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The Kaplan-Meier task is used for comparing the survival curves among two or more groups of samples. The groups are defined by one or more categorical attributes (factors) specified by the user. Like in the case of Cox Regression, it is possible to use feature expression data, if available. In that case, quantitative feature expression is converted into a feature-specific categorical attribute. Each combination of the attribute levels corresponds to a distinct group. If one selects three factors with 2, 3 and 5 levels, respectively, then the total count of compared groups is 2*3*5 = 30. Therefore, selecting too many factors and/or factors with many levels may not work since the total number of samples may be not enough to fill all of the groups.

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For each feature, the expression values are sorted in ascending order and placed into B bins of (roughly) equal size. As a result, a feature-specific categorical attribute with B levels is constructed which can be used by itself or in combination with other categorical attributes. For instance, for B = 2 which is shown in the example below, we compute the median feature expression and the samples are separated into two groups, depending on whether the expression in the sample is below or above the median (Figure 1). The levels of thus the created categorical attribute are automatically denoted by P_1, P_2, …, P_B. Here P stands for “percentile” and the higher the bin number the higher the feature expression of the samples in the bin.

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