Biological Probe Array Feature Intensity Reconstruction
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Solution Overview
Problem
Existing image processing methods for biological arrays face challenges in accurately determining feature intensities from pixel-level data, particularly due to non-uniform intensity profiles and confounding error terms like DNA concentration and hybridization efficiency, which affect the precision of gene expression analysis.
Innovation Solution
A method that involves obtaining sample image data from biological probe arrays, determining theoretical pixel intensity using a transfer function, optimizing a multiplicative error function, and iteratively updating feature intensity using a weight function to converge on a unique value, while minimizing assumptions about intensity linearity and shape.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to determine feature intensities from pixel-level data, then the process is simple and fast, but the accuracy is reduced due to non-uniform intensity profiles and confounding error terms
Solution Approach 1:
The patent applies preliminary action by performing background subtraction and normalizing pixel values before calculating feature intensities. This preprocessing step removes confounding error terms and non-uniform intensity profiles early in the process, improving measurement accuracy without adding significant complexity to the overall system.
Solution Approach 2:
The patent implements feedback through an iterative optimization process where initial feature intensity estimates are refined by comparing predicted pixel values with actual observed pixel values. The algorithm adjusts intensity estimates based on the difference between predicted and observed values, progressively improving accuracy through multiple refinement cycles.
2Reliability
If minimal assumptions about intensity linearity and shape are made, then the method is more robust and accurate, but the computational complexity increases due to iterative optimization
Solution Approach 1:
The patent applies partial action by making reasonable but limited assumptions about intensity distribution (e.g., that background pixels have uniform intensity) rather than attempting to model all possible variations. This approach achieves robustness for practical applications while avoiding the excessive computational complexity that would result from fully general models.
Solution Approach 2:
The patent utilizes parameter changes by transforming the optimization problem into a form where the objective function can be efficiently minimized. By changing the mathematical representation of the intensity model and using iterative reweighting, the algorithm achieves convergence to accurate solutions without requiring prohibitively complex computations.
3Measurement precision
If confounding error terms like DNA concentration and hybridization efficiency are not accounted for, then the analysis is simpler, but the precision of gene expression analysis deteriorates
Solution Approach 1:
The patent extracts and separates confounding error terms from the feature intensity measurement process. By identifying and removing the influences of DNA concentration variations and hybridization efficiency differences through background subtraction and normalization, the method isolates the true gene expression signal, improving precision without requiring complex additional measurements.
Data Source
AI summary
The invention provides methods and systems for reconstructing feature intensities from pixel level data. In certain embodiments, the invention uses an empirically determined transfer function to construct a theoretical estimate of pixel level data and then iteratively updates feature intensities based on a minimum multiplicative error between the pixel level data and the theoretical estimate of the pixel level data.