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Biomedical subjects

Bruce Weir

Publications and source records attributed to Bruce Weir.

2 recordsLinked to original sources

A systematic statistical linear modeling approach to oligonucleotide array experiments.

We outline and describe steps for a statistically rigorous approach to analyzing probe-level Affymetrix GeneChip data. The approach employs classical linear mixed models and operates on a gene-by-gene basis. Forgoing any attempts at gene presence or absence calls, the method simultaneously considers the data across all chips in an experiment. Primary output includes precise estimates of fold change (some as low as 1.1), their statistical significance, and measures of array and probe variability. The method can accommodate complex experiments involving many kinds of treatments and can test for their effects at the probe level. Furthermore, mismatch probe data can be incorporated in different ways or ignored altogether. Data from an ionizing radiation experiment on human cell lines illustrate the key concepts.

Cells, Cultured↗

Assessing sources of variability in microarray gene expression data.

Experiments using microarrays abound in genomic research, yet one factor remains in question. Without replication, how much stock can we put into the findings of microarray experiments? In addition, there is a growing desire to integrate microarray data with other molecular databases. To accomplish this in a scientifically acceptable manner, we must be able to measure the validity and quality of microarray data. Otherwise, it would be the weakest link in any integration process. Validating and evaluating the quality of data requires the ability to determine the reproducibility of results. Data obtained from a microarray experiment designed as a feasibility test provided a unique opportunity to partition and quantify several sources of variation that are likely to be present in most microarray experiments. We use this opportunity to discuss the origins of variability observed in microarray experiments and provide some suggestions for how to minimize or avoid them when designing an experiment.

Adolescent↗