代谢组学
无损压缩
质谱法
压缩(物理)
色谱法
化学
计算机科学
计算生物学
数据压缩
生物
算法
物理
热力学
作者
Junjie Tong,Miaoshan Lu,Ruimin Wang,Shaowei An,Jinyin Wang,Tong Wang,Cong Xie,Changbin Yu
标识
DOI:10.1021/acs.jproteome.3c00851
摘要
Several lossy compressors have achieved superior compression rates for mass spectrometry (MS) data at the cost of storage precision. Currently, the impacts of precision losses on MS data processing have not been thoroughly evaluated, which is critical for the future development of lossy compressors. We first evaluated different storage precision (32 bit and 64 bit) in lossless mzML files. We then applied 10 truncation transformations to generate precision-lossy files: five relative errors for intensities and five absolute errors for m/z values. MZmine3 and XCMS were used for feature detection and GNPS for compound annotation. Lastly, we compared Precision, Recall, F1 – score, and file sizes between lossy files and lossless files under different conditions. Overall, we revealed that the discrepancy between 32 and 64 bit precision was under 1%. We proposed an absolute m/z error of 10–4 and a relative intensity error of 2 × 10–2, adhering to a 5% error threshold (F1 – scores above 95%). For a stricter 1% error threshold (F1 – scores above 99%), an absolute m/z error of 2 × 10–5 and a relative intensity error of 2 × 10–3 were advised. This guidance aims to help researchers improve lossy compression algorithms and minimize the negative effects of precision losses on downstream data processing.
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