One Group Test
The One Group Test, also called the "one sample test", allows the user to run a variety of one-group inference tests (including "Z-Test", "T-Test", "Wilcoxon Test", "Shapiro-Wilk", and "Anderson-Darling"), to test whether the samples are from the same population with a hypothesized mean. If the samples can be easily grouped into two groups for certain genes, or a population with different mean, the users should expect to see a low p-value. A more detailed explanation can be found here
To run this module, click MicroArray | Inference | Other Tests | One Group Test.
Input Data Requirements
This module works on -Omic data types. For data that follow a normal distribution, users can use parametric methods such as T-Test; For data that do not approximate a normal distribution, users should use non-parametric methods such as the Wilcoxon Test, or transform their data to approximate a normal distribution.
- 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.
- Group: By default it is set to all. Users can choose to define a factor in the design table to see whether the samples in each level of the group are from the same population with hypothesized mean.
- Test type: Select the Test Type most appropriate to the data (available types are "ZTest", "TTest", "WilcoxonTest", "ShapiroWilk", and "AndersonDarling").
- Hypothesized mean: Specify the hypothesized mean (if applicable to the test type).
- Population SD: Specify a Population SD (if applicable to the test type).
- Hypothesis: Specify a Hypothesis - Choose from either "Two-sided" (the sample mean is different from hypothesized mean), "Greater than" (the sample mean is great than hypothesized mean) or "Less than"(the sample mean is less than hypothesized mean).
- Multiplicity: Select a Multiplicity test to adjust for multiple-testing of genes/probesets(None, FDR_BH, FDR_BY, Bonferroni, Sidak, StepDownBonferroni, StepDownSidak, and StepUp--with BDR_BH being the default option)
- Generate significant list: Checking this box generates a List of significant rows, based on the test.
- Alpha level: The p value cutoff to be used for the generation of the List.
Clicking Submit will generate a Table in the Inference tab of the Solution Explorer for the requested test. For each comparison (if specified by Group), Array Studio will generate four statistics for each gene: Estimate, Fold Change, raw p value and adjusted p value. Then the gene annotation is attached in the inference table.