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Category Genomics>Gene Expression Analysis/Profiling/Tools Abstract TMA Foresight is a tissue microarray data analysis software product designed to explore the relatedness of 'prognostic marker expression' and clinico-pathological variates with the outcome. It identifies important prognostic markers that influence the outcome and identifies prognostically significant clusters of patients using statistical techniques such as Cox Regression, Hierarchical Clustering and Survival Analysis (see Note 1) using Kaplan-Meier Survival Plots. Based on the data provided, it helps decide the risk group of a cohort. TMA Foresight enables easy data pre-processing. The data can be categorized, replaced or ignored from a single screen. Missing data is easily filled in depending on the measurement level chosen, ensuring completeness of data for further analysis. Data can be filtered for customized analysis using logical operators. You can then apply multivariate statistical techniques such as Cox proportional hazard model to identify prognostic markers, hierarchical clustering and Kaplan Meier survival plots to identify prognostically significant clusters and biomarkers and their impact on the outcome. Correlation analysis can be performed to measure the association between the variables. This is useful in validating cDNA microarray data by finding the correlation between the gene copy number and protein expression. Principal component analysis enables you to analyze a multi-dimensional data set. Reducing the dimensionality helps cluster the patients into prognostically significant groups. TMA Foresight Not only analyzes the data but interprets it too, making it a useful tool for pathologists, clinicians and researchers. Additional product features/capabilities include: 1) Mapping: TMA data is usually both quantitative and qualitative. The qualitative variables may be character or alphanumeric. For any kind of analysis such variables need to be transformed to a numeric scale. TMA Foresight helps map character data to numeric values with a click of a button, so that you do Not have to bother with entering the data yourself. You can even define the measurement level of each variable. 2) Replacing Missing Values: TMA Foresight assists in replacing the missing values for biomarkers or clinico-pathological parameters based on their measurement levels. This ensures the completeness of data for further analysis. 3) Descriptive Statistics: TMA Foresight calculates the mean, standard deviation and displays the range of different parameters. The information helps you quickly identify any abnormalities in the data. 4) Cox Regression: This multivariate tool is used to identify prognostically significant markers and clinico-pathological parameters that have a significant impact on the outcome. The survival or recurrence function provides information about the risk of death or recurrence of a disease for a cohort. 5) Hierarchical Clustering: Tissue microarray software is used for grouping patients into relatively homogeneous sub-groups based on a set of variables. It identifies prognostically significant clusters of the patients based on biomarkers/clinico-pathological variables. The survival information of patients within each cluster is used to determine whether the clusters formed are significantly different from each other. TMA Foresight enables you to move the linkage bar over the dendogram which updates the Kaplan Meier plot and results of the Log Rank test accordingly. This functionality helps in determining prognostically significant clusters and identifying high and low risk groups patients within a cohort. 6) Kaplan-Meier Survival Plot: This tool is used to visualize the Kaplan Meier survival and recurrence rate(s) for a cohort. You can partition the data based on a single variable and compare the survival functions. The significance of difference in the Kaplan Meier survival rates for a cohort can be tested using the log-rank test. 7) Data Filtering: This tool allows you to filter the data set based on certain set of conditions that help you to accomplish specific research goals. 8) Correlation Analysis: This tool measures the strength of association between any two variables. You can also analyze the partial association between two variables by controlling the effect of the third. This functionality may help in understanding the genomic and proteomic level alterations in patients. 9) Principal Component Analysis (PCA): This tool reduces the dimensionality of the data set while retaining the variation in the data set as much as possible. TMA Foresight provides an axis to move over the 2D scatter plots to quickly generate clusters. 10) Test of Independence: To study the likelihood of two categorical variables being dependent on each other, TMA Foresight allows you to run Fisher's exact test or Chi-square test. This enables you to accept or reject the null hypothesis for the association between any two biomarkers. 11) Project Management: TMA Foresight organizes your data so that you can easily access it. The reports and plots generated are linked to the data from which they are derived. Note 1: Survival analysis is a statistical technique used for estimating the survival/disease recurrence of the patients under study. The term survival analysis is typically used in biomedical sciences where the time to death of patients or animals is observed. Multivariate analytical tools such as Cox proportional hazard model are used to study the impact of biomarkers on the clinical outcome. Such techniques help in identifying important prognostic markers. 'Kaplan Meier survival analysis' is another such tool. It is used for comparative analysis of survival rates across cohorts. For a cohort, patients can be grouped according to a particular prognostically significant marker or a clinico-pathological parameter. Survival analysis helps in identifying biomarkers that are prognostically significant. Survival analysis also helps in categorizing patients into high and low risk groups. Based on this demarcation, homogeneous groups of patients are identified. The groups can be administered a common drug or treatment to determine their efficacy. System Requirements
CPU Required: Pentium-III; Recommended: Pentium-IV RAM Required: 256 MB; Recommended: 512 MB Hard Disk Drive Space Required: 40 MB; Recommended: 50 MB Screen Resolution Required: 800 x 600; Recommended: 1024 X 768 Manufacturer Home office
3786 Corina Way Palo Alto, CA 94303-4504 TEL: 650-856-2703 FAX: 650-618-1773 sales@premierbiosoft.com support@premierbiosoft.com
microarray/index.html Price Contact manufacturer G6G Product Number 20100 G6G Manufacturer Number 102176 |
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