A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments

By Wei Pan. Bioinform., 2002

575 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/460bc5461711b9dec056438a9197c4565bcc8cfb

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Citations in the corpus

Gene regulatory network inference: Data integration in dynamic models - A review (2009, methodology): “, n at time t, f is the function that describes the rate f change of the state variables xi in dependence on the model arameter set p, and the externally given perturbation signals u. s 96 (2009) 86–103 91”
Estimating the Occurrence of False Positives and False Negatives in Microarray Studies by Approximating and Partitioning the Empirical Distribution of P-values (2003, methodology): “Many methods that address the multiple-testing issue compare the expressions of genes across two treatments (Pan, 2002).”
Post-analysis follow-up and validation of microarray experiments (2002, methodology): “The microarray field is rapidly evolving, and the number of publications on methods is substantia”
Nonparametric methods for identifying differentially expressed genes in microarray data (2002, methodology): “Pan (2002) used a mixture modeling approach that estimates the distribution of t-statistic-type scores using normal mixture models and compared it with two parametric approaches, including a regular t-test.”
9) TM4 Microarray Software Suite (2006, unknown): “MeV provides three t test (Dudoit et al., 2000; Korn, et al., 2001, 2004; Pan, 2002; Welch, 1947; Zar, 1999) designs: one sample, between subjects, and paired.”
Detecting differential gene expression with a semiparametric hierarchical mixture method. (2004, background): “…innocuous assumption is the discrete mixture model, which is becoming popular in microarray data analysis (Newtonet al., 2001; Efronet al., 2001; Allisonet al., 2002; Bröet et al., 2002; Leeet al., 2002; Lonnstedt and Speed, 2002; Pan, 2002; Kendziorskiet al., 2003; Storey and Tibshirani, 2003).”
Penalized Model-Based Clustering with Application to Variable Selection (2007, background): “…eight genes had their cluster-specific mean estimates as 0's across all seven clusters, and hence were regarded as non-informative; previous studies demonstrated that there were indeed a large number of the genes differentially expressed between ALL and AML samples (Thomas et al., 2001; Pan, 2002).”