# Detecting differential expression in RNA-Seq data During import, you created a categorical attribute called *Tissue* and assigned the 4 samples to either the *muscle* or *not muscle* groups. This step was to create replicates within a group, albeit this grouping is somewhat artificial and is only used in this tutorial because we want to illustrate ANOVA with a small data set. Replicates are a prerequisite for differential expression analysis using ANOVA. * Select **Differential Expression Analysis** from the *Analyze Known Genes* section of the *RNA-Seq* workflow The *Differential Expression Analysis* dialog offers the choice of analyzing at Gene-,Transcript-, or Exon-level. * Select **Gene-level** * Specify the **1/gene\_rpkm (RNA-Seq\_results.gene.rpkm)** spreadsheet from the *Spreadsheet* drop-down menu (Figure 1) ![](/files/qXeQxH4ExsB82fa3hcPQ) Figure 1. Choosing the type of differential expression analysis * Select **OK** to open the *ANOVA* dialog Available factors are listed in the *Experimental Factor(s)* panel on the left-hand side of the dialog. * Select **Tissue**, then select **Add Factor >** to move **Tissue** to the *ANOVA Factor(s)* panel on the right-hand side of the dialog (Figure 2) ![](/files/kvt14wGD5FVqApWIkMfo) Figure 2. The ANOVA dialog If the ANOVA were now performed (without contrasts), a p-value for differential expression would be calculated, but it would only indicate if there are differences within the factor *Tissue*; it would not inform you which groups are different or give any information on the magnitude of the difference between groups (fold-change or ratio). To get this more specific information, you need to define linear contrasts. * Select **Contrasts...** to open the *Configure* dialog * For *Select Factor/Interaction*, **Tissue** will be the only factor available as it was the only factor included in the ANOVA model in the previous step; if multiple factors were included, they could be selected in the *Select Factor/Interaction:* drop-down menu. The levels in this factor are listed on the *Candidate Level(s)* panel on the left side of the dialog * For this data set, verify that **No** is selected for *Data is already log transformed?* * Left click to select **muscle** from the *Candidate Level(s)* panel and move it to the *Group 1* panel (renamed *muscle*) by selecting **Add Contrast Level** **>** in the top half of the dialog. *Label 1* will be changed to the subgroup name automatically, but you can also manually specify the label name * Select **not muscle** from the *Candidate Level(s)* panel and move it to the *Group 2* panel (renamed *not muscle)* * The **Add Contrast** button can now be selected (Figure 3) * Select **OK** to return to the *ANOVA* dialog ![](/files/zFkpSx00bZJABwRu5UgO) Figure 3. Defining linear contrasts * Select **OK** to perform the ANOVA as configured (Figure 4) ![](/files/oVyS3zKKgqPDlmttqz6q) Figure 4. Fully configured ANOVA Once the ANOVA has been performed on each gene in the data set, an ANOVA child spreadsheet *ANOVA-1way (ANOVAResults)* will appear under the *gene\_rpkm* spreadsheet (Figure 5). The format of the ANOVA spreadsheet is similar for all workflows. Mouse over each column title for a description of the column contents. ![](/files/Al0CKk5tHw2YlZzhIkZZ) Figure 5. Viewing ANOVA results In this tutorial, the overall p-value for the factor (column 4) is the same as the p-value for the linear contrast (column 5) as there are only two levels within *Tissue*. If we had more than two groups, the overall p-value and the linear contrast p-values would most likely differ. You can also see the ? symbol in the ratio/fold-change columns (6 and 7) for several genes that also have a low p-value because there are zero reads in one of the groups, thus making it impossible to calculate ratios and fold-changes between groups. For using ANOVA with more complicated experimental designs, including multiple factors and linear contrasts, please refer to [Identifying differentially expressed genes using ANOVA](/partek/partek-genomics-suite/tutorials/gene-expression-analysis/identifying-differentially-expressed-genes-using-anova.md) in the Gene Expression Analysis tutorial. ## Additional Assistance If you need additional assistance, please visit [our support page](http://www.partek.com/support) to submit a help ticket or find phone numbers for regional support. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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