ArticleGenome biology2004
Clustering analysis of SAGE data using a Poisson approach.
Article in Genome biology, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
31 citing papers in PubMed.
- Article
- Whole transcriptome analysis reveals differential gene expression profile reflecting macrophage polarization in response to influenza A H5N1 virus infection.BMC medical genomics · 2018Article
- Multiple-cumulative probabilities used to cluster and visualize transcriptomes.FEBS open bio · 2017Article
- Article
- DGEclust: differential expression analysis of clustered count data.Genome biology · 2015Article
- Review
- Generalized Linear and Mixed Models for Label-Free Shotgun Proteomics.Statistics and its interface · 2012Article
- Article
- Gene expression profiling of human breast tissue samples using SAGE-Seq.Genome research · 2010Article
- LNCaP Atlas: gene expression associated with in vivo progression to castration-recurrent prostate cancer.BMC medical genomics · 2010Article
- Genomic analysis distinguishes phases of early development of the mouse atrio-ventricular canal.Physiological genomics · 2010Article
- Article
- Differential gene expression in normal esophagus and Barrett's esophagus.Journal of gastroenterology · 2009Article
- Significance analysis of spectral count data in label-free shotgun proteomics.Molecular & cellular proteomics : MCP · 2008Article
- A seriation approach for visualization-driven discovery of co-expression patterns in Serial Analysis of Gene Expression (SAGE) data.PloS one · 2008Article
- Clustering-based approaches to SAGE data mining.BioData mining · 2008Article
- Using a seed-network to query multiple large-scale gene expression datasets from the developing retina in order to identify and prioritize experimental targets.Bioinformatics and biology insights · 2008Article
- An en masse phenotype and function prediction system for Mus musculus.Genome biology · 2008Article
- Identification of transcripts with enriched expression in the developing and adult pancreas.Genome biology · 2008Article
- Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework.BMC bioinformatics · 2007Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Serial analysis of gene expression (SAGE) data have been poorly exploited by clustering analysis owing to the lack of appropriate statistical methods that consider their specific properties. We modeled SAGE data by Poisson statistics and developed two Poisson-based distances. Their application to simulated and experimental mouse retina data show that the Poisson-based distances are more appropriate and reliable for analyzing SAGE data compared to other commonly used distances or similarity measures such as Pearson correlation or Euclidean distance.
Indexed as
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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.