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论文题目: A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers
英文论文题目: A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers
第一作者: Li, QG; He, YH; Wu, H; Yang, CP; Pu, SY; Fan, SQ; Jiang, LP; Shen, QS; Wang, XX; Chen, XQ; Yu, Q; Li, Y; Sun, C; Wang, XT; Zhou, JM; Li, HP; Chen, YB; Kong, QP
英文第一作者: Li, QG; He, YH; Wu, H; Yang, CP; Pu, SY; Fan, SQ; Jiang, LP; Shen, QS; Wang, XX; Chen, XQ; Yu, Q; Li, Y; Sun, C; Wang, XT; Zhou, JM; Li, HP; Chen, YB; Kong, QP
联系作者: Kong, QP (reprint author), Chinese Acad Sci, Kunming Inst Zool, State Key Lab Genet Resources & Evolut, Kunming 650223, Peoples R China.
英文联系作者: Kong, QP (reprint author), Chinese Acad Sci, Kunming Inst Zool, State Key Lab Genet Resources & Evolut, Kunming 650223, Peoples R China.
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发表年度: 2017
卷: 7
期: 11
页码: 2888-2899
摘要: Heterogeneity in transcriptional data hampers the identification of differentially expressed genes (DEGs) and understanding of cancer, essentially because current methods rely on cross-sample normalization and/or distribution assumption-both sensitive to heterogeneous values. Here, we developed a new method, Cross-Value Association Analysis (CVAA), which overcomes the limitation and is more robust to heterogeneous data than the other methods. Applying CVAA to a more complex pan-cancer dataset containing 5,540 transcriptomes discovered numerous new DEGs and many previously rarely explored pathways/processes; some of them were validated, both in vitro and in vivo, to be crucial in tumorigenesis, e.g., alcohol metabolism (ADH1B), chromosome remodeling (NCAPH) and complement system (Adipsin). Together, we present a sharper tool to navigate large-scale expression data and gain new mechanistic insights into tumorigenesis.
英文摘要: Heterogeneity in transcriptional data hampers the identification of differentially expressed genes (DEGs) and understanding of cancer, essentially because current methods rely on cross-sample normalization and/or distribution assumption-both sensitive to heterogeneous values. Here, we developed a new method, Cross-Value Association Analysis (CVAA), which overcomes the limitation and is more robust to heterogeneous data than the other methods. Applying CVAA to a more complex pan-cancer dataset containing 5,540 transcriptomes discovered numerous new DEGs and many previously rarely explored pathways/processes; some of them were validated, both in vitro and in vivo, to be crucial in tumorigenesis, e.g., alcohol metabolism (ADH1B), chromosome remodeling (NCAPH) and complement system (Adipsin). Together, we present a sharper tool to navigate large-scale expression data and gain new mechanistic insights into tumorigenesis.
刊物名称: THERANOSTICS
英文刊物名称: THERANOSTICS
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学科: Medicine, Research & Experimental
英文学科: Medicine, Research & Experimental
影响因子: 8.712
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论文类别: Article
英文论文类别: Article
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