Expression deconvolution: A reinterpretation of DNA microarray data reveals dynamic changes in cell populations

Lu, Ping; Nakorchevskiy, Aleksey A.; Marcotte, Edward M.

doi:10.1073/pnas.1832361100

Cited by 126 publications

(144 citation statements)

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“…Data-driven methods rely solely on matrix decomposition algorithms and/or classification based on expression signatures is sensitive to the noise in the data [15,16]. On the other hand, tissue purification (e.g.…”

Section: Discussionmentioning

confidence: 99%

Deconvoluting complex tissues for expression quantitative trait locus-based analyses

Seo

Fatima

et al. 2013

Phil. Trans. R. Soc. B

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Breast cancer genome-wide association studies have pinpointed dozens of variants associated with breast cancer pathogenesis. The majority of risk variants, however, are located outside of known protein-coding regions. Therefore, identifying which genes the risk variants are acting through presents an important challenge. Variants that are associated with mRNA transcript levels are referred to as expression quantitative trait loci (eQTLs). Many studies have demonstrated that eQTL-based strategies provide a direct way to connect a trait-associated locus with its candidate target gene. Performing eQTL-based analyses in human samples is complicated because of the heterogeneous nature of human tissue. We addressed this issue by devising a method to computationally infer the fraction of cell types in normal human breast tissues. We then applied this method to 13 known breast cancer risk loci, which we hypothesized were eQTLs. For each risk locus, we took all known transcripts within a 2 Mb interval and performed an eQTL analysis in 100 reduction mammoplasty cases. A total of 18 significant associations were discovered (eight in the epithelial compartment and 10 in the stromal compartment). This study highlights the ability to perform large-scale eQTL studies in heterogeneous tissues.

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Section: Discussionmentioning

confidence: 99%

Deconvoluting complex tissues for expression quantitative trait locus-based analyses

Seo

Fatima

et al. 2013

Phil. Trans. R. Soc. B

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“…Other studies also supported the reliability of oligonucleotide microarray data when compared with other validation methods, such as RT-PCR (Bernaudin et al, 2002;Tang et al, 2001), and others (Chudin et al, 2002;Ishii et al, 2000). New methods for microarray data analysis are being developed to improve sensitivity and specificity in a variety of experimental paradigms, such as Significance Analysis of Microarray Data (SAM) and Expression Deconvolution Analysis (Lu et al, 2003;Tusher et al, 2001). Further improvements and application of these methods to complex data, such as expression profiling of multiple groups along a time course, as in the current study, would improve data mining.…”

Section: Figmentioning

confidence: 94%

Genome-Wide Gene Expression Analysis for Induced Ischemic Tolerance and Delayed Neuronal Death following Transient Global Ischemia in Rats

Kawahara

Wang

Mukasa

et al. 2004

J Cereb Blood Flow Metab

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Summary:Genome-wide gene expression analysis of the hippocampal CA1 region was conducted in a rat global ischemia model for delayed neuronal death and induced ischemic tolerance using an oligonucleotide-based DNA microarray containing 8,799 probes. The results showed that expression levels of 246 transcripts were increased and 213 were decreased following ischemia, corresponding to 5.1% of the represented probe sets. These changes were divided into seven expression clusters using hierarchical cluster analysis, each with distinct conditions and time-specific patterns. Ischemic tolerance was associated with transient up-regulation of transcription factors (c-Fos, JunB Egr-1, -2, -4, NGFI-B), Hsp70 and MAP kinase cascaderelated genes (MKP-1), which are implicated cell survival. Delayed neuronal death exhibited complex long-lasting changes of expression, such as up-regulation of proapoptotic genes (GADD153, Smad2, Dral, Caspase-2 and -3) and downregulation of genes implicated in survival signaling (MKK2, and PI4 kinase, DAG/PKC signaling pathways), suggesting an imbalance between death and survival signals. Our study provides a differential gene expression profile between delayed neuronal death and induced ischemic tolerance in a genomewide analysis, and contributes to further understanding of the complex molecular pathophysiology in cerebral ischemia.

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“…If the cell cycle distribution of a population of cells at various times during an experiment could be estimated, then assessments that more accurately reflect true single-cell values could be determined from population-level measurements. [3][4][5] Here, we develop a robust mathematical model to estimate the evolving cell cycle distribution of a population of cells in a synchrony experiment.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Model for Cell Cycle Distributions in Synchrony Experiments

Orlando

Lin²,

Bernard³

et al. 2007

Cell Cycle

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Expression deconvolution: A reinterpretation of DNA microarray data reveals dynamic changes in cell populations

Cited by 126 publications

References 50 publications

Deconvoluting complex tissues for expression quantitative trait locus-based analyses

Deconvoluting complex tissues for expression quantitative trait locus-based analyses

Genome-Wide Gene Expression Analysis for Induced Ischemic Tolerance and Delayed Neuronal Death following Transient Global Ischemia in Rats

A Probabilistic Model for Cell Cycle Distributions in Synchrony Experiments

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