TwoWayNestedAnova.pdf
Two Way Nested ANOVA
Overview
The Two Way Nested ANOVA is the simplified version of the General Linear Model for users with two-way factorial designs, where the second factor is nested in the first factor. For example, if a user has a study that just contains toxicity as a factor, as well as multiple compounds (that are toxic or non-toxic), the compound factor is nested in the toxicity factor.
This model can be quickly used to generate results (including fold changes, estimates, raw and adjusted p-values, LSMeans, and Estimate data). More complicated designs should use the General Linear Model command. By selecting a Group, and then specifying the level to Compare to, Array Studio will automatically create the comparisons and model for the user. This model generates an Inference Report (including automatically generated Report View and VolcanoPlot View), as well as optional LSMeans and Estimate datasets.
To run this module, click MicroArray | Inference | Standard Test | Two-Way Nest ANOVA.
Input Data Requirements
This function works on -Omic data types. Since this is ANOVA, the underlying assumption is that the data follows a normal distribution.
General Options
Input/Output
- Project & Data: The window includes a dropdown box to select the Project and Data object to be filtered.
- Variables: Selections can be made on which variables should be included in the filtering (options include All variables, Selected variables, Visible variables, and Customized variables (select any pre-generated Lists)).
- Observations: Selections can be made on which observations should be included in the filtering (options include All observations, Selected observations, Visible observations, and Customized observations (select any pre-generated Lists).
- Output name: The user can choose to name the output data object.
Options
- Group: This is the first factor, in which the second factor is nested.
- SubGroup: This is the factor that is nested in the Group factor.
- Comparison: This option gives the user two choices for the type of comparison to be made. "Control" or "Pairwise". If "Control" is chosen, the "Compare to" dropdown box will be activated to choose a baseline to compare. If "Pairwise" is chosen, all pairwise comparisons for that factor will be made.
- Compare to: This option allows the user to specify the level of the Group for making each comparison. For instance, in an experiment with four time points and two treatments (Control and Treatment), if the user chooses time as Factor 1, and Compare to Control, then 4 comparisons will be generated (Timepoint 1 treatment vs. control, Timepoint 2 treatment vs. control, Timepoint 3 treatment vs. control. etc.).
- By: This function allows the user to select a design covariate to use in separating out the analysis based on the covariate groups.
- Multiplicity: This function specifies the multiple comparisons adjustment used for the analysis. The options include: "FDR_BH", "FDR_BY", "Bonferroni", "Sidak", "StepDownBonferroni", "StepDownSidak", "StepUp" and QValue (FDR_BH is the default option).
- Note: The Multiplicity adjustment takes into account the total number of tests performed within a given analysis. There is the ability to set the default option to adjust p-values on a per-test basis. Please refer to the Statistics section in the User Guide.
- FC transformation: This option will let the user set the fold change transformation to any of the following: "Exp2" (default), "Exp", "Exp10" and Ratio.
- Alpha level: This option allows the user to set a p-value cutoff for Lists which are automatically generated for each comparison in the ANOVA.
- Report F-Test Pvalues: Checking this box will report the FTest pvalue for the One-way ANOVA.
- Generate LSMeans data: Checking this box will generate an LSMeans dataset, using the Group as the factor for the LSMeans.
- Append LSMeans data: Checking this box will appended LSMeans data to the inference report (to allow the user to quickly see the intensity levels for each group).
- Generate estimate data: Checking this box will generate an Estimate dataset (a dataset containing all the comparisons and the estimate levels).
- Split the significant list by change direction: Checking this box will split each generated significant list (based on the alpha level value) by direction of change.
Output Results
An example Report (TableView) and VolcanoPlot View generated using this command, shown below:
OmicScript
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