The goal of taxafmt is to format and parse taxonomic assignments, especially for microbiome data sets.
You can install the development version of taxafmt like so:
# install.packages("devtools")
devtools::install_github("kylebittinger/taxafmt")The taxafmt package comes with a small data set of taxonomic
assignments.
sprockett_taxa
#> # A tibble: 10 × 2
#> feature_id lineage
#> <chr> <chr>
#> 1 f2ad15e k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__En…
#> 2 560c185 k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Bifidobacte…
#> 3 2766544 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Entero…
#> 4 ac7454f k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__L…
#> 5 39ba0d9 k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Bifidobacte…
#> 6 79286fc k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__L…
#> 7 f895c95 k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__En…
#> 8 a6ac740 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Entero…
#> 9 848fce2 k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Bifidobacte…
#> 10 b8bedcf k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__L…Here, we’ll show how to parse and format the assignments for use in
plots or statistical comparisons. Our first goal is to split up the taxa
into ranks, like family, genus, and species. The split_lineage
function does this for us.
split_lineage(sprockett_taxa$lineage)
#> # A tibble: 10 × 7
#> Kingdom Phylum Class Order Family Genus Species
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 k__Bacteria p__Firmicutes c__Bacilli o__L… f__En… g__E… <NA>
#> 2 k__Bacteria p__Actinobacteria c__Actinobacteria o__B… f__Bi… g__B… <NA>
#> 3 k__Bacteria p__Proteobacteria c__Gammaproteobacte… o__E… f__En… <NA> <NA>
#> 4 k__Bacteria p__Firmicutes c__Clostridia o__C… f__La… g__[… s__gna…
#> 5 k__Bacteria p__Actinobacteria c__Actinobacteria o__B… f__Bi… g__B… <NA>
#> 6 k__Bacteria p__Firmicutes c__Clostridia o__C… f__La… g__B… s__
#> 7 k__Bacteria p__Firmicutes c__Bacilli o__L… f__En… g__E… <NA>
#> 8 k__Bacteria p__Proteobacteria c__Gammaproteobacte… o__E… f__En… <NA> <NA>
#> 9 k__Bacteria p__Actinobacteria c__Actinobacteria o__B… f__Bi… g__B… <NA>
#> 10 k__Bacteria p__Firmicutes c__Clostridia o__C… f__La… g__[… s__gna…That’s okay, but we’d prefer to have the taxa lined up beside the
Feature ID’s. We can use the tidyverse for that.
library(tidyverse)
sprockett_taxa |>
mutate(split_lineage(lineage))
#> # A tibble: 10 × 9
#> feature_id lineage Kingdom Phylum Class Order Family Genus Species
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 f2ad15e k__Bacteria; p__F… k__Bac… p__Fi… c__B… o__L… f__En… g__E… <NA>
#> 2 560c185 k__Bacteria; p__A… k__Bac… p__Ac… c__A… o__B… f__Bi… g__B… <NA>
#> 3 2766544 k__Bacteria; p__P… k__Bac… p__Pr… c__G… o__E… f__En… <NA> <NA>
#> 4 ac7454f k__Bacteria; p__F… k__Bac… p__Fi… c__C… o__C… f__La… g__[… s__gna…
#> 5 39ba0d9 k__Bacteria; p__A… k__Bac… p__Ac… c__A… o__B… f__Bi… g__B… <NA>
#> 6 79286fc k__Bacteria; p__F… k__Bac… p__Fi… c__C… o__C… f__La… g__B… s__
#> 7 f895c95 k__Bacteria; p__F… k__Bac… p__Fi… c__B… o__L… f__En… g__E… <NA>
#> 8 a6ac740 k__Bacteria; p__P… k__Bac… p__Pr… c__G… o__E… f__En… <NA> <NA>
#> 9 848fce2 k__Bacteria; p__A… k__Bac… p__Ac… c__A… o__B… f__Bi… g__B… <NA>
#> 10 b8bedcf k__Bacteria; p__F… k__Bac… p__Fi… c__C… o__C… f__La… g__[… s__gna…In some taxonomic systems like this one, the taxa are given a one-letter
prefix to indicate the rank. This is convenient, especially when we want
to double check that the correct taxon is in each column, but it’s too
ugly to use in plots and reports. The function remove_rank_prefix will
remove this prefix.
sprockett_taxa |>
mutate(split_lineage(lineage)) |>
mutate(across(Kingdom:Species, remove_rank_prefix))
#> # A tibble: 10 × 9
#> feature_id lineage Kingdom Phylum Class Order Family Genus Species
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 f2ad15e k__Bacteria; p__F… Bacter… Firmi… Baci… Lact… Enter… Ente… <NA>
#> 2 560c185 k__Bacteria; p__A… Bacter… Actin… Acti… Bifi… Bifid… Bifi… <NA>
#> 3 2766544 k__Bacteria; p__P… Bacter… Prote… Gamm… Ente… Enter… <NA> <NA>
#> 4 ac7454f k__Bacteria; p__F… Bacter… Firmi… Clos… Clos… Lachn… [Rum… gnavus
#> 5 39ba0d9 k__Bacteria; p__A… Bacter… Actin… Acti… Bifi… Bifid… Bifi… <NA>
#> 6 79286fc k__Bacteria; p__F… Bacter… Firmi… Clos… Clos… Lachn… Blau… <NA>
#> 7 f895c95 k__Bacteria; p__F… Bacter… Firmi… Baci… Lact… Enter… Ente… <NA>
#> 8 a6ac740 k__Bacteria; p__P… Bacter… Prote… Gamm… Ente… Enter… <NA> <NA>
#> 9 848fce2 k__Bacteria; p__A… Bacter… Actin… Acti… Bifi… Bifid… Bifi… <NA>
#> 10 b8bedcf k__Bacteria; p__F… Bacter… Firmi… Clos… Clos… Lachn… [Rum… gnavusThe species names are not quite right–they only contain the specific
epithet, which is the second word of the species name. The first word in
the species name is the genus. To generate real species names, we can
use the function make_binomial_name.
sprockett_taxa |>
mutate(split_lineage(lineage)) |>
mutate(across(Kingdom:Species, remove_rank_prefix)) |>
mutate(Species = make_binomial_name(Genus, Species))
#> # A tibble: 10 × 9
#> feature_id lineage Kingdom Phylum Class Order Family Genus Species
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 f2ad15e k__Bacteria; p__F… Bacter… Firmi… Baci… Lact… Enter… Ente… <NA>
#> 2 560c185 k__Bacteria; p__A… Bacter… Actin… Acti… Bifi… Bifid… Bifi… <NA>
#> 3 2766544 k__Bacteria; p__P… Bacter… Prote… Gamm… Ente… Enter… <NA> <NA>
#> 4 ac7454f k__Bacteria; p__F… Bacter… Firmi… Clos… Clos… Lachn… [Rum… [Rumin…
#> 5 39ba0d9 k__Bacteria; p__A… Bacter… Actin… Acti… Bifi… Bifid… Bifi… <NA>
#> 6 79286fc k__Bacteria; p__F… Bacter… Firmi… Clos… Clos… Lachn… Blau… <NA>
#> 7 f895c95 k__Bacteria; p__F… Bacter… Firmi… Baci… Lact… Enter… Ente… <NA>
#> 8 a6ac740 k__Bacteria; p__P… Bacter… Prote… Gamm… Ente… Enter… <NA> <NA>
#> 9 848fce2 k__Bacteria; p__A… Bacter… Actin… Acti… Bifi… Bifid… Bifi… <NA>
#> 10 b8bedcf k__Bacteria; p__F… Bacter… Firmi… Clos… Clos… Lachn… [Rum… [Rumin…Finally, we’d like to create a shortened label for each taxonomic
assignment, something that’s nice to use in a plot or report. The
function format_taxa does this. We give format_taxa a data frame of
the taxa, and it generates labels using the lowest-ranking taxon in the
data frame (which should be on the right hand side). If that taxon is
NA, then we get a label containing the lowest-rank taxon that’s
available, but tagged to indicate that it’s not classified all the way
down.
Even though we worked to tidy up the species names, we can’t really
trust species-level assignments for a data set like this. Therefore, we
use the pick function from dplyr to select the taxa from Kingdom
to Genus when making the labels.
sprockett_taxa |>
mutate(split_lineage(lineage)) |>
mutate(across(Kingdom:Species, remove_rank_prefix)) |>
mutate(Species = make_binomial_name(Genus, Species)) |>
mutate(label = format_taxa(pick(Kingdom:Genus)))
#> # A tibble: 10 × 10
#> feature_id lineage Kingdom Phylum Class Order Family Genus Species label
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 f2ad15e k__Bacteria… Bacter… Firmi… Baci… Lact… Enter… Ente… <NA> Firm…
#> 2 560c185 k__Bacteria… Bacter… Actin… Acti… Bifi… Bifid… Bifi… <NA> Acti…
#> 3 2766544 k__Bacteria… Bacter… Prote… Gamm… Ente… Enter… <NA> <NA> Prot…
#> 4 ac7454f k__Bacteria… Bacter… Firmi… Clos… Clos… Lachn… [Rum… [Rumin… Firm…
#> 5 39ba0d9 k__Bacteria… Bacter… Actin… Acti… Bifi… Bifid… Bifi… <NA> Acti…
#> 6 79286fc k__Bacteria… Bacter… Firmi… Clos… Clos… Lachn… Blau… <NA> Firm…
#> 7 f895c95 k__Bacteria… Bacter… Firmi… Baci… Lact… Enter… Ente… <NA> Firm…
#> 8 a6ac740 k__Bacteria… Bacter… Prote… Gamm… Ente… Enter… <NA> <NA> Prot…
#> 9 848fce2 k__Bacteria… Bacter… Actin… Acti… Bifi… Bifid… Bifi… <NA> Acti…
#> 10 b8bedcf k__Bacteria… Bacter… Firmi… Clos… Clos… Lachn… [Rum… [Rumin… Firm…Now, let’s check out those labels.
sprockett_taxa |>
mutate(split_lineage(lineage)) |>
mutate(across(Kingdom:Species, remove_rank_prefix)) |>
mutate(Species = make_binomial_name(Genus, Species)) |>
mutate(label = format_taxa(pick(Kingdom:Genus))) |>
select(feature_id, label)
#> # A tibble: 10 × 2
#> feature_id label
#> <chr> <chr>
#> 1 f2ad15e Firmicutes - Enterococcus
#> 2 560c185 Actinobacteria - Bifidobacterium
#> 3 2766544 Proteobacteria - unclassified Enterobacteriaceae
#> 4 ac7454f Firmicutes - [Ruminococcus]
#> 5 39ba0d9 Actinobacteria - Bifidobacterium
#> 6 79286fc Firmicutes - Blautia
#> 7 f895c95 Firmicutes - Enterococcus
#> 8 a6ac740 Proteobacteria - unclassified Enterobacteriaceae
#> 9 848fce2 Actinobacteria - Bifidobacterium
#> 10 b8bedcf Firmicutes - [Ruminococcus]