A Method for Improving the Detection Accura-cy of MSIsensor Based on Downsampling

TL;DR

This paper evaluates the limitations of MSIsensor's chi-square test algorithm in high-depth NGS data for MSI detection and proposes downsampling as a method to improve detection accuracy.

Contribution

The study identifies the shortcomings of MSIsensor's current algorithm and introduces downsampling techniques to enhance MSI detection reliability in high-depth sequencing data.

Findings

Chi-square test is insufficient for high-depth MSI assessment

Downsampling improves MSIsensor detection accuracy

Provides insights for more reliable MSI detection methods

Abstract

Motivation: Microsatellite instability (MSI) is a cancer biomarker associated with cancer prognosis and chemotherapy sensitivity. Since the discovery of MSI, polymerase chain reaction (PCR)-based testing has been considered the gold standard for MSI detection. However, with the decrease in sequencing costs, software that calculates MSI based on next-generation sequencing (NGS) data has been widely applied. Results: In this study, we evaluated the performance of the MSIsensor detection software, focusing on the limitations of the chi-square test algorithm in determining microsatellite stability under high-depth sequencing data. We demonstrated that the chi-square test algorithm is insufficient for accurately as-sessing microsatellite stability in this context. Furthermore, we explored the application of downsampling techniques to enhance the accuracy of MSIsensor detection. Our findings provide insight into the limita-tions of current methods and offer potential improvements for more reliable MSI detection based on NGS data.