GOsummaries =========== An R package that visualizes the GO enrichment results as word clouds and arranges them together with figures of experimental data. This allows us to draw informative summary plots for analyses such as differential expression or clustering, where for each gene list we display its behaviour in the experiment alongside with its GO annotations. The approach is especially interesting for Principal Component Analysis (PCA), where we can annotate the principal axes functionally based on the weights of the genes. ## Citation Raivo Kolde, Jaak Vilo. 2015. “GOsummaries: an R Package for Visual Functional Annotation of Experimental Data.” F1000Research 4. ## Installation The package is available ar Bioconductor, so the installation can be done as usual. ```s if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("GOsummaries") ``` More comprehensive user guide can be found in the [vignette](https://www.bioconductor.org/packages/release/bioc/vignettes/GOsummaries/inst/doc/GOsummaries-basics.pdf). ## Examples ### PCA ```s # Perform PCA on samples pcr = prcomp(t(tissue_example$exp)) # Create gosummaries object gs_pca = gosummaries(pcr, annotation = tissue_example$annot) # Plot plot(gs_pca, classes = "Tissue") ``` ![pca](http://raivokolde.github.com/GOsummaries/images/pca.png) ### K-means clustering ```s library(GOsummaries) data(tissue_example) # Filter genes and perform k-means sd = apply(tissue_example$exp, 1, sd) exp2 = tissue_example$exp[sd > 0.75,] exp2 = exp2 - apply(exp2, 1, mean) kmr = kmeans(exp2, centers = 6, iter.max = 100) # Create gosummaries object gs_kmeans = gosummaries(kmr, exp = exp2, annotation = tissue_example$annot) plot(gs_kmeans, components = 1:2) ``` ![kmeans](http://raivokolde.github.com/GOsummaries/images/kmeans.png)