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BMC Bioinformatics, Vol. 4, No. 1. (11 December 2003)BACKGROUND: With the increasing amount of data generated in molecular genetics laboratories, it is often difficult to make sense of results because of the vast number of different outcomes or variables studied. Examples include expression levels for large numbers of genes and haplotypes at large numbers of loci. It is then natural to group observations into smaller numbers of classes that allow for an easier overview and interpretation of the data. This grouping is often carried out in multiple steps with the aid of hierarchical cluster analysis, each step leading to a smaller number of classes by combining similar observations or classes. At each step, either implicitly or explicitly, researchers tend to interpret results and eventually focus on that set of classes providing the "best" (most significant) result. While this approach makes sense, the overall statistical significance of the experiment must include the clustering process, which modifies the grouping structure of the data and often removes variation. RESULTS: For hierarchically clustered data, we propose considering the strongest result or, equivalently, the smallest p-value as the experiment-wise statistic of interest and evaluating its significance level for a global assessment of statistical significance. We apply our approach to datasets from haplotype association and microarray expression studies where hierarchical clustering has been used. CONCLUSION: In all of the cases we examine, we find that relying on one set of classes in the course of clustering leads to significance levels that are too small when compared with the significance level associated with an overall statistic that incorporates the process of clustering. In other words, relying on one step of clustering may furnish a formally significant result while the overall experiment is not significant.MA Levenstien, Y Yang, J Ott, citeulike.org

Physiol Genomics, Vol. 21, No. 1. (21 March 2005), pp. 92-104.Microarrays have been used to evaluate the expression of thousands of genes in various tissues. However, few studies have investigated the change in gene expression profiles in one of the most easily accessible tissues, whole blood. We utilized an acute inflammation model to investigate the possibility of using a cDNA microarray to measure the gene expression profile in the cells of whole blood. Blood was collected from male Sprague-Dawley rats at 2 and 6 h after treatment with 5 mg/kg (ip) LPS. Hematology showed marked neutrophilia accompanied by lymphopenia at both time points. TNF-alpha and IL-6 levels were markedly elevated at 2 h, indicating acute inflammation, but by 6 h the levels had declined. Total RNA was isolated from whole blood and hybridized to the National Institute of Environmental Health Sciences Rat Chip v.3.0. LPS treatment caused 226 and 180 genes to be differentially expressed at 2 and 6 h, respectively. Many of the differentially expressed genes are involved in inflammation and the acute phase response, but differential expression was also noted in genes involved in the cytoskeleton, cell adhesion, oxidative respiration, and transcription. Real-time RT-PCR confirmed the differential regulation of a representative subset of genes. Principal component analysis of gene expression discriminated between the acute inflammatory response apparent at 2 h and the observed recovery underway at 6 h. These studies indicate that, in whole blood, changes in gene expression profiles can be detected that are reflective of inflammation, despite the adaptive shifts in leukocyte populations that accompany such inflammatory processes.RD Fannin, JT Auman, ME Bruno, SO Sieber, SM Ward, CJ Tucker, BA Merrick, RS Paules, citeulike.org

Proc Natl Acad Sci U S A, Vol. 102, No. 7. (15 February 2005), pp. 2448-2453.The hematopoietic stem cell (HSC) compartment is composed of long-term reconstituting (LTR) and short-term reconstituting (STR) stem cells. LTR HSC can reconstitute the hematopoietic system for life, whereas STR HSC can sustain hematopoiesis for only a few weeks in the mouse. Several excellent gene expression profiles have been obtained of the total hematopoietic stem cell population. We have used five-color FACS sorting to isolate separate populations of LTR and STR stem cell subsets. The LTR HSC has the phenotype defined as Lin- Sca+ Kit+ 38+ 34-; two subsets of STR HSC were obtained with phenotypes of Lin- Sca+ Kit+ 38+ 34+ and Lin- Sca+ Kit+ 38- 34+. The microarray profiling study reported here was able to identify genes specific for LTR functions. In the interrogated genes (approximately 12,000 probe sets corresponding to 8,000 genes), 210 genes are differentially expressed, and 72 genes are associated with LTR activity, including membrane proteins, signal transduction molecules, and transcription factors. Hierarchical clustering of the 210 differentially expressed genes suggested that they are not bone marrow-specific but rather appear to be stem cell-specific. Transcription factor-binding site analysis suggested that GATA3 might play an important role in the biology of LTR HSC.JF Zhong, Y Zhao, S Sutton, A Su, Y Zhan, L Zhu, C Yan, T Gallaher, PB Johnston, WF Anderson, MP Cooke, citeulike.org

Hum Mol Genet, Vol. 11, No. 19. (15 September 2002), pp. 2223-2232.A randomization procedure to evaluate the significance level and the false-discovery rate in complex microarray experiments is proposed. A related graph can be used to compare different test statistics that can be used to analyze the same experiment. This graph is closely related to receiver operator characteristic (ROC) curves. The proposed method is applied to a subset of the data from a cell-line experiment related to Huntington's disease. A small simulation study compares the effectiveness of the proposed procedure with the significance analysis of microarrays (SAM) procedure.C Kooperberg, S Sipione, M LeBlanc, AD Strand, E Cattaneo, JM Olson, citeulike.org

Nat Genet, Vol. 32 Suppl (December 2002), pp. 496-501.Underlying every microarray experiment is an experimental question that one would like to address. Finding a useful and satisfactory answer relies on careful experimental design and the use of a variety of data-mining tools to explore the relationships between genes or reveal patterns of expression. While other sections of this issue deal with these lofty issues, this review focuses on the much more mundane but indispensable tasks of 'normalizing' data from individual hybridizations to make meaningful comparisons of expression levels, and of 'transforming' them to select genes for further analysis and data mining.J Quackenbush, citeulike.org

Lancet, Vol. 365, No. 9458. (5 February 2005), pp. 454-455.JP Ioannidis, citeulike.org

Nature Methods, Vol. 2, No. 5. (21 April 2005), pp. 337-344.Jennie Larkin, Bryan Frank, Haralambos Gavras, Razvan Sultana, John Quackenbush, citeulike.org

Plant J, Vol. 33, No. 5. (March 2003), pp. 911-921.With the availability of microarray technology, the expression profiles of thousands of genes can be monitored simultaneously to help determine the mechanisms of these biological processes. We conducted Affymetrix GeneChip microarray analyses of the Arabidopsis-cyst nematode interaction and employed a statistical procedure to analyze the resultant data, which allowed us to identify significant gene expression changes. Quantitative real-time RT-PCR assays were used to confirm the microarray analyses. The results of the expression profiling revealed 128 genes with altered steady-state mRNA levels following infection by the sugar beet cyst nematode (Heterodera schachtii; BCN), in contrast to only 12 genes that had altered expression following infection by the soybean cyst nematode (H. glycines; SCN). The expression of these 12 genes also changed following infection by BCN, i.e. we did not identify any genes regulated exclusively by SCN. The identification of 116 genes whose expression changes during successful cyst nematode parasitism by BCN suggests a potential involvement of these genes in the infection events starting with successful syncytium induction. Further characterization of these genes will permit the formulation of testable hypotheses to explain successful cyst nematode parasitism.DP Puthoff, D Nettleton, SR Rodermel, TJ Baum, citeulike.org

BMC Genomics, Vol. 5, No. 1. (27 February 2004)BACKGROUND: Gene microarray technology provides the ability to study the regulation of thousands of genes simultaneously, but its potential is limited without an estimate of the statistical significance of the observed changes in gene expression. Due to the large number of genes being tested and the comparatively small number of array replicates (e.g., N = 3), standard statistical methods such as the Student's t-test fail to produce reliable results. Two other statistical approaches commonly used to improve significance estimates are a penalized t-test and a Z-test using intensity-dependent variance estimates. RESULTS: The performance of these approaches is compared using a dataset of 23 replicates, and a new implementation of the Z-test is introduced that pools together variance estimates of genes with similar minimum intensity. Significance estimates based on 3 replicate arrays are calculated using each statistical technique, and their accuracy is evaluated by comparing them to a reliable estimate based on the remaining 20 replicates. The reproducibility of each test statistic is evaluated by applying it to multiple, independent sets of 3 replicate arrays. Two implementations of a Z-test using intensity-dependent variance produce more reproducible results than two implementations of a penalized t-test. Furthermore, the minimum intensity-based Z-statistic demonstrates higher accuracy and higher or equal precision than all other statistical techniques tested. CONCLUSION: An intensity-based variance estimation technique provides one simple, effective approach that can improve p-value estimates for differentially regulated genes derived from replicated microarray datasets. Implementations of the Z-test algorithms are available at http://vessels.bwh.harvard.edu/software/papers/bmcg2004.J Comander, S Natarajan, MA Gimbrone, G García-Cardeña, citeulike.org

Biostatistics, Vol. 4, No. 2. (April 2003), pp. 249-264.In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.RA Irizarry, B Hobbs, F Collin, YD Beazer-Barclay, KJ Antonellis, U Scherf, TP Speed, citeulike.org