ArticleGenome research2001
An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles.
Article in Genome research, 2001. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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Who cites it
69 citing papers in PubMed.
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- Computationally repurposing drugs for breast cancer subtypes using a network-based approach.BMC bioinformatics · 2022Article
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- Gene Correlation Guided Gene Selection for Microarray Data Classification.BioMed research international · 2021Article
- Osteoblasts Promote Prostate Cancer Cell Proliferation Through Androgen Receptor Independent Mechanisms.Frontiers in oncology · 2021Article
- Single-Cell Classification Using Mass Spectrometry through Interpretable Machine Learning.Analytical chemistry · 2020Article
- Gene selection for microarray data classification via subspace learning and manifold regularization.Medical & biological engineering & computing · 2018Article
- Investigating a multigene prognostic assay based on significant pathways for Luminal A breast cancer through gene expression profile analysis.Oncology letters · 2018Article
- An improved analysis methodology for translational profiling by microarray.RNA (New York, N.Y.) · 2017Article
- The impact of RNA sequence library construction protocols on transcriptomic profiling of leukemia.BMC genomics · 2017Article
- A clustering-based approach for efficient identification of microRNA combinatorial biomarkers.BMC genomics · 2017Article
- SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information.BMC bioinformatics · 2017Article
9 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
We have developed a statistical regression modeling approach to discover genes that are differentially expressed between two predefined sample groups in DNA microarray experiments. Our model is based on well-defined assumptions, uses rigorous and well-characterized statistical measures, and accounts for the heterogeneity and genomic complexity of the data. In contrast to cluster analysis, which attempts to define groups of genes and/or samples that share common overall expression profiles, our modeling approach uses known sample group membership to focus on expression profiles of individual genes in a sensitive and robust manner. Further, this approach can be used to test statistical hypotheses about gene expression. To demonstrate this methodology, we compared the expression profiles of 11 acute myeloid leukemia (AML) and 27 acute lymphoblastic leukemia (ALL) samples from a previous study (Golub et al. 1999) and found 141 genes differentially expressed between AML and ALL with a 1% significance at the genomic level. Using this modeling approach to compare different sample groups within the AML samples, we identified a group of genes whose expression profiles correlated with that of thrombopoietin and found that genes whose expression associated with AML treatment outcome lie in recurrent chromosomal locations. Our results are compared with those obtained using t-tests or Wilcoxon rank sum statistics.
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