Alternative splicing results in a single gene coding for multiple protein isoforms, so this task can only be invoked from transcript level data. The algorithm is is based on ANOVA to detect genes with multiple transcripts showing expression changes differently in different biology groups, e.g. a gene has two transcripts: A and B, transcript A is showing up-regulation in treated group comparing to control group, and B is showing down regulation in treated group.
The alt-splicing dialog is very similar to ANOVA dialog, the analysis is based on the ANOVA model specified. To setup ANOVA model, select factors from sample attributes. The factors can be categorical or numeric attribute(s). Click on a check button to select and click Add factors button to add it to the model (Figure 1).
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Only and only one alt-splicing factor needs to be selected from the ANOVA factors. The ANOVA model performed is based on the factors specified in the dialog, the transcript ID and transcript ID interaction with alt-splicing factor effects are added automatically into the model:
Transcript ID effect: not all transcripts in a gene express at the same level, so transcript ID is added to the model to account for transcript to transcript differences
Interaction of transcript ID with alt-splicing factor: this effect is to estimate different transcripts have different expression among the levels of the factor
Suppose there is an experiment designed to detect transcripts showing differential expression in two tissue groups: liver vs muscle. The alt-splicing ANOVA dialog allows you to specify the ANOVA model that is tissue, the alt-splicing factor is chosen from the ANOVA factor(s), so the alt-splicing factor is also tissue
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