Kaplan Meier Survival Analysis
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.
Principal Component Analysis
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.
Kaplan-Meier Survival Plot
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.
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 association between any two biomarkers.
calculates the mean, standard deviation and displays the
range of different parameters. The information helps you
quickly identify any abnormalities in the data.
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
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.
Replacing Missing Values
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
This tool allows you to filter the data set based on certain set of conditions that help you to accomplish specific research goals.