Convolutional Neural Network Detection of Genomic Contamination
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Solution Overview
Problem
Existing methods lack robust techniques for accurately assessing the quality of genomic data from biological samples, particularly in the context of contamination detection, which is crucial for reliable cancer diagnostics using circulating cell-free DNA.
Innovation Solution
A convolutional neural network (CNN) is trained using transfer learning and image analysis to classify biological samples as contaminated or not, utilizing a contamination analysis classification layer and leveraging pre-trained networks like LeNet, AlexNet, VGGNet 16, GoogLeNet, or ResNet, and representing genomic data as images for contamination detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional contamination detection methods are used, then the process is simple, but the detection accuracy is insufficient
Solution Approach 1:
The patent replaces traditional mechanical or manual contamination detection methods with a convolutional neural network-based automated image analysis system. The CNN processes images of biological samples to detect contamination patterns, substituting human expertise and simple filtering methods with an intelligent automated system that achieves superior detection accuracy while maintaining operational simplicity through software-based solutions.
Solution Approach 2:
The patent introduces an intermediary image analysis layer between sample preparation and sequencing. By capturing and analyzing images of biological samples during processing, the system creates an intermediate detection step that identifies contamination without interfering with the downstream sequencing workflow, thereby improving detection accuracy while integrating seamlessly into existing systems.
2Reliability
If no contamination detection is performed, then the workflow is faster, but false positives increase
Solution Approach 1:
The patent performs contamination detection as a preliminary action before sequencing and diagnostic analysis. By implementing the CNN-based image analysis at an early stage in the workflow, the system identifies and flags contaminated samples prior to resource-intensive sequencing, thereby preventing false positives in downstream analysis while minimizing time loss through parallel processing capabilities.
Solution Approach 2:
The patent enables rapid contamination assessment by having the CNN quickly analyze sample images and generate contamination probability scores. This fast preliminary screening allows the workflow to skip unnecessary sequencing steps for clearly contaminated samples, reducing overall processing time while maintaining high diagnostic reliability through accurate early detection.
Data Source
AI summary
A method for training a convolutional neural net for contamination analysis is provided. A training dataset is obtained comprising, for each respective training subject in a plurality of subjects, a variant allele frequency of each respective single nucleotide variant in a respective plurality of single nucleotide variants, and a respective contamination indication. First and second subsets of the plurality of training subjects have first and second contamination indication values, respectively. A corresponding first channel comprising a first plurality of parameters that include a respective parameter for a single nucleotide variant allele frequency of each respective single nucleotide variant in a set of single nucleotide variants in a reference genome is constructed for each respective training subject. An untrained or partially trained convolutional neural net is trained using, for each respective training subject, at least the corresponding first channel of the respective training subject as input against the respective contamination indication.