Transcriptomic Classification Confidence Scoring Under Sequencing Variance
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Solution Overview
Problem
Next-generation sequencing technologies introduce noise and artifacts that lead to indeterminate classification results, causing low-confidence or discordant patient grouping, which affects clinical decision-making and patient confidence.
Innovation Solution
A simulation-based approach using machine learning models to predict standard deviation and confidence scores for gene expression values, flagging samples with low confidence as potentially discordant to ensure accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sequencing runs are performed to reduce noise and artifacts, then measurement precision improves, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by performing simulations and generating confidence scores before actual clinical sequencing results are finalized. The system pre-calculates what the classification outcomes would be under various noise conditions, allowing clinicians to know in advance whether a sample is likely to produce indeterminate results. This prevents wasted sequencing runs on samples that would anyway yield discordant classifications.
2Measurement precision
If sequencing depth is increased to reduce variance, then measurement precision improves, but use of energy increases
Solution Approach 1:
The patent changes the parameter from physical sequencing depth to computational confidence scoring. Instead of increasing sequencing depth to reduce variance, the system uses machine learning models to simulate various expression levels and calculate confidence scores. This computational approach achieves the same goal of assessing measurement reliability without the energy cost of additional sequencing.
3Measurement precision
If classification thresholds are made more stringent to improve accuracy, then measurement precision improves, but productivity decreases
Solution Approach 1:
The patent introduces an intermediary confidence score between the raw sequencing data and the final classification. This confidence score acts as a mediator that filters samples before classification. Samples with low confidence scores are flagged for additional analysis or resequencing, while high confidence samples proceed directly to classification. This intermediary step maintains high accuracy without bottlenecking the overall workflow.
Data Source
AI summary
A method includes receiving transcriptomic data; predicting standard deviation values for each gene expression values; for each sample: computing a simulated expression value, classifying the simulated expression values, computing confidence scores; and flagging the sample; and storing the confidence scores. A computing system includes a processor; and a memory having stored thereon instructions that when executed, cause the computing system to: receive transcriptomic data; predict standard deviation values for each gene expression values; for each sample: compute a simulated expression value, classify the simulated expression values, compute confidence scores; and flag the sample; and store the confidence scores. A computer-readable media includes non-transitory computer-readable instructions that, when executed, cause a computer to: receive transcriptomic data; predict standard deviation values for each gene expression values; for each sample: compute a simulated expression value, classify the simulated expression values, compute confidence scores; and flag the sample; and store the confidence scores.