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Kerry Bemis

Publications and source records attributed to Kerry Bemis.

2 recordsLinked to original sources

SUM: a new way to incorporate mismatch probe measurements.

Affymetrix's high-density oligonucleotide arrays offer an exciting technology in biomedical research. With more and more statistical involvement in every step of the process, there has been a constant effort to make sure that the expression data are appropriately extracted in the first place. According to Affymetrix GeneChip technology, each gene is represented by 11-20 oligo probe pairs; the challenge is how to extract one meaningful number, expression, from the 11-20 pairs of numbers. More specifically, there is first a need to differentiate the components of specific binding, nonspecific binding, and optical background noise in both PM and MM probes, and then an expression measure that is proportional to the true abundance of transcripts is to be derived. A new method, SUM, which sums up PM and MM values and then follows a process similar to that of RMA, is considered. The performance of SUM is investigated and compared to the three most popular methods, MAS5, dChip, and RMA. The assessments are based on a well-controlled experiment dataset that is publicly available. The results show that in several respects the performance of SUM is comparable to that of RMA and dChip, and all three of these methods show some advantages over MAS5. There is some evidence showing that SUM has higher differential sensitivity than other methods in certain situations.

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Assessing the variability in GeneChip data.

INTRODUCTION: Oligonucleotide and cDNA microarray experiments are now common practice in biological science research. The goal of these experiments is generally to gain clues about the functions of genes by measuring how their expression levels rise and fall in response to changing experimental conditions. Measures of gene expression are affected, however, by a variety of factors. This paper introduces statistical methods to assess the variability of Affymetrix GeneChip data due to randomness. METHODS: The variation of Affymetrix's GeneChip signal data are quantified at both chip level and individual gene level, respectively, by the agreement study method and variance components method. Three agreement measurement methods are introduced to assess the variability among chips. Variation sources for gene expression data are decomposed into four categories: systematic experiment variation, treatment effect, biological variation, and chip variation. The focus of this paper is on evaluating and comparing the last two kinds of variations. RESULTS: Measurement of agreement and variance components methods were applied to an experimental data, and the calculation and interpretation were exemplified. The variability between biological samples were shown to exist and were assessed at both the chip level and individual gene level. Using the variance components method, it was found that the biological and chip variation are roughly comparable. The Statistical Analysis System (SAS) program for doing the agreement studies can be obtained from the correspondence author.

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