Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations.

By M. Lee, F. Kuo, G. A. Whitmore, J. Sklar. Proceedings of the National Academy of Sciences of the United States of America, 2000

946 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1746ef5dd71d1c7e3b41fb59d06f7970d19a16d6

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Citations in the corpus

A Practical Approach to Microarray Data Analysis (2003, background): “Thus, microarrayers are becoming increasingly aware of the need for replications (Lee et al., 2000).”
Design issues for cDNA microarray experiments (2002, background): “It is more difficult to say how many replicates should be done, athough Lee et al”
A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments (2002, background): “It has also been noticed that data based on a single array may not be reliable and may contain high noises (Lee et al. , 2000).”
Statistical Design and Analysis of RNA Sequencing Data (2010, neutral): “2000; L ee et al .”
Fundamentals of cDNA microarray data analysis. (2003, background): “It has been suggested that every microarray experiment should be performed in triplicate to increase data reliability [16].”
On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles (2003, background): “[12] stress the importance of replication in microarray studies.”
The properties of high-dimensional data spaces: implications for exploring gene and protein expression data (2008, background): “The number of replicate estimates of each sample is usually limited; often only a single measurement is obtaine”
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).”
Mycobacterium tuberculosis Pathogenesis and Molecular Determinants of Virulence (2003, result): “In DNA array analyses, since the levels of 4,000 genes are being compared usually as a ratio between two conditions or bacterial strains, many factors such as background noise (84) and statistical considerations (137, 161) may give erroneous results for the values obtained with specific genes.”
Size, power and false discovery rates (2007, background): “11 , − Efron [8, 10] lists four reasons why f 0 might diﬀer from the theoretical null: 0 .”
A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes (2001, background): “…creating terabytes of information, potentially capable of providing fundamental insights into biological processes ranging from gene function, to development, to cancer (Spellman et al. , 1998; Alon et al. , 1999; Golub et al. , 1999; Lee et al. , 1999; White et al. , 1999; Ly et al. , 2000).”