TMA Foresight 是一款功能强大组织芯片数据分析的工具，进行多元统计技术如用Cox危险的预兆特点、等级分布和最后存活点的比例模型来探讨关联的表达和预测的临床病理变数与结果。由于TMA Foresight 不仅能够分析数据而且能够解释数据，所以它对病理、临床和研究都是非常有用的工具。

Tissue Microarray Software for Data Analysis

"TMA Foresight is an excellent program. The analysis which took me years to do manually, could now be completed in just one minute." - N. Makretsov MD PhD, Clinical Research Fellow, Department of Oncology, University of Cambridge, UK.
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.

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.