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TMA Foresight

Upgrades

TMA Foresight 3.01 Released

The upgrade includes fixes to reported problems.

TMA Foresight 3.00 Released

TMA Foresight now supports the following:

Removing Multicollinearity from Data- Multicollinearity in a dataset is observed due to a strong correlation between independent variables. It obscures the statistical significance of such variables, leading to incorrect conclusions about the relationships between independent and dependent variables. This could be particularly problematic when you would like to study the contribution of an individual variable.

With this version, you can eliminate the effect of multicollinearity from your data at the click of a button.

Partial Correlation for Multiple Controlling Variables- Partial correlation is used to study the relationship between two variables while controlling the effects of the third. This upgrade enables you to study the correlation between two variable by controlling the effect of more than one variable at a time.

Also includes general improvements and fixes to the reported problems.

TMA Foresight 2.50 Released

TMA Foresight is now equipped to analyze biomarker expression data in addition to performing survival analysis. You can now perform analysis such as clustering and correlation without providing survival data. You should be able to apply all the statistical techniques included directly to immunohistochemical data to identify diagnostic biomarkers, cluster them and draw inferences from other known markers. When used in conjunction with expression profiling, TMA Foresight is now a more powerful tool for evaluating and interpreting TMA data.

TMA Foresight 2.02 Released

The upgrade includes fixes to reported problems.

TMA Foresight 2.00 Released

  • Data Filtering- With TMA Foresight 2.0, filter the tissue microarray data using logical operators.

  • Correlation Analysis- Explore linear, monotonic, curvilinear, non-linear relationships between two covariates. Various correlation coefficients, viz., Phi, Cramer's V for nominal, Spearman's rank correlation, Kendall's tau-b and tau-c for ordinal and Pearson's coefficient for ratio scale, can be used for the analysis. For analyzing correlation between any two variables by negating the influence of other variables, run a partial analysis.

  • Principle Component Analysis- TMA Foresight helps you quickly generate clusters from 2D scatter plots generated from a principal component analysis using its point-and-click functionality. The Kaplan Meier plot and results of the Log Rank test are updated accordingly.

  • Test of Independence- TMA Foresight helps you study the likelihood of any two categorical variables being associated using Fisher's Exact and Chi-square tests.
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