Many different methods have been proposed to test for geographical disease clustering, and more generally, for spatial clustering of any type of observations while adjusting for an inhomogeneous background population generating the observations. Despite the many proposed test statistics, there has been few formal comparisons conducted. We present a collection of 1,220,000 simulated benchmark data sets generated under 51 different cluster models and the null hypothesis, to be used for power evaluations. We then use these data sets to compare the power of the spatial scan statistic, the maximized excess events test and the nonparametric M statistic. All have good power, the first having an advantage for localized hot-spot type clusters and the second for global clustering where randomly located cases generate other cases close by. By making the simulated data sets publicly available, new tests can easily be compared with previously evaluated tests by analyzing the same benchmark data.
MOTIVATION: Many have observed a nonlinear relationship between the signal intensity and the transcript abundance in microarray data. The first step in analyzing the data is to normalize it properly, and this should include a correction for the nonlinearity. The commonly used linear normalization schemes do not address this problem. RESULTS: Nonlinearity is present in both cDNA and oligonucleotide arrays, but we concentrate on the latter in this paper. Across a set of chips, we identify those genes whose within-chip ranks are relatively constant compared to other genes of similar intensity. For each gene, we compute the sum of the squares of the differences in its within-chip ranks between every pair of chips as our statistic and we select a small fraction of the genes with the minimal changes in ranks at each intensity level. These genes are most likely to be non-differentially expressed and are subsequently used in the normalization procedure. This method is a generalization of the rank-invariant normalization (Li and Wong, 2001), using all available chips rather than two at a time to gather more information, while using the chip that is least likely to be affected by nonlinear effects as the reference chip. The assumption in our method is that there are at least a small number of nondifferentially expressed genes across the intensity range. The normalized expression values can be substantially different from the unnormalized values and may result in altered down-stream analysis.
We developed a novel method for the discovery of functional relationships between pairs of genes based on gene expression profiles generated from microarrays. This approach examines all possible pairs of genes and identifies those in which the relationship between the two genes changes in different diseases or conditions. In contrast to previous methods that have focused on differentially expressed genes, this method attempts to find changes in the correlation between genes. These changes may be indicative of the functional relationships related to a disease mechanism. We demonstrate the utility of this approach by applying it to an oral squamous cell carcinoma (OSCC) microarray data set. Our results suggest new directions for future experimental investigations.
Natural killer cells constitute 50-90% of lymphocytes in human uterine decidua in early pregnancy. Here, CD56(bright) uterine decidual NK (dNK) cells were compared with the CD56(bright) and CD56(dim) peripheral NK cell subsets by microarray analysis, with verification of results by flow cytometry and RT-PCR. Among the approximately 10,000 genes studied, 278 genes showed at least a threefold change with P < or = 0.001 when comparing the dNK and peripheral NK cell subsets, most displaying increased expression in dNK cells. The largest number of these encoded surface proteins, including the unusual lectinlike receptors NKG2E and Ly-49L, several killer cell Ig-like receptors, the integrin subunits alpha(D), alpha(X), beta1, and beta5, and multiple tetraspanins (CD9, CD151, CD53, CD63, and TSPAN-5). Additionally, two secreted proteins, galectin-1 and progestagen-associated protein 14, known to have immunomodulatory functions, were selectively expressed in dNK cells.
BACKGROUND: The Human Genome Project, or HGP, has inspired a great deal of exciting biology recently by enabling the development of new technologies that will be essential for understanding the different types of abnormalities in diseases related to the oral cavity. LITERATURE REVIEWED: The authors review current literature pertaining to the advanced microarray technologies arising from the HGP and how they can contribute to dentistry. This technology has become a standard tool for monitoring activities of genes at both academic and pharmaceutical research institutions. RESULTS: With the availability of the DNA sequences for the entire human genome, attention now is focused on understanding various diseases at the genome level. Deciphering the molecular behavior of genetically encoded proteins is crucial to obtaining a more comprehensive picture of disease processes. Important progress has been made using microarrays, which have been shown to be effective in identifying gene expression patterns and variations that correlate with cellular development, physiology and function. Arrays can be used to classify tissue samples accurately based on molecular profiles and to select candidate genes related to a number of cancers, including oral cancer. This type of oral genetic approach will aid in the understanding of disease progression, thus improving diagnosis and treatment for patients. CLINICAL IMPLICATIONS: Microarrays hold much promise for the analysis of diseases in the oral cavity. As the technology evolves, dentists may see these tools as screening tests for better managing patients' dental care.
BACKGROUND: Serial analysis of gene expression using small amounts of starting material (microSAGE) has not yet been conclusively shown to be representative, reproducible or accurate. RESULTS: We show that microSAGE is highly representative, reproducible and accurate, but that pronounced differences in gene expression are seen between tissue samples taken from different individuals. CONCLUSIONS: MicroSAGE is a reliable method of comprehensively profiling differences in gene expression among samples, but care should be taken in generalizing results obtained from libraries constructed from tissue obtained from different individuals and/or processed or stored differently.