Microarray data analysis: from disarray to consolidation and consensus

By David B. Allison, Xiangqin Cui, Grier P. Page, M. Sabripour. Nature reviews genetics, 2006

1,473 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7aeace2306487c5e5f3ff6711e1a5d3fc1e4c899

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Citations in the corpus

Computational solutions for omics data (2013, background): “Also relevant are excellent review articles on more traditional expression analysis 59,60 , data normalization 61 and RNA-seq data analysis 62,56 (for example, integrated pipelines such as Tophat, Cufflinks and Cuffdiff 54,63–65 ).”
CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research (2019, neutral): “There is no clear consensus in which algorithm is superior (Allison et al., 2006), but some studies point to SVMs and RF as the stronger contends (Lee et al., 2005; Pirooznia et al., 2008; Statnikov et al., 2008).”
The properties of high-dimensional data spaces: implications for exploring gene and protein expression data (2008, methodology): “Our goal in this Review is not to provide a guide to data analysis, as this has been done by others11,12.”
The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies (2008, methodology): “[16] collectively described these methods as "variance shrinkage" approaches.”
Fold change and p-value cutoffs significantly alter microarray interpretations (2012, background): “As more and more genomes are sequenced and annotated, the capacity to accurately and efficiently catalog the gene expression profiles of these organisms is becoming ever more apparent [1].”
Gene-set approach for expression pattern analysis (2008, background): “However, this feature of relativism can cause the peculiar behavior of a ‘zero-sum game’ [23, 24].”
GREAT improves functional interpretation of cis-regulatory regions (2010, methodology): “Functional interpretation is then performed using gene-based tools developed in the wake of the preceding microarray revolutio”
Multi-omic and multi-view clustering algorithms: review and cancer benchmark (2018, background): “The measured parameters include DNA genome sequence (1), RNA expression (2,3), DNA methylation (4) etc.”
Rank-Based Inverse Normal Transformations are Increasingly Used, But are They Merited? (2009, neutral): “However, see Allison et al. (2006) and references therein for nuances and caveats.”
RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. (2008, background): “Alternatively, with appropriate transformations of the count data, existing approaches for microarray experiments (Allison et al. 2006) may also work well.”
A method for generating realistic correlation matrices (2011, background): “Indeed, there has been a call for work that seeks to determine 31 the reproducibility of clustering methods instead of continued work on developing new 32 clustering algorithms [1, 37].”
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015, methodology): “For differential gene expression (DGE), RNA-seq studies should observe existing analytical and statistical guidelines for microarrays 221 and, if possible, should carry out pilot experiments to estimate power 222 .”