SPSNet: subpopulation-sensitive network-based analysis of heterogeneous gene expression data.
SPSNet: subpopulation-sensitive network-based analysis of heterogeneous gene expression data.
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DOI：
10.1186/s12918-018-0538-1
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发表时间：
2018-03-19
影响因子：
--
通讯作者：
Wong L
中科院分区：
文献类型：
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作者：
Belorkar A;Vadigepalli R;Wong L
转录数据集通常包含由生物变异引起的未申报的异质性，如疾病亚型、治疗亚组、时间序列基因表达的多样性、嵌套实验条件，以及由于批次效应、集成荟萃分析中的平台差异等引起的技术变异。然而，目前的分析方法主要是为了处理由同质样本代表的实验条件之间的比较，从而排除了发现潜在的亚表型的可能性。非监督的亚型鉴定方法通常基于个体基因水平分析，这往往导致潜在亚型的基因签名不可复制。研究异质性的新兴方法在很大程度上是在包含数百到数千个样本的单细胞数据集的背景下开发的，限制了它们对选择背景的使用。我们提出了一种新的分析方法SPSNet，它根据生物通路中的子网络的活动来识别特定亚型的基因表达特征。SPSNet确定了捕获潜在生物学机制多样性的基因子网络，表明了潜在的样本亚型。在存在外在或非生物异质性(如批次效应)的情况下，SPSNet识别特别受这种变异影响的子网络，从而帮助消除与所研究表型的生物学无关的因素。使用多个公开可用的数据集，我们说明SPSNet能够一致地发现基因表达数据中对应于各种来源的有意义的异质性的模式。我们还展示了SPSNet作为理解这种异质性的结构和性质的灵敏和可靠的工具的性能。本文的在线版本(10.1186/s129180180538-1)包含向授权用户提供的补充材料。
Transcriptomic datasets often contain undeclared heterogeneity arising from biological variation such as diversity of disease subtypes, treatment subgroups, time-series gene expression, nested experimental conditions, as well as technical variation due to batch effects, platform differences in integrated meta-analyses, etc. However, current analysis approaches are primarily designed to handle comparisons between experimental conditions represented by homogeneous samples, thus precluding the discovery of underlying subphenotypes. Unsupervised methods for subtype identification are typically based on individual gene level analysis, which often result in irreproducible gene signatures for potential subtypes. Emerging methods to study heterogeneity have been largely developed in the context of single-cell datasets containing hundreds to thousands of samples, limiting their use to select contexts. We present a novel analysis method, SPSNet, which identifies subtype-specific gene expression signatures based on the activity of subnetworks in biological pathways. SPSNet identifies the gene subnetworks capturing the diversity of underlying biological mechanisms, indicating potential sample subphenotypes. In the presence of extrinsic or non-biological heterogeneity (e.g. batch effects), SPSNet identifies subnetworks that are particularly affected by such variation, thus helping eliminate factors irrelevant to the biology of the phenotypes under study. Using multiple publicly available datasets, we illustrate that SPSNet is able to consistently uncover patterns within gene expression data that correspond to meaningful heterogeneity of various origins. We also demonstrate the performance of SPSNet as a sensitive and reliable tool for understanding the structure and nature of such heterogeneity. The online version of this article (10.1186/s12918-018-0538-1) contains supplementary material, which is available to authorized users.
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影响因子：
3
作者：
Soh D;Dong D;Guo Y;Wong L
通讯作者：
Wong L
影响因子：
3
作者：
Soh D;Dong D;Guo Y;Wong L
通讯作者：
Wong L
从集合到图：转化转录组系统的现实富集分析。
DOI：
10.1093/bioinformatics/btr228
发表时间：
2011-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
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作者：
Geistlinger L;Csaba G;Küffner R;Mulder N;Zimmer R
通讯作者：
Zimmer R
影响因子：
4.3
作者：
Haynes WA;Higdon R;Stanberry L;Collins D;Kolker E
通讯作者：
Kolker E
影响因子：
9.9
作者：
通讯作者：
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