减少数据处理导致的数量不确定性 在非目标代谢学中
Zixuan Zhang1, Huaxu Yu1, Ethan Wong-Ma1
1Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver V6T 1Z1, BC, Canada.
Analytical chemistry
|February 23, 2024
概括
一个新的工具,AVIR,使用机器学习来识别和纠正代谢学数据中的计算变化. 这提高了在非目标代谢学分析中的定量结果的准确性.
科学领域:
- 代谢学 代谢学 代谢学
- 计算生物学 计算生物学
- 数据分析 数据分析
背景情况:
- 基于液体染色学-质谱学 (LC-MS) 的代谢学数据处理可以引入定量不确定性.
- 这种不确定性,称为计算变异,可以影响结果的可靠性.
研究的目的:
- 开发一种计算解决方案,用于自动识别具有计算变化的代谢特征.
- 提高非目标代谢学分析的定量确定性.
主要方法:
- 开发了AVIR (准确对整合和整合的评估),这是一个基于支持矢量机器的机器学习工具.
- 在696个手动精选的代谢特征上训练有素的AVIR,在交叉验证中达到94%的准确性.
- 在外部数据集上验证了AVIR,显示 84% - 97% 的准确性.
主要成果:
- 在一项大规模的代谢学研究中,AVIR成功识别了具有计算变异的特征.
- 在75.3%的样本中,手动纠正已识别的特征使相对强度差异减少了20%以上.
- 证明了AVIR在减少计算变量的有效性.
结论:
- AVIR 是一个有价值的工具,可以提高在非目标代谢学中的定量确定性.
- 自动识别和纠正计算变异可以提高数据可靠性.
- 通过AVIR,可以从代谢学数据中进行更准确的下游生物解释.
相关概念视频
01:15Systematic Error: Methodological and Sampling Errors
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
00:59Uncertainty: Overview
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
01:15Data Validation
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:
01:16Contaminants and Errors
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Another key consideration is determining the appropriate number of samples required to...
00:59Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
01:13Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...