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fDesc[0] = "TMA Foresight is a tissue microarray data analysis software designed to explore the relatedness of biomarker expression and clinico-pathological variates with the outcome. It identifies important biomarkers that influence the outcome and identifies prognostically significant clusters of patients using statistical techniques such as Cox Regression, Hierarchical Clustering and Survival Analysis using Kaplan-Meier Survival Plots. Based on the data provided it helps decide the risk group of a cohort. In a typical tissue microarray study, every core is associated with data elements such as the core image and patient demographics. Such a tissue array experiment calls for an extensive tissue microarray data management and analysis tool to draw valid inferences from the data generated. TMA Foresight is a data analysis tool that uses well established statistical techniques to interpret the results of a TMA experiment. TMA Foresight enables easy data pre-processing. The data can be categorized, replaced or ignored from a single screen. Missing data is easily filled up 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. Tissue microarray software can be used to group patients or biomarkers 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 in identifying high and low risk groups patients within a cohort. 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 one or more variables. This functionality may help in understanding the genomic and proteomic level alterations in patients. 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. 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. This tool is used to visualize the Kaplan Meier survival and recurrence rate for a cohort. You can parstition 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. To study the likelihood of two categorical variables being dependent on each other, TMA Foresight allows you to run Fisher&#039;s exact test or Chi-square test. This enables you to accept or reject the null hypothesis for association between any two biomarkers.<br /> 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.";

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