ≥2 and corresponds to the number of chemical compounds in the pool, and m≥2 and corresponds to the number of samples in the pool.