Dataset_Govus_2025_JP

<b>Information</b>Dataset to reproduce the statistical analysis undertaken in Govus et al., (2025) Acute Metabolic Phenotype Responses to Swimming Exercise of Different Intensities in Highly Trained Male and Female Swimmers, submitted for review to the <i>Journal of Physiology</i>.<b>Study Overview</b>This study analysed the changes in metabolomic profile in 16 highly trained male (n = 9) and female (n = 7) swimmers who performed each of the swimming trials below, separated by a 1-2 day break:Moderate domain trial: 5 × 400 m on 6’ at A1/A2 intensityHeavy domain trial: 3 × (8 × 100 m holding critical speed, 100 m recovery on 2’)Severe domain trial: 3 × (1 × 35 m dive max on 2’, 2 × 50 m dive max on 3’, 200 m recovery on 5’)<b>Project Team</b>Dr Andrew Govus (La Trobe University, Principal Investigator)Dr Chloe Goldsmith (University of Western Sydney, Co-investigator)Dr Katie McGibbon (Queensland Academy of Sport, Co-investigator)Dr Lachlan Mitchell (Victorian Institute of Sport, Co-investigator)Dr Maria Kozlovskaia (University of Canberra, Co-Investigator)E/Prof David Pyne (University of Canberra, Co-Investigator)Dr Nathan Lawler (Australian National Phenome Centre, Murdoch Univeristy, Co-investigator)<b>Sample &amp; Data Analysis</b><b>Metabolomic data analysis:</b> Blood plasma was analysed by NMR and LC-MS by Dr Nathan Lawler (Mudroch University) at the Australian National Phenome Centre.<b>Bioinformatics:</b> Bioinformatics for metabolomic data was performed by Andrew Govus (La Trobe University) and Dr Nathan Lawler (Murdoch University).<b>Project Funding</b>Queensland Academy of Sport Innovation and Knowledge Excellence (SPIKE) ($25,000 AUD)University of Canberra Industry Collaborative Seed Grant ($25,000 AUD)La Trobe University School of Allied Health, Human Services &amp; Sport Stategic Research Allocation ($10,000 AUD)<b>Bioinformatics Data Analysis</b>Data analysis is peformed using the R statistical programming language.<i>Bioinformatics Approach - Metabolomics</i>Data cleaningInputation of missing data &amp; outlier detectionSupervised multivariate analysis: Orthogonal Partial Least Squeres Discriminant Analysis (OPLS-DA) on log2 fold change (post-exercise/pre-exercise) to compare the heavy and severe domain trials against the moderate domain trialUnivariate analysis: Linear mixed models to compare each trial + eruption plot to visualise influential metabolites<b>Github link:</b> https://github.com/NathanGLawler/Swimmer-s-Phenomics-Project

<b>数据集说明</b>本数据集用于复现Govus等人2025年发表于《生理学杂志》（*Journal of Physiology*，投稿待审中）的研究中所采用的统计分析流程，该研究主题为高水平男女游泳运动员不同强度游泳运动后的急性代谢表型（metabolic phenotype）响应。<b>研究概况</b>本研究分析了16名高水平男女游泳运动员（男性9名，女性7名）的代谢组学（metabolomics）特征变化，受试者依次完成以下各项游泳测试，每项测试间隔1-2天休息：中等强度区间测试：以A1/A2强度完成5组400米游泳，组间休息6分钟；高强度区间测试：3组×(8组100米游泳，维持临界速度，随后100米恢复，休息2分钟)；极高强度区间测试：3组×(1组35米全力出发游泳，休息2分钟；2组50米全力出发游泳，休息3分钟；随后200米恢复，休息5分钟)。<b>项目团队</b>安德鲁·戈夫斯博士（拉筹伯大学，首席研究员）、克洛伊·戈德史密斯博士（西悉尼大学，联合研究员）、凯蒂·麦吉本博士（昆士兰体育学院，联合研究员）、拉克兰·米切尔博士（维多利亚体育学院，联合研究员）、玛丽亚·科兹洛夫斯卡娅博士（堪培拉大学，联合研究员）、戴维·派恩特聘教授（堪培拉大学，联合研究员）、内森·劳勒博士（默多克大学澳大利亚国家表型组中心，联合研究员）。<b>样本与数据分析</b><b>代谢组学数据分析：</b>血液血浆样本由澳大利亚国家表型组中心的内森·劳勒博士（默多克大学）通过核磁共振（NMR）与液相色谱-质谱联用法（LC-MS）完成检测。<b>生物信息学分析：</b>代谢组学数据的生物信息学分析由安德鲁·戈夫斯（拉筹伯大学）与内森·劳勒博士（默多克大学）共同完成。<b>项目资助</b>昆士兰体育学院创新与知识卓越计划（SPIKE），资助金额25000澳元；堪培拉大学产业合作种子基金，资助金额25000澳元；拉筹伯大学附属健康、人文服务与体育学院战略研究拨款，资助金额10000澳元。<b>生物信息学数据分析</b>数据分析采用R统计编程语言完成。<i>生物信息学分析流程——代谢组学</i>数据清洗、缺失数据填充与异常值检测、监督式多变量分析：基于log₂倍数变化（log2 fold change，运动后/运动前）的正交偏最小二乘判别分析（Orthogonal Partial Least Squares Discriminant Analysis，OPLS-DA），用于对比高强度、极高强度区间测试与中等强度区间测试的组间差异；单变量分析：线性混合模型（Linear mixed models）用于对比各项测试的差异，并结合喷发图以可视化具有统计学影响力的代谢物。<b>Github链接：</b>https://github.com/NathanGLawler/Swimmer-s-Phenomics-Project