ArticleProceedings of the National Academy of Sciences of the United States of America2012
Making sense out of massive data by going beyond differential expression.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed.
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Genome-wide association study identifies novel variants in olfactory, vitamin A, vitamin B, and cadherin pathways associated with learning and memory.Scientific reports · 2025Article
- Improved prediction of smoking status via isoform-aware RNA-seq deep learning models.PLoS computational biology · 2021Article
- Exploratory Gene Ontology Analysis with Interactive Visualization.Scientific reports · 2019Article
- Identification of gene expression logical invariants inPlant direct · 2019Article
- A Computational Framework for Genome-wide Characterization of the Human Disease Landscape.Cell systems · 2019Article
- Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis.BMC genomics · 2017Article
- Data sources for in vivo molecular profiling of human phenotypes.Wiley interdisciplinary reviews. Systems biology and medicine · 2016Review
- Principal components analysis and the reported low intrinsic dimensionality of gene expression microarray data.Scientific reports · 2016Article
- Transcriptomics analysis of iPSC-derived neurons and modeling of neuropsychiatric disorders.Molecular and cellular neurosciences · 2016Review
- Identification of Cancer Related Genes Using a Comprehensive Map of Human Gene Expression.PloS one · 2016Article
- Integrated analysis of numerous heterogeneous gene expression profiles for detecting robust disease-specific biomarkers and proposing drug targets.Nucleic acids research · 2015Article
- Discovering What Dimensionality Reduction Really Tells Us About RNA-Seq Data.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- An interactive web application for the dissemination of human systems immunology data.Journal of translational medicine · 2015Article
- Understanding multicellular function and disease with human tissue-specific networks.Nature genetics · 2015Article
- Gene expression analysis in Fmr1KO mice identifies an immunological signature in brain tissue and mGluR5-related signaling in primary neuronal cultures.Molecular autism · 2015Article
- Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability.PloS one · 2015Article
- iPSC-derived neurons as a higher-throughput readout for autism: promises and pitfalls.Trends in molecular medicine · 2014Review
- Article
- Ontology-aware classification of tissue and cell-type signals in gene expression profiles across platforms and technologies.Bioinformatics (Oxford, England) · 2013Article
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Authors and funding
4 authors.
Funding
Abstract
With the rapid growth of publicly available high-throughput transcriptomic data, there is increasing recognition that large sets of such data can be mined to better understand disease states and mechanisms. Prior gene expression analyses, both large and small, have been dichotomous in nature, in which phenotypes are compared using clearly defined controls. Such approaches may require arbitrary decisions about what are considered "normal" phenotypes, and what each phenotype should be compared to. Instead, we adopt a holistic approach in which we characterize phenotypes in the context of a myriad of tissues and diseases. We introduce scalable methods that associate expression patterns to phenotypes in order both to assign phenotype labels to new expression samples and to select phenotypically meaningful gene signatures. By using a nonparametric statistical approach, we identify signatures that are more precise than those from existing approaches and accurately reveal biological processes that are hidden in case vs. control studies. Employing a comprehensive perspective on expression, we show how metastasized tumor samples localize in the vicinity of the primary site counterparts and are overenriched for those phenotype labels. We find that our approach provides insights into the biological processes that underlie differences between tissues and diseases beyond those identified by traditional differential expression analyses. Finally, we provide an online resource (http://concordia.csail.mit.edu) for mapping users' gene expression samples onto the expression landscape of tissue and disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.