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DESeq General Linear Model
This is the command in Array Studio for running differential expression analysis on RNA-Seq count data. It allows the user to model the data on a liner model basis and test for differential expression by use of the negative binomial distribution. The function should perform similarly as DESeq R package
- The window includes a dropdown box to select the Project and Data object on which the command will be run.
- Selections can be made on which variables should be included in the General Linear Model (options include "all", "selected", "visible", and any pre-generated Lists).
- Selections can also be made on which observations should be included in the General Linear Model. (options include "all", "selected", "visible", and any pre-generated Lists).
If user is not familiar with General Linear Model (GLM), please also read general linear model function documentation. The Options section for the Linear Model window include 3 steps:
- Step 1, which is required, involves specifying the model. This is where the user will specify the terms of the model, main effects and cross/interaction terms:
the "Columns" section contains columns from the Data object's Design Table. If the column should be considered a Class term, a checkbox for that column can be selected. By default, Array Studio will guess on what constitutes a Class term. In general, numeric columns will not be considered Class terms by default, while other column such as "Factors", will be considered Class terms by default. Consult with a statistician if not sure as to whether a column should be a class term.
The "Construct Model" section is where the user can add the terms to the model. By selecting terms on the left, the user can use the Add, Cross, and Remove buttons to select the terms for that particular model. Selecting "Add" will add one or multiple terms to the model, whereas "Cross" will cross the terms selected on the left.
Clicking "OK" returns the user to the General Linear Model window, where Step 1 is now complete.
- Step 2, which is also required, involves specifying the contrasts involved. This includes any particular comparisons the user is interested in, along with the tests:
The user has the option of manually building contrasts for each comparison or using the "For each" option to let Array Studio generate multiple estimates at once. In the Options section, the user can decide whether Estimates, Fold changes, Raw p-values, Adjusted p-values, Generate significant list, and Split significant list (by direction) will be created for the Inference report generated by this command.
- Step 3, change or keep the option for FDR and cutoff.
- Fit Type: Select a fit type, from parametric, local, or mean
- Alpha level:Choose a p-value cutoff for significance calls
- Minimal replicates for replacing
- Perform independent filtering: Filter genes with a low overall count
- Export dispersion table: In addition to an inference report, generate a Dispersion table
- Export Wald table: In addition to an inference report, generate a Wald table
- Export outliers: Export an outlier table
- Export group means: Export a table with mean values for each group
- Export contrast vector table: Export a table with contrast vectors
- Use alphabetical order factor level: Enable sorting of factors by alphabetical order