Improved Statistical Inference from DNA Microarray Data Using Analysis of Variance and A Bayesian Statistical Framework

By Anthony D. Long, H. Mangalam, Bob Y. Chan, L. Tolleri, G. W. Hatfield, Pierre Baldi. Journal of Biological Chemistry, 2001

409 citations recorded by the source corpus.

We describe statistical methods based on thet test that can be conveniently used on high density array data to test for statistically significant differences between treatments. These t tests employ either the observed variance among replicates within treatments or a Bayesian estimate of the variance among replicates within treatments based on a prior estimate obtained from a local estimate of the standard deviation. The Bayesian prior allows statistical inference to be made from microarray data even when experiments are only replicated at nominal levels. We apply these new statistical tests to a data set that examined differential gene expression patterns in IHF+ and IHF− Escherichia coli cells (Arfin, S. M., Long, A. D., Ito, E. T., Tolleri, L., Riehle, M. M., Paegle, E. S., and Hatfield, G. W. (2000) J. Biol. Chem. 275, 29672–29684). These analyses identify a more biologically reasonable set of candidate genes than those identified using statistical tests not incorporating a Bayesian prior. We also show that statistical tests based on analysis of variance and a Bayesian prior identify genes that are up- or down-regulated following an experimental manipulation more reliably than approaches based only on a t test or fold change. All the described tests are implemented in a simple-to-use web interface called Cyber-T that is located on the University of California at Irvine genomics web site.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ec7278b95eaf7afb551fa27b262295bd21b4a710

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Citations in the corpus

Bayesian infinite mixture model based clustering of gene expression profiles (2002, neutral): “(Newton et al., 2001; Baldi and Long, 2001; Long et al., 2001; Lonnstedt and Speed, 2001).”
Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data (2003, background): “Variance shrinkage is a very good strategy to improve the estimation of variance (Long et al., 2001).”
Nonparametric methods for identifying differentially expressed genes in microarray data (2002, methodology): “The probabilistic approaches applied to microarray analysis include a t-test based on a Bayesian estimate of variance among experiment replicates with a Gaussian model for expression measurements (Long et al., 2001) and a hierarchical Bayesian modeling framework with Gaussian gene-independent models”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, methodology): “The ﬁ nal fusion rank of each feature can also be calculated as a weighted sum of the ranks assigned by the individual algorithms, where the weight of each algorithm is determined based on metrics such as the classi ﬁ cation performance of the algorithm (Long et al., 2001).”
Fundamentals of cDNA microarray data analysis. (2003, methodology): “Statistical methods such as Student’s t-test and its variants [17,18], ANOVA [19, 20 ], Bayesian method [17,20,21], or Mann–Whitney test [22], can be used to rank the genes from replicated data.”
A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes (2001, result): “While several biologically relevant results are reported elsewhere (Long et al. , 2001), we have found that the Bayesian approach compares favorably to a simple fold approach or a straight t -test and partially overcomes deﬁciencies related to low replication in a statistically consistent way.”