TMA Foresight is a tool 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. Based on the data provided, it helps decide the risk group of a cohort. TMA Foresight uses well established statistical techniques to interpret the results of a TMA
experiment, making it a useful tool for pathologists, clinicians and researchers.
Easy Data Preprocessing
TMA Foresight allows the data to be categorized, replaced or ignored from a single screen. It helps map character data to numeric values with a click of a button. Missing data can be easily filled up depending on
the measurement level chosen, ensuring completeness of data for further analysis. You can then apply multivariate statistical techniques to identify prognostically significant markers and clinico-pathological parameters that have a significant impact on the outcome.
Statistical Tests
TMA Foresight has the ability to rapidly perform various statistical tests such as Cox proportional hazard model to identify prognostic markers, Kaplan-Meier survival plots to visualize the survival/recurrence rates for a cohort and hierarchical clustering and Principal Component Analysis to group patients into relatively homogeneous sub-groups based on a set of variables. TMA Foresight has excellent point-and- click wizards making it easy to use for beginners, while the versatile functionality offers full control for experts. For hierarchical clustering, TMA Foresight enables you to move the linkage bar over the dendrogram which updates the Kaplan Meier plot and the log rank test results accordingly. Similarly, for Principal Component Analysis, TMA Foresight enables you to move an axis across the scatter plot generated between two variables,
to cluster patients. This functionality helps in determining prognostically significant clusters and identifying high and low risk groups patients within a cohort. To explore linear, monotonic, curvilinear, non-linear relationships between two covariates use bivariate correlation analysis. To measure the correlation between any two variables by negating the influence of other variables use the partial analysis. To study the likelihood of any two categorical variables being associated use Fisher's Exact and Chi-square tests. TMA Foresight also counts the frequency, calculates the mean, standard deviation and displays the range of different parameters. The information helps in quick identification of any abnormalities within the data.
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.