Genetic Copy Number Alteration Classification via Noise Normalization
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Solution Overview
Problem
Current methods for classifying chromosome copy number alterations (CNAs) in non-invasive prenatal and oncology testing face challenges in accurately distinguishing between true and false positives due to noise in candidate regions, leading to reduced confidence in diagnosis.
Innovation Solution
A method that involves shifting and normalizing sequence read quantifications from candidate regions to baseline levels outside these regions, using a computing device to generate confidence determinations by comparing noise levels in test samples to reference samples without significant CNAs, thereby enhancing the classification of CNAs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequence read quantifications from candidate regions are used directly for CNA classification, then the classification process is simple and fast, but noise in candidate regions leads to false positives and reduced diagnostic confidence
Solution Approach 1:
The patent applies preliminary normalization to sequence read quantifications before classification. By shifting quantifications to a common scale and subtracting baseline levels from control regions, the method prepares the data in advance to reduce noise impact, enabling more reliable CNA detection without requiring complex real-time processing during classification
Solution Approach 2:
The patent introduces control regions as intermediary elements between the candidate regions and the final classification decision. These control regions serve as mediators to establish baseline noise levels, which are then used to adjust and normalize the quantifications from candidate regions, improving diagnostic confidence by accounting for regional noise variations
2Measurement precision
If noise levels in candidate regions are reduced through normalization, then false positives decrease and diagnostic confidence increases, but the processing time and computational complexity increase
Solution Approach 1:
The patent applies partial normalization by focusing computational efforts only on control regions and candidate regions of interest, rather than normalizing the entire genome. This selective approach reduces processing time while still achieving sufficient noise reduction to improve CNA detection accuracy in the regions that matter most for diagnosis
Solution Approach 2:
The patent transforms sequence read quantifications by applying mathematical transformations (shifting to common scale, subtracting baseline levels) to change the parameters of the data. These parameter changes normalize the quantifications and reduce noise impact, improving measurement precision without requiring redundant processing steps
Data Source
AI summary
Technology provided herein relates in part to non-invasive classification of one or more genetic copy number alterations (CNAs) for a test sample. Certain methods include sampling a quantification of sequence reads from parts of a genome, generating a confidence determination, and using the confidence determination to enhance classification. Technology provided herein is useful for classifying a genetic CNA for a sample as part of non-invasive pre-natal (NIPT) testing and oncology testing, for example.