Read Depth Correction for Accurate FFPE CNV Profiling
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Solution Overview
Problem
DNA damage in FFPE-treated tissues introduces noise in copy number variation (CNV) profiles during whole-genome analysis, leading to inaccurate results, which are not present in FF-treated tissues, necessitating effective noise processing to obtain accurate analysis.
Innovation Solution
A method involving read depth correction using a data set of FFPE and FF samples, followed by wavelet coefficient thresholding to remove noise, ensuring accurate CNV profiling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If FFPE treatment and prolonged storage are used for tissue preservation, then cost-effectiveness and ease of storage are improved, but DNA damage occurs leading to noise in CNV profiles and reduced measurement precision
Solution Approach 1:
The patent extracts and removes noise from FFPE sample data through a series of processing steps: initial noise removal using FF sample data, wavelet transform decomposition, thresholding of wavelet coefficients, and reconstruction. This separates the harmful noise components from the useful CNV profile information, allowing accurate analysis despite FFPE treatment
Solution Approach 2:
The patent creates a composite processing approach by combining data from both FFPE and FF samples, applying multiple correction techniques (initial noise removal, wavelet transform, thresholding) in sequence. This composite methodology leverages the advantages of both sample types and multiple processing techniques to achieve accurate CNV profiling from FFPE tissues
2Measurement precision
If FF treatment method is used for whole-genome analysis, then measurement precision of CNV profile is improved, but specialized facilities such as nitrogen tanks are required
Solution Approach 1:
The patent uses FF sample data as an intermediary reference to remove noise from FFPE sample data. By comparing FFPE data against FF data and using the latter as a baseline for noise characteristics, the method transfers the accuracy benefits of FF processing to FFPE samples without requiring FF processing facilities
Solution Approach 2:
The patent applies parameter changes through wavelet transform decomposition and thresholding operations. By transforming the data into different domains and selectively modifying parameters (thresholding wavelet coefficients), the method enhances signal quality and removes noise, achieving FF-level precision from FFPE samples
Data Source
AI summary
The present disclosure relates to a method for determining a copy number variation profile, which is executed by one or more processors, and includes acquiring results of whole-genome analysis associated with a target sample collected from a subject, calculating a read depth associated with the target sample for each of a plurality of predetermined bins on genome based on the acquired results of whole-genome analysis, correcting the read depth associated with the target sample, and determining a copy number variation profile associated with the target sample using the corrected read depth.