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Using Named Colors with ggplot2

Consider these two plots:

The colors assigned to the phyla are not the same in the two plots. We cannot make them so simply by faceting because there are unique phyla at each site and the sites were sampled at different depths.  In fact, they were plotted using different tibbles. Still, it would be much easier to interpret the plots if the same colors were assigned to the phyla that are common between the plots. We can do this by using a palette of named colors.

First, get a vector of the union of unique phyla for each plot and determine its length. The plots were made from two tibbles, df1 and df2.

unique.phyla <- union(unique(df1$Phylum), unique(df2$Phylum))
length(unique.phyla)
[1] 9

So while there are only 7 phyla in each plot, there are a total of 9 in the two plots. Next, get a vector of 9 colors and name the colors with the names of the phyla.

my.colors <- RColorBrewer::brewer.pal(n = length(unique.phyla),name = "Paired")
names(my.colors) <- unique.phyla
my.colors
Cyanobacteria  Campilobacterota  Nitrospirae Proteobacteria
"#A6CEE3"      "#1F78B4"         "#B2DF8A"       "#33A02C"
Abditibacteriota Saccharibacteria Verrucomicrobia Acidobacteria
"#FB9A99"        "#E31A1C"        "#FDBF6F"       "#FF7F00"
Bacteroidetes
"#CAB2D6"

Replot, using the named palette for the fill colors. The plots above were made using the same code as below except that the scale_fill_manual lines were omitted.

plt1 <- ggplot(df1, aes(x=Depth, y=value, fill = Phylum)) +
    geom_col() +
    ylab("Relative Abundance") +
   scale_fill_manual(values = my.colors, aesthetics = "fill") +
    xlab("Depth (m)") +
    ggtitle("Site 1")
plt2 <- ggplot(df2, aes(x=Depth, y=value, fill = Phylum)) +
    geom_col() +
    scale_fill_manual(values = my.colors, aesthetics = "fill") +
    ylab("Relative Abundance") + xlab("Depth (m)") +
    gtitle("Site 2")

cowplot::plot_grid(plt1, plt2)

Voila! Now the color assignments are consistent between phyla. This approach can be generalized to solve the same problem with other types of plots.

 

 

RDP Classifier Updated

The RDP Classifier was updated to version 2.13 and released 30 July 2020 on SourceForge (https://sourceforge.net/projects/rdp-classifier/). The update is based on  bacterial and archaeal training set No.18 with over 800 new genera and significant rearrangements of several phyla and genera based on the latest genome analyses.  For detailed explanations of these revisions, please see the release notes.

As in earlier editions, databases are also included for classification of  fungal ITS sequences by UNITE and Warcup taxonomies and for  classification of fungal 28S rRNA gene sequences.

Web Version

The web version of the classifier has been updated to use training set No. 18. The taxonomy browser has also been updated to comply with the new training set.

Installing as a stand-alone application

Written in Java, the RDP Classifier may be installed and run in Windows, Mac OS, Linux, Unix and Solaris environments.

  1. Test if the Java Runtime Environment (JRE) is already installed by opening a terminal and entering:
 java -version
  1. If necessary, install JRE  downloaded from here. While Oracle now charges for the development version of Java (JDK), JRE is still free.
  2. Download RDP Classifier version 2.13 from here and extract the compressed file.

The classifier.jar file will be in sub-directory dist.

Installing as part of RDPTools

The only way to update the classifier in RDPTools is to remove and re-install RDPTools following the instructions here. Updating RDPTools will not work because the new databases are not part of the code but are downloaded during RDPTools installation.

Updating the QIIME2 version of the RDP Classifier

Reference sequences and corresponding taxonomy file for re-training the RDP Classifier included in QIIME2 can be downloaded by clicking here. See this page for how to re-train the classifier.

Import DADA2 ASV Tables into phyloseq

ASV Tables Created in R

ASV tables created using the Bioconductor/R version of DADA2 are matrix files with samples as rows and taxa as columns. The taxa names are the sequences themselves. Because these matrices can be quite large they are most conveniently saved as compressed rds files. Read these files into R and create an experiment level phyloseq object containing an OTU or ASV table and representative sequences with the following R script: Continue reading “Import DADA2 ASV Tables into phyloseq”

Get ggplot plot panel limits

I have added a new function (get_plot_limits) to my package QsRutils.  It extracts the minimum and maximum X and Y values for a ggplot panel. This is useful in formatting ggplots. For example, you may wish to expand the panel to avoid text running out of the panel, or nudge text relative to some point. For an example, see my post on adding compact letter displays to box plots created with ggplot2.

Rooting Unifrac Trees in phyloseq Objects

The method of rooting trees described in the post “Unifrac and Tree Roots” is now included in QsRutils beginning with version 0.3.2 as function root_phyloseq_tree. Given a phyloseq object with an unrooted tree, it returns the same type of phyloseq object with the tree rooted by the longest terminal branch.

Unifrac and Tree Roots

Unifrac distances have the attraction of including phylogenetic relatedness, based on a tree of the representative sequences, in the distances among samples calculated from an OTU table. FastTree is the usual method of choice in generating the tree, although USEARCH also provides a method. Both methods calculate unrooted trees, and calculation of Unifrac distances requires a rooted tree. The problem arises in how to best root the tree. I found a discussion of the problem on the phyloseq GitHub site (https://github.com/joey711/phyloseq/issues/597). Continue reading “Unifrac and Tree Roots”

Update R in Ubuntu

If you installed R from the Ubuntu repository with

sudo apt-get install r-base

you most likely got an out of date version. In February 2018, that method still gave me R version 3.2.3 (2015-12-10). To get the latest versions of R and its packages, you need to add CRAN to the apt-get repositories. Do this with the code below. Enter one line at a time. Cut and paste to prevent errors. Continue reading “Update R in Ubuntu”