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To compare gene expression for malignant cells between astrocytoma and oligodendroglioma subtypes, malignant cells from each sample can be pooled to simulate bulk RNA-Seq data. 

Pool cells

• Click the Filtered counts data node 
• Expand the Pre-analysis tools section of the task menu
• Click Pool cells (Figure 1)

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• Choose Cell type (sample level) from the Pool cells by drop-down list 
• Keep the default pool method (Mean)
• Click Finish 

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The Glioma data node is equivalent to a bulk RNA-Seq gene counts data node and the same analysis steps can be performed on it including PCA and differential expression analysis.

Explore data with PCA

We can use principal components analysis (PCA) to visualize similarities and differences between samples for a cell type. 

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Identify differentially expressed genes

Next, we will perform differential expression analysis. 

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Next, we can set up a comparison between astrocytoma and oligodendroglioma subtypes. 

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Adding Astrocytoma vs. Oligodendroglioma will give fold-change and p-value for the comparison. Fold-change will be calculated as astrocytoma (numerator) over oligodendroglioma (denominator).

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• Double click the GSA data node

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Genes are listed by ascending P-value, so the most significant genes are at the top of the list. Results for all genes can be visualized using a volcano plot.

• Select Image RemovedImage Added in the top right corner of the table to open the volcano plot for a comparison

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• Click the GSA report tab in your browser to return to the feature list

A dot plot is available for each gene.

• Select Image RemovedImage Added next to SYT4 to open its feature plot (Figure 10)

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The dot plot opens in a new data viewer session. The plot can be configured using various options in the Configuration card on the left. For example, the Summary card can be used to add violins or box & whisker plots. 

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• Click FDR step up 
• Set it to 0.05 with the drop-down menu set to Less than or equal to 
• Press Enter
• Click Fold change
• Set it to -2 to 2 for the excluded range
• Press Enter
• Click Image RemovedImage Added to apply the filter (Figure 11)

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You will be returned to the Analyses tab and a new Differential analysis filter task will be added. This will produce a new Filtered feature list data node.

Analyze differentially expressed genes

The Filtered Feature list data node is a good starting point for drawing a hierarchical clustering heatmap and analyzing gene set or pathway enrichment.

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The hierarchical clustering heatmap displays samples on rows and genes on columns (Figure 13). The colors are scaled (normalized) expression values represented by the Z-score (standardized) for a heatmap by default.

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The plot is interactive and configurable.

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The heatmap can be saved as a publication-quality image by clicking the save image icon Image Removed Image Added or sent to a page in the Notebook tab by clicking Image RemovedImage Added. For more information about customizing and interacting with the heatmap, please see the Hierarchical Clustering section of the user's manual. 

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The GO enrichment task report lists every analyzed gene set, ranked by Enrichment score, with P-value, number of genes in list, and number of genes not in list given for each (Figure 16). Using the default GO database, each gene set name is a link to the geneontology.org web-page for that GO term. Hovering over Image RemovedImage Added shows the numbers of genes in the set in the list, in the set not in the list, in the list not in the set, and not in the list not in the set. You can view the genes in and not in the gene set by selecting Image RemovedImage Added. For more information about enrichment analysis and using custom gene sets, please see the Gene Set Enrichment section of the user's manual. 

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Gene set enrichment analysis, can also be performed using the the KEGG pathway database as the source for its gene sets. Note, to perform these next steps, you need to have the Pathway toolkit enabled. 

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Clicking one of the blue pathway names opens a KEGG pathway map that can be colored by fold-change or p-value.

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Here, we can see that several components of the spliceosome pathway are upregulated in astrocytoma vs. oligodendroglioma.

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