Sequencing Intensity Normalization via Transfer Functions
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Solution Overview
Problem
Next-generation sequencing (NGS) technologies face challenges in accurately deducing nucleotide sequences due to channel-specific bias and noise, which current correction methods cannot fully address, especially when unknown phenomena introduce systematic offsets and fluctuations.
Innovation Solution
A method for normalizing intensity values by parametrizing and combining channel-specific intensity distributions to create a common distribution, using transfer functions to map each channel's intensity to this common distribution, thereby reducing bias and noise effects without requiring knowledge of the specific biasing phenomena, and applying smoothing functions across cycles to maintain consistent intensity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-based correction approaches (background correction and crosstalk correction) are used, then known bias phenomena can be corrected, but unknown biasing phenomena cannot be addressed
Solution Approach 1:
The system performs self-correction by using the observed intensity distributions themselves to identify and correct biases. The method calculates median intensities and determines correction factors from the data without requiring external knowledge of bias sources, allowing the system to adapt to both known and unknown biasing phenomena automatically
Solution Approach 2:
The invention transforms the correction approach by changing from model-based parameters (assuming known bias mechanisms) to data-driven parameters (using observed intensity distributions). By parametrizing the intensity distributions and using empirical statistics like medians, the system can adapt to any bias pattern present in the data regardless of its origin
2Measurement precision
If channel-specific correction models are applied, then systematic offsets can be corrected for known phenomena, but the complexity of the correction algorithm increases
Solution Approach 1:
The method extracts only the essential correction information (median intensities and correction factors) from the intensity distributions, separating the critical normalization step from complex model-based corrections. This extraction approach simplifies the algorithm by focusing only on what is necessary for effective normalization
Solution Approach 2:
Instead of applying complex forward models to predict and correct biases, the invention inverts the approach by directly observing the intensity distributions and deriving correction factors from the data itself. This reverse engineering approach simplifies the algorithm by working from observed effects rather than theoretical models
Data Source
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AI summary
A method for performing a normalization of intensity values obtained to perform sequencing analysis comprises the steps: receiving (110) a plurality of image data for a plurality of channels, each image data (11a-11d, 15a-15d) describes for the respective channel (a-d) an intensity distribution over all positions of an image of the a plurality of image data (11 a-11 d, 15a-15d); parametrization (120) of the intensity distribution over all positions for the plurality of channels (a-d) to obtain parametrized distributions for the plurality of channels (a-d); combining (130) the parametrized distributions for the plurality of channels (a-d) to obtain a common distribution for all of the plurality of channels (a-d); and determining (140) for each of the plurality of channels (a-d) a transfer function such that the respective transfer function for the respective channel (a-d) maps the corresponding intensity distribution to the common distribution.