gosummaries.kmeans {GOsummaries} | R Documentation |
The gosummaries object is created based on the genes in the clusters, it is possible to add corresponding gene expression data as well.
## S3 method for class 'kmeans' gosummaries(x, exp = NULL, annotation = NULL, components = 1:length(x$size), organism = "hsapiens", ...)
x |
an object of class |
exp |
an expression matrix, with row names corresponding to the names of the genes in clusters (Optional) |
annotation |
a |
components |
numeric vector of clusters to annotate |
organism |
the organism that the gene lists correspond to. The format should be as follows: "hsapiens", "mmusculus", "scerevisiae", etc. |
... |
GO annotation filtering parameters as defined in
|
The k-means clustering of expression matrix naturally defines a set of gene
lists that can be annotated functionally and displayed as a GOsummaries
figure. This functon takes in a kmeans
object and and converts it to
a gosummaries
object that can be plotted. If expression matrix is
attached then the panel shows the expression values for each gene as
boxplots, if not then number of genes is displayed
It is advisable to filter some genes out before doing the clustering since the very large gene lists (more than 2000 genes) might fail the annotation step and are usually not too specific either.
A gosummaries object.
Raivo Kolde <raivo.kolde@eesti.ee>
## Not run: data(tissue_example) # Filter genes and perform k-means sd = apply(tissue_example$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, panel_height = 0, components = 1:3, fontsize = 8) plot(gs_kmeans, classes = "Tissue", components = 1:3, fontsize = 8) ## End(Not run)