#Clc genomics workbench versus deseq2 how to#
In the original paper using this dataset, there is a heatmap of 31 genes in Figure 6b (see the tutorial here if you would like to see how to generate the heatmap). This enables us to visualize where these genes are in terms of significance and in comparison to the other genes. We can also label one or more genes of interest in a volcano plot. Create volcano plot labelling genes of interest As this dataset compares lactating and pregnant mice, it makes sense that it is a gene that is very differentially expressed. This gene is a calcium-sensitive casein that is important in milk production. Here we will visualize the results of the luminal pregnant vs lactating comparison.Ĭsn1s2b, as it is the gene nearest the top of the plot and it is also far to the left. This study examined the expression profiles of basal and luminal cells in the mammary gland of virgin, pregnant and lactating mice. The data for this tutorial comes from Fu et al. The file used here was generated from limma-voom but you could use a file from any RNA-seq differential expression tool, such as edgeR or DESeq2, as long as it has the required columns (see below). To generate this file yourself, see the RNA-seq counts to genes tutorial. To generate a volcano plot of RNA-seq results, we need a file of differentially expressed results which is provided for you here. In a volcano plot, the most upregulated genes are towards the right, the most downregulated genes are towards the left, and the most statistically significant genes are towards the top. These may be the most biologically significant genes. It enables quick visual identification of genes with large fold changes that are also statistically significant. A volcano plot is a type of scatterplot that shows statistical significance (P value) versus magnitude of change (fold change). Volcano plots are commonly used to display the results of RNA-seq or other omics experiments.
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