Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application

By Cheng Li, Wing Hung Wong. Genome Biology, 2001

1,078 citations recorded by the source corpus.

BackgroundA model-based analysis of oligonucleotide expression arrays we developed previously uses a probe-sensitivity index to capture the response characteristic of a specific probe pair and calculates model-based expression indexes (MBEI). MBEI has standard error attached to it as a measure of accuracy. Here we investigate the stability of the probe-sensitivity index across different tissue types, the reproducibility of results in replicate experiments, and the use of MBEI in perfect match (PM)-only arrays.ResultsProbe-sensitivity indexes are stable across tissue types. The target gene's presence in many arrays of an array set allows the probe-sensitivity index to be estimated accurately. We extended the model to obtain expression values for PM-only arrays, and found that the 20-probe PM-only model is comparable to the 10-probe PM/MM difference model, in terms of the expression correlations with the original 20-probe PM/MM difference model. MBEI method is able to extend the reliable detection limit of expression to a lower mRNA concentration. The standard errors of MBEI can be used to construct confidence intervals of fold changes, and the lower confidence bound of fold change is a better ranking statistic for filtering genes. We can assign reliability indexes for genes in a specific cluster of interest in hierarchical clustering by resampling clustering trees. A software dChip implementing many of these analysis methods is made available.ConclusionsThe model-based approach reduces the variability of low expression estimates, and provides a natural method of calculating expression values for PM-only arrays. The standard errors attached to expression values can be used to assess the reliability of downstream analysis.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/68fd4fb3b55b9ae64530022a1f778be6da6c6921

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Computational cluster validation in post-genomic data analysis (2005, methodology): “Finally, the stability of a clustering result can also be assessed by comparing the partitions obtained for perturbed data (Bittner et al., 2000; Kerr and Churchill, 2001; Li and Wong, 2001).”
Systematic determination of patterns of gene expression during Drosophila embryogenesis (2002, methodology): “Consecutive time points representing RNA samples from differentially aged 1-h embryo collections are presented on the x-axis, while the y-axis represents the scale of absolute expression indexes as analyzed by dChip analysis software [37].”
Studying and modelling dynamic biological processes using time-series gene expression data (2012, methodology): “If not, some normalization methods, such as dChip 30 , that do not rely on total RNA quantities should be preferred.”
Statistical tests for differential expression in cDNA microarray experiments (2003, methodology): “The process of creating a derived data set and computing the F tests described above can be integrated in one step by applying [20,35] our fixed-effects ANOVA model [9]; further discussion is provided Lee et al. [34].”
A comparison of normalization methods for high density oligonucleotide array data based on variance and bias (2003, methodology): “This method is used in Li and Wong (2001) and implemented in the dChip software http://www.dchip. org.”
affy - analysis of Affymetrix GeneChip data at the probe level (2004, methodology): “Details concerning the normalization methods included in the package can be found in their respective references: loess (Åstrand, 2003), invariantset (Li and Wong, 2001b), qspline (Workman et al. , 2002) and quantiles (Bolstad, 2001).”