Microarray data normalization and transformation

By John Quackenbush. Nature Genetics, 2002

2,157 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b901366e06ea924ab5ad9a27611d811d004a8ee7

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In Molecular Biology

Citations in the corpus

From patterns to pathways: gene expression data analysis comes of age (2002, background): “Although log transformation can improve normality (see review by J. Quackenbush, pages 496–501, this issue) 19 and help equalize variances, ultimately the best estimates of the data’s distribution come from the data themselves.”
A custom microarray platform for analysis of microRNA gene expression (2004, methodology): “Several normalization methods have been developed for microarray data (see ref”
Persisters: a distinct physiological state of E. coli (2006, methodology): “Fluorescence intensity dependent effects in log2(ratio) values were removed by using locally weighted linear regression (lowess) procedure [40].”
PAGE: Parametric Analysis of Gene Set Enrichment (2005, positive): “Since inception of the microarray technology, significant technological and analytical improvements have been introduced to meet these challenges, from experimental design [1], probe-level analysis of oligonucleotide chips [3,4], data normalization [5], statistical analysis [6], clustering technique”
Data Analysis Strategies in Medical Imaging (2018, background): “Microarray data analysis field, for example, although now a mature field, initially struggled with many obstacles related to data normalization (16), batch effects (17, 18), replicability (19), and the use of gene expression profiles for disease subtyping (20) and classification (21).”
Recruitment to the Nuclear Periphery Can Alter Expression of Genes in Human Cells (2008, methodology): “A mean log2 tethered/untethered ratio for each oligo was calculated from the remaining values [40].”
ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. (2004, background): “We will not cover these issues here, since they have been reviewed extensively elsewhere [39–43].”
Revisiting Global Gene Expression Analysis (2012, neutral): “Assumptions and Interpretation Almost all global expression analysis involves isolation of RNA from two or more cellular sources, introducing similar amounts of RNA from the sources into the experimental platform and analyzing the data by using algorithms that normalize the signal from the samples (”
The rise of "big data" on cloud computing: Review and open research issues (2015, methodology): “Normalization is the method of structuring database schema to minimize redundancy [26].”
An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex (2014, methodology): “Any FPKM that is 0.1 were set to 0.1 for fold enrichment calculations to avoid ratio inflation (Quackenbush, 2002).”
An insight-based methodology for evaluating bioinformatics visualizations (2005, methodology): “Since our study is focused on the interactive visualization portion of data analysis, the data sets were preprocessed, normalized, prefiltered, and converted to the required formats (as discussed in [31] and [32]) in advance.”
Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks (2012, background): “Thus, the former is likely to be less robust to noisy data such as gene expression [49,51,52]).”
Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes (2013, positive): “Additional approaches can be borrowed from the molecular genetics community, which has made noteworthy strides in dealing with unwanted variation in microarray technologies and procedures (see Quackenbush, 2002 for a review).”