2026-02-13 中国科学院(CAS)
RamEX: a modularized pipeline for ramanome analysis. (Image by LIU Yang)
<関連情報>
- https://english.cas.cn/newsroom/research-news/202602/t20260224_1151116.shtml
- https://link.springer.com/article/10.1186/s40168-026-02339-3
RamEx: 正確な品質評価を備えた高スループット微生物ラマンオノーム分析のためのRパッケージ RamEx: an R package for high-throughput microbial ramanome analyses with accurate quality assessment
Yanmei Zhang,Gongchao Jing,Rongze Chen,Yanhai Gong,Yuandong Li,Yongshun Wang,Xixian Wang,Jia Zhang,Yuli Mao,Yuehui He,Xiaoshan Zheng,Mingchao Wang,Hao Yuan,Jian Xu & Luyang Sun
Microbiome Published:10 February 2026
DOI:https://doi.org/10.1186/s40168-026-02339-3 An unedited version of this manuscript
Abstract
Background
Microbial single-cell Raman spectroscopy (SCRS) has emerged as a powerful tool for label-free phenotyping, enabling rapid characterization of microbial diversity, metabolic states, and functional interactions within complex communities. However, high-throughput SCRS datasets often contain spectral anomalies from noise and fluorescence interference, which obscure microbial signatures and hinder accurate classification. Robust algorithms for outlier detection and microbial ramanome analysis remain underdeveloped.
Results
Here, we introduce RamEx, an R package specifically designed for high-throughput microbial ramanome analyses with robust quality control and phenotypic classification. At the core of RamEx is the Iterative Convolutional Outlier Detection (ICOD) algorithm, which dynamically detects spectral anomalies without requiring predefined thresholds. Benchmarking on both simulated and real microbial datasets—including pathogenic bacteria, probiotic strains, and yeast fermentation populations—demonstrated that ICOD achieves an F1 score of 0.97 on simulated datasets and 0.74 on real datasets, outperforming existing approaches by at least 19.8%. Beyond anomaly detection, RamEx provides a modular and scalable workflow for microbial phenotype differentiation, taxonomic marker identification, metabolic-associated fingerprinting, and intra-population heterogeneity analysis. It integrates Raman-based species-specific biomarkers, enabling precise classification of microbial communities and facilitating functional trait mapping at the single-cell level. To support large-scale studies, RamEx incorporates C++ acceleration, GPU parallelization, and optimized memory management, enabling the rapid processing of over one million microbial spectra within an hour.
Conclusions
By bridging the gap between high-throughput Raman-based microbial phenotyping and computational analysis, RamEx provides a comprehensive toolkit for exploring microbial ecology, metabolic interactions, and antibiotic susceptibility at the single-cell resolution. RamEx is freely available under the MIT license at https://github.com/qibebt-bioinfo/RamEx.
