Unequal group variances in microarray data analyses

Unequal group variances in microarray data analyses

MOTIVATION In searching for differentially expressed (DE) genes in microarray data, we often observe a fraction of the genes to have unequal variability between groups. This is not an issue in large samples, where a valid test exists that uses individual variances separately. The problem arises in the small-sample setting, where the approximately valid Welch test lacks sensitivity, while the mo...

Two-way Anova with Unequal Cell Frequencies and Unequal Variances

In this article we consider the Two-Way ANOVA model with unequal cell frequencies without the assumption of equal error variances. By taking the generalized approach to finding p-values, classical F-tests for no interaction effects and equal main effects are extended under heteroscedasticity. The generalized Ftests developed in this article can be utilized in significance testing or in fixed le...

cDNA Microarray Data Normalization

A Multiple Comparison Procedure for Populations with Unequal Variances

This article investigates an exact method that extends Dunnett’s method on the multiple comparisons with a control (MCC) to the case of unequal error variances when the ratios of population variances of k new treatments to that of the control group are known from previous experience. The advantages of the proposed method is illustrated through an example. The simulation results show that the ty...

A Parametric Bootstrap Approach for One-Way ANOVA Under Unequal Variances with Unbalanced Data

This research is to provide a solution of one-way ANOVA without using transformation when variances are heteroscedastic and group sizes are unequal. Parametric boothstrap test (Krishnamoorthy, Lu, & Mathew, 2007) has been shown to be competitive with many other methods when testing the equality of group means. We extend the parametric bootstrap algorithm to a multiple comparison procedure. Simu...