ArticleBMC bioinformatics2010
A comparison of probe-level and probeset models for small-sample gene expression data.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Incorporation of subject-level covariates in quantile normalization of miRNA data.BMC genomics · 2015Article
- Accounting for dependence induced by weighted KNN imputation in paired samples, motivated by a colorectal cancer study.PloS one · 2015Article
- t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data.Microarrays (Basel, Switzerland) · 2014Article
- Ocular fibroblast types differ in their mRNA profiles--implications for fibrosis prevention after aqueous shunt implantation.Molecular vision · 2013Article
- Assessing numerical dependence in gene expression summaries with the jackknife expression difference.PloS one · 2012Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStatistical methods to tentatively identify differentially expressed genes in microarray studies typically assume larger sample sizes than are practical or even possible in some settings.
resultsThe performance of several probe-level and probeset models was assessed graphically and numerically using three spike-in datasets. Based on the Affymetrix GeneChip, a novel nested factorial model was developed and found to perform competitively on small-sample spike-in experiments.
conclusionsStatistical methods with test statistics related to the estimated log fold change tend to be more consistent in their performance on small-sample gene expression data. For such small-sample experiments, the nested factorial model can be a useful statistical tool. This method is implemented in freely-available R code (affyNFM), available with a tutorial document at http://www.stat.usu.edu/~jrstevens.
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