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Biomedical subjects

Kevin Dobbin

Publications and source records attributed to Kevin Dobbin.

8 recordsLinked to original sources
PubMed2005

Sample size determination in microarray experiments for class comparison and prognostic classification.

Determining sample sizes for microarray experiments is important but the complexity of these experiments, and the large amounts of data they produce, can make the sample size issue seem daunting, and tempt researchers to use rules of thumb in place of formal calculations based on the goals of the experiment. Here we present formulae for determining sample sizes to achieve a variety of experimental goals, including class comparison and the development of prognostic markers. Results are derived which describe the impact of pooling, technical replicates and dye-swap arrays on sample size requirements. These results are shown to depend on the relative sizes of different sources of variability. A variety of common types of experimental situations and designs used with single-label and dual-label microarrays are considered. We discuss procedures for controlling the false discovery rate. Our calculations are based on relatively simple yet realistic statistical models for the data, and provide straightforward sample size calculation formulae.

Biomarkers↗
PubMed2005

Prostate cancer in patients with screening serum prostate specific antigen values less than 4.0 ng/dl: results from the cooperative prostate cancer tissue resource.

PURPOSE: Prostate cancer can occur in patients with low screening serum prostate specific antigen (PSA) values (less than 4.0 ng/ml). It is currently unclear whether these tumors are different from prostate cancer in patients with high PSA levels (greater than 4.0 ng/ml). MATERIALS AND METHODS: From the Cooperative Prostate Cancer Tissue Resource database through March 2004, 3,416 patients with screening PSA less than 16.0 ng/ml diagnosed with prostate cancer between 1993 and 2004 were stratified in groups based on screening serum PSA. These subsets were compared for race, age at diagnosis, clinical and pathological stage, Gleason score, positive surgical margins, posttreatment recurrent disease, and vital status. RESULTS: We identified 468 (14%) patients with screening PSA less than 4.0 ng/ml, 142 (4.2%) of whom had a PSA of less than 2.0 ng/ml. This group included 40 black and 376 white patients. Men with low screening PSA treated with radical prostatectomy had smaller cancers, lower Gleason scores, lower pathological tumor (T) stage and lower PSA recurrence rates than men with high PSA levels (4 ng/ml or greater). These differences held true for men who were younger than 62 years or were white, whereas older or black men had tumor characteristics and outcomes similar to those with higher PSA levels. CONCLUSIONS: Young (younger than 62 years) or white patients with screening serum PSA less than 4.0 ng/ml had smaller, lower grade tumors and lower recurrence rates than patients with PSA 4.0 ng/ml or greater. This was not true for those older than 62 years and for black men.

Humans↗
PubMed2004

Effects of pooling mRNA in microarray class comparisons.

MOTIVATION: In microarray experiments investigators sometimes wish to pool RNA samples before labeling and hybridization due to insufficient RNA from each individual sample or to reduce the number of arrays for the purpose of saving cost. The basic assumption of pooling is that the expression of an mRNA molecule in the pool is close to the average expression from individual samples. Recently, a method for studying the effect of pooling mRNA on statistical power in detecting differentially expressed genes between classes has been proposed, but the different sources of variation arising in microarray experiments were not distinguished. Another paper recently did take different sources of variation into account, but did not address power and sample size for class comparison. In this paper, we study the implication of pooling in detecting differential gene expression taking into account different sources of variation and check the basic assumption of pooling using data from both the cDNA and Affymetrix GeneChip microarray experiments. RESULTS: We present formulas for the required number of subjects and arrays to achieve a desired power at a specified significance level. We show that due to the loss of degrees of freedom for a pooled design, a large increase in the number of subjects may be required to achieve a power comparable to that of a non-pooled design. The added expense of additional samples for the pooled design may outweigh the benefit of saving on microarray cost. The microarray data from both platforms show that the major assumption of pooling may not hold. SUPPLEMENTARY INFORMATION: Supplementary material referenced in the text is available at http://linus.nci.nih.gov/brb/TechReport.htm.

Algorithms↗
PubMed2004

The tissue microarray data exchange specification: implementation by the Cooperative Prostate Cancer Tissue Resource.

BACKGROUND: Tissue Microarrays (TMAs) have emerged as a powerful tool for examining the distribution of marker molecules in hundreds of different tissues displayed on a single slide. TMAs have been used successfully to validate candidate molecules discovered in gene array experiments. Like gene expression studies, TMA experiments are data intensive, requiring substantial information to interpret, replicate or validate. Recently, an open access Tissue Microarray Data Exchange Specification has been released that allows TMA data to be organized in a self-describing XML document annotated with well-defined common data elements. While this specification provides sufficient information for the reproduction of the experiment by outside research groups, its initial description did not contain instructions or examples of actual implementations, and no implementation studies have been published. The purpose of this paper is to demonstrate how the TMA Data Exchange Specification is implemented in a prostate cancer TMA. RESULTS: The Cooperative Prostate Cancer Tissue Resource (CPCTR) is funded by the National Cancer Institute to provide researchers with samples of prostate cancer annotated with demographic and clinical data. The CPCTR now offers prostate cancer TMAs and has implemented a TMA database conforming to the new open access Tissue Microarray Data Exchange Specification. The bulk of the TMA database consists of clinical and demographic data elements for 299 patient samples. These data elements were extracted from an Excel database using a transformative Perl script. The Perl script and the TMA database are open access documents distributed with this manuscript. CONCLUSIONS: TMA databases conforming to the Tissue Microarray Data Exchange Specification can be merged with other TMA files, expanded through the addition of data elements, or linked to data contained in external biological databases. This article describes an open access implementation of the TMA Data Exchange Specification and provides detailed guidance to researchers who wish to use the Specification.

Confidentiality↗
PubMed2002

Design of studies using DNA microarrays.

DNA microarrays are assays that simultaneously provide information about expression levels of thousands of genes and are consequently finding wide use in biomedical research. In order to control the many sources of variation and the many opportunities for misanalysis, DNA microarray studies require careful planning. Different studies have different objectives, and important aspects of design and analysis strategy differ for different types of studies. We review several types of objectives of studies using DNA microarrays and address issues such as selection of samples, levels of replication needed, allocation of samples to dyes and arrays, sample size considerations, and analysis strategies.

Computational Biology↗