Assessing the Impact of Sample Heterogeneity on Transcriptome Analysis of Human Diseases Using MDP Webtool
Assessing the Impact of Sample Heterogeneity on Transcriptome Analysis of Human Diseases Using MDP Webtool
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DOI：
10.3389/fgene.2019.00971
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发表时间：
2019-10-24
影响因子：
3.7
通讯作者：
Nakaya, Helder, I
中科院分区：
文献类型：
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作者：
Goncalves, Andre N. A.;Lever, Melissa;Nakaya, Helder, I
转录组分析增加了我们对人类疾病分子机制的理解。大多数方法旨在通过比较健康受试者和患有某种疾病的一组患者之间的表达值来识别重要基因。鉴于研究通常包含很少的样本，环境因素或未发现的疾病引起的个体之间的异质性可能会影响基因表达分析。我们对与炎症和传染病相关的各种基因表达研究中的样本异质性进行了系统分析，并表明一旦异质性得到解决，可能会出现新的免疫学见解。使用未受到干扰的受试者（即健康受试者）作为参考组来量化样本的扰动分数。这样的分数使我们能够检测患病患者的外围样本和亚组，甚至评估感染病毒的单细胞的分子扰动。我们还展示了去除外围样本如何改善疾病的“信号”并影响差异表达基因的检测。该方法可通过 mdp Bioconductor R 包和用户友好的网络工具 webMDP 提供，可从 http://mdp.sysbio.tools 获取。
Transcriptome analyses have increased our understanding of the molecular mechanisms underlying human diseases. Most approaches aim to identify significant genes by comparing their expression values between healthy subjects and a group of patients with a certain disease. Given that studies normally contain few samples, the heterogeneity among individuals caused by environmental factors or undetected illnesses can impact gene expression analyses. We present a systematic analysis of sample heterogeneity in a variety of gene expression studies relating to inflammatory and infectious diseases and show that novel immunological insights may arise once heterogeneity is addressed. The perturbation score of samples is quantified using nonperturbed subjects (i.e., healthy subjects) as a reference group. Such a score allows us to detect outlying samples and subgroups of diseased patients and even assess the molecular perturbation of single cells infected with viruses. We also show how removal of outlying samples can improve the "signal" of the disease and impact detection of differentially expressed genes. The method is made available via the mdp Bioconductor R package and as a user-friendly webtool, webMDP, available at http://mdp.sysbio.tools.