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This is the simplified version of the General Linear Model, for users with two-way factorial designs. For example, if a user has an experiment with factors for time and treatment, this model can be quickly used to generate results (including fold changes, estimates, raw and adjusted p-values, LSMeans, and Estimate data). This should not be used for experiments that contain more than two factors. More complicated designs should use the General Linear Model command.

By selecting Factor 1 and Factor 2, and then the level to Compare to, Array Studio will automatically create the comparisons and model for the user. This model generates an Inference Report Table (including automatically generated Report View and VolcanoPlotView, as well as optional LSMeans and Estimate datasets. This command opens the Two-way Analysis of Variance window).

To run this module, type MicroArray | Inference | Standard Test | Two-Way ANOVA.

TwoWay menu.png

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Input Data Requirements

This module works on -Omic data types that follow a normal distribution. Most imported microarray data will follow a normal distribution, because the signal data are generally log2-transformed during import.

Users can use these methods to see whether their data is normally distributed. Array Studio can plot kernel density of samples, and summarize skewness and kurtosis to check how well their data approximate a normal distribution.

For data that do not follow a normal distribution, the user can consider transformation or normalization.

For NGS count data that follow a negative binomial distribution, the user can use interference methods such as DeSeq2.

The factor(s) used in your statistical design should only use characters in A-Z a-z 0-9 - _ . and space.

Other characters, including ~ + - * / : ^ | [ ] { } ( ) # may be interpreted improperly in the statistical design.


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General Options


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  • 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.

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  • Factor 1: The first factor to be used in the analysis, and the one that will be automatically inserted into the For each field.
  • Factor 2: The factor that will be used for the Compare to portion of the analysis (described below).
  • Include interaction term: By default, the user usually wants the interaction between the two factors, and the "Include interaction term" checkbox is selected, however this is optional.
  • For each: If Include interaction term is checked, the Factor 1 will be shown here. If Include interaction term is unchecked, it will be none.
  • Compare to: This option allows the user to specify the level of the Group for making each comparison. For instance, in an experiment with 4 time points and two treatments (Control and Treatmen1), if the user chose time as Factor 1, and Compare to as Control, then 4 comparisons would be generated (Timepoint 1 treatment vs. control, Timepoint 2 treatment vs. control, Timepoint 3 treatment vs. control. etc.).
  • Comparison: This option gives the user various choices for the type of comparison to be made. "Control", "Dunnett", and "Tukey", are the different types of tests available for a two-way ANOVA comparison.
  • 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", "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. For more details about LSMeans, the user can check here
  • 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.
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Output Results

An example Report (TableView) and VolcanoPlotView generated using this command are shown below:



The users can also visualize the cutoff line by specify Cutoff Lines under the Task tab.


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