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A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers
- Source :
- Theranostics
- Publication Year :
- 2017
- Publisher :
- Ivyspring International Publisher, 2017.
-
Abstract
- 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.
- Subjects :
- 0301 basic medicine
Normalization (statistics)
Common gene
pan-cancer
Medicine (miscellaneous)
Biology
medicine.disease_cause
Transcriptome
03 medical and health sciences
heterogeneity
Neoplasms
medicine
Humans
Pharmacology, Toxicology and Pharmaceutics (miscellaneous)
Cross-Value Association Analysis
Genetic association
Genetics
normalization-free
Gene Expression Profiling
Nonparametric statistics
Computational Biology
030104 developmental biology
Differentially expressed genes
Expression data
Carcinogenesis
transcriptome
Research Paper
Genes, Neoplasm
Subjects
Details
- ISSN :
- 18387640
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- Theranostics
- Accession number :
- edsair.doi.dedup.....aba4610b6b9ef8214d0b8d4136b1842e