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Category Genomics>Gene Expression Analysis/Profiling/Tools Abstract ArrayStar is a microarray gene expression analysis system. Product provides a wide range of analytical and visualization tools for interpreting microarray gene expression data. Its advanced statistics can be applied to define differentially expressed genes, and its clustering algorithms can be used to identify genes with similar expression patterns. Products graphical and tabular views are synchronized so that subsets in one view are selected in other views. Microarray data can be imported into ArrayStar in a variety of file formats. It fully supports Affymetrix files and you can also download and import annotation files for the corresponding array directly from Affymetrix Netaffx Analysis Center. Nimblegen *.call and *.calls files can also be imported as well as delimited text files containing microarray data using the ArrayStar Data Import Wizard. This wizard allows you to specify the type of data in each column such as gene names, annotations, and external database identifiers. Products capabilities/features include: Scatter Plot - Multi-functional scatter plots can be generated that allow the user to select groups of genes for analysis. ArrayStar’s Scatter Plot view gives a visual comparison of gene expression levels between any two (2) datasets; whether they are individual arrays or replicated sets. Each data point on the Scatter Plot represents an individual gene and is plotted based on its expression level in both of the selected experiments. Data can be scaled and visualized as either linear or log2 values. Visualizations to assist in Gene Expression level analysis - For analysis across a series of experiments, such as a time series or a related set of conditions, two (2) advanced clustering algorithms are available in ArrayStar: Hierarchical Clustering and k-Means Clustering. 1) The Hierarchical Clustering method groups data points by clustering them one-by-one into ever-growing groups. After grouping all of the data points, the resulting clusters are displayed in a Heat Map. Heat Maps illustrate expression levels of the genes across a number of experiments. Genes can be selected within the Heat Map for additional analysis. 2) The k-Means Clustering method differs from the Heat Map method since it groups data points by partitioning them into a fixed number of arbitrary groups and then repeatedly refining the groups. This process is done by first randomly selecting one starting point for each cluster, and then grouping each of the data points to the closest starting point. The algorithm then defines a new center point for each group by finding the centroid, and each data point is then re-grouped to the closest center point. This process is repeated again and again, until the process No longer yields improvement. k-Means Clustering may be: a) Displayed in Line Graph Thumbnails; b) Displayed in Heat Map formats; c) Performed as single trial, or multiple trials may be run on one set of data points (performing multiple trials will create multiple sets of clusters); d) Performed on either the entire gene set or on a selected subset. Expression Level Changes - Line Graphs and Thumbnail Graphs - Line Graphs - ArrayStar allows users to visualize the expression level changes seen in individual genes over the course of the experiment through the use of Line Graphs. Any gene can be highlighted by passing your cursor over it to generate the graphical representation of its expression. Analytical features of this include: 1) Selection of desired gene reveals ontology information; 2) You can view comparisons of different gene expression levels. Note: ArrayStar’s Line Graph view plots the expression levels for selected genes over each experiment in your project, and connects the data points with a line so that expression levels are shown relative to one another across the group of experiments. Expression levels are plotted vertically along the Y-axis, while the X-axis position for each point is determined by the experiment to which it belongs. Thumbnail Line Graph - ArrayStar’s Line Graph Thumbnails view displays a series of Line Graphs generated from a clustering. Information obtained from the plots helps identify expression patterns with monitored clustering. Each individual Line Graph shows a visualization of the data contained within one cluster. Expression levels are plotted vertically along the Y-axis, while the X-axis position for each point is determined by the experiment to which it belongs. You can mouse-over a vertical gridline to view the experiment name. Data Analysis - ArrayStar provides users with several different methods for data analysis using statistical methods. Depending on the experiment and the type of information sought, different methods may be applied by the user. A) Probabilistic Statistical Analysis Methods - To use these statistical tools replicate samples are required. Variability is measured within the replicates. From the variability, confidence scores that are generated can be used to reflect differential gene expression. Methods available to users are: 1) Student t-test; 2) Moderated t-test; 3) F-test analysis of variance (ANOVA). After selection, ArrayStar calculates a P Value and a T/F Value for each gene. In general, if the T/F value is large, then the assumption can be made that the gene is differentially expressed. The P value represents the probability that the calculated T/F value occurred by chance. In general, the lower the P value, the more confident you can be that the gene is differentially expressed. Note: In addition to the probabilistic statistical analyses listed above, the following general statistics are also available in ArrayStar: a) Coefficient of Variation; b) Standard Deviation; c) Variance. B) Multiple Testing Corrections - Statistical tests like the Student’s t- Test, F-Test (ANOVA) and Moderated t-Test are used to identify differentially expressed genes. However, often with a large dataset, it’s possible to have a significant group of false positives. For example, a t-Test can be applied on a group of genes and those which have a p-value less than a certain value (0.05, for example) can be chosen as differentially expressed. However, when the test is performed on a large number of genes (order of 10,000), a significant number of genes (~500) that are Not actually differentially expressed will have a p-value lower than the set threshold and thus will be selected as differentially expressed. These genes are false positives, and this issue is referred to as the 'Multiple Testing' problem. Various adjustments can be made to the p-values with the objective of reducing the number of false positives. The adjustments available in ArrayStar are listed below, and can be applied to the p-values for any of the probabilistic statistical tests in ArrayStar. Bonferroni - In the Bonferroni method, the p-values for each gene are multiplied by N, where N is the total number of genes being tested. This increases the p-values to such a level, that very few genes are selected within the threshold. The Bonferroni method is highly conservative and while it reduces the number of false positives greatly, a number of truly differentially expressed genes are excluded. The Bonferroni method may be best utilized when looking for a small number of genes which are highly differentially expressed. Holm-Bonferroni - Using the Holm-Bonferroni method, the p-values are first sorted and then the smallest value is multiplied by N, where N is the total number of genes being tested. The next value is then multiplied by N-1 and so on, so that the last p-value is multiplied by 1. This method is Not as conservative as the Bonferroni method, but may still exclude many potentially interesting genes (false negatives). As with the Bonferroni method, the Holm-Bonferroni method may be best utilized when looking for a small number of genes for further experiments which are highly differentially expressed. In other words, this method can be effective when the goal is to just eliminate false positives even if it is at the cost of a number of false negatives. FDR (Benjamini Hochberg) - The FDR (Benjamini Hochberg) method is the default P-value adjustment method in ArrayStar. In this method, the p-values are first sorted and ranked. The smallest value gets rank 1, the second rank 2, and the largest gets rank N. Then, each p-value is multiplied by N and divided by its assigned rank to give the adjusted p-values. In order to restrict the false discovery rate to (say) 0.05, all the genes with adjusted p-values less than 0.05 are selected. This method aims to reduce what is called the False Discovery Rate (FDR) and is used when the objective is to reduce the number of false positives and to increase the chances of identifying all the differentially expressed genes. C) Filtering - The Filtering capability of ArrayStar permits users to modify gene searches in a number of different ways. Criteria that can be used include: 1) Fold Change; 2) Gene Annotations; 3) Expression Levels; 4) Statistics. Fold-change analysis is a simple method used to identify genes with expression ratios or differences between a treatment and a control that are outside of a given cutoff or threshold. ArrayStar permits, in its Filtering mode, searches to be conducted on Fold Change levels that are determined by the user. Note: In the Scatter Plot image, a range of pre-set Fold Change levels are provided. Gene Annotation permits filtering based on the selected genes that have annotations entered. Expression Levels and Statistics permit users to define filter criteria in each for the search. Note: See G6G Product Number 20071 for additional product info from this manufacturer. System Requirements ArrayStar (Windows® computer running XP or Vista™)
1 GHz or faster x86 CPU 384 MB of RAM (512MB RAM on Vista™) 80 MB free hard drive space for installation (additional 280 MB) required on XP if .NET 2.0 is not installed Internet access (required to install, recommended for NetAffx™ usage) Manufacturer Home office; see web site for international locations.
3801 Regent Street Madison, WI 53705 USA Phone: 1 608-258-7420 Toll Free: 1 866-511-5090 Toll free calls from the U.K.: 0-808-234-1643 FAX: 1 608-258-7439 Email: info@dnastar.com
Price Contact manufacturer G6G Product Number 20058 G6G Manufacturer Number 100770 |
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