I am using dotplot() to visualize results from enrichGO(), enrichDO(), enricher() and compareCluster() in clusterProfiler R package. When specifying showCategory, I get the right number of categories except with the results of compareCluser().
In my case, I use compareCluster() on a list of 3 elements:
str(ClusterList) List of 3 $ All : chr [1:1450] "89886" "29923" "100132891" "101410536" ... $ g1 : chr [1:858] "89886" "29923" "100132891" "101410536" ... $ g2: chr [1:592] "5325" "170691" "29953" "283392" ... CompareGO_BP=compareCluster(ClusterList, fun="enrichGO", pvalueCutoff=0.01, pAdjustMethod="BH", OrgDb=org.Hs.eg.db,ont="BP",readable=T) dotplot(CompareGO_BP, showCategory=10, title="GO - Biological Process")
I ask for 10 categories, but I get 15 categories in All, 8 categories in g1 and 12 categories in g2. None of the categories, neither the sum of the categories are 10…
Is the option showCategory working in the case of comparison? Am I missing something here?
And which categories precisely will it plot? the most significant whatever my 3 cases or the most significant of each case?
The question was posted in Bioconductor support site. It seems quite confusing and I think I need to write a post to clarify it.
I try to plot long tip labels in ggtree and usually adjust them using xlim(), however when creating a facet_plot xlim affects all plots and minimizes them.
Is it possible to work around this and only affect the tree and it’s tip labels leaving the other plots in facet_plot unaffected?
This is indeed a desire feature, as
ggplot2 can’t automatically adjust
xlim for text since the units are in two different spaces (data and pixel).
gheatmap for visualizing heatmap and
msaplot for visualizing multiple sequence alignment with phylogenetic tree.
We may have different data types and want to visualize and align them with the tree. For example,
dotplot of SNP site (e.g. using
barplot of trait values (e.g. using
geom_barh(stat='identity')) et al.
To make it easy to associate different types of data with phylogenetic tree, I implemented the
facet_plot function which accepts a
geom function to draw the input
data.frame and display it in an additional
ggtree provides many helper functions for manupulating phylogenetic trees and make it easy to explore tree structure visually.
Here, as examples, I used
ggtree to draw capital character
C, which are first letter of my name :-).
To draw a tree in such shape, we need
fan layout (
circular layout with open angle) and then rotating the tree to let the open space on the correct position. Here are the source codes to produce the
C shapes of tree. I am thinking about using the
G shaped tree as
ggtree logo. Have fun with
OutbreakTools implements basic tools for the analysis of Disease Outbreaks.
obkData to store case-base outbreak data. It also provides a function,
plotggphy, to visualize such data on the phylogenetic tree.
library(OutbreakTools) data(FluH1N1pdm2009) attach(FluH1N1pdm2009) x <- new("obkData", individuals = individuals, dna = FluH1N1pdm2009$dna, dna.individualID = samples$individualID, dna.date = samples$date, trees = FluH1N1pdm2009$trees) plotggphy(x, ladderize = TRUE, branch.unit = "year", tip.color = "location", tip.size = 3, tip.alpha = 0.75)
ggtree can parse many software outputs and the evolution evidences inferred by these software can be used directly for tree annotation. ggtree not only works as an infrastructure that enables evolutionary data that inferred by commonly used software packages to be used in R, but also serves as a general tree visualization and annotation tool for the R community as it supports many S3/S4 objects defined by other R packages.
phyloseq class defined in the phyloseq package was designed for microbiome data.
phyloseq package implemented
plot_tree function using
ggplot2. Although the function was implemented by
ggplot2 and we can use
scale_color_manual etc for customization, the most valuable part of
ggplot2, adding layer, is missing.
plot_tree only provides limited parameters to control the output graph and it is hard to add layer unless user has expertise in both
MeSH (Medical Subject Headings) is the NLM (U.S. National Library of
Medicine) controlled vocabulary used to manually index articles for
MEDLINE/PubMed. MeSH is comprehensive life science vocabulary. MeSH has
19 categories and
MeSH.db contains 16 of them. That is:
Leading edge analysis reports
Tags to indicate the percentage of genes contributing to the enrichment score,
List to indicate where in the list the enrichment score is attained and
Signal for enrichment signal strength.
It would also be very interesting to get the core enriched genes that contribute to the enrichment.