metacoder
Tools for Parsing, Manipulating, and Graphing Taxonomic Abundance Data
A set of tools for parsing, manipulating, and graphing data classified by a hierarchy (e.g. a taxonomy).
An R package for metabarcoding research planning and analysis
Metabarcoding is revolutionizing microbial ecology and presenting new challenges:
- Numerous database formats make taxonomic data difficult to parse, combine, and subset.
- Stacked bar charts, commonly used to depict community diversity, lack taxonomic context and are limited by the number of discernible colors.
- Barcode loci and primers are a source of under-explored bias.
Metacoder is an R package that attempts to addresses these issues:
- Sources of taxonomic data can be extracted from most file formats and manipulated.
- Community diversity can be visualized by color and size in a tree plot.
- Primer specificity can be estimated with in silico PCR.
Installation
Stable releases are available on CRAN and can be installed in the standard way:
install.packages("metacoder")
The most recent version can be installed from Github:
devtools::install_github("ropensci/taxa")
devtools::install_github("grunwaldlab/metacoder")
library(metacoder)
Documentation
This is only a short demonstration. See the full documentation at http://grunwaldlab.github.io/metacoder_documentation.
Parsing
Many functions that used to be in metacoder have now been moved into the taxa package. These include the flexible parsers and dplyr-like data-manipulation functions. If you have an non-standard data format or want to use the more flexible taxa parsers, check out the intro to the taxa package here. Metacoder now has functions for parsing specific file formats used in metagenomics research. However, for this demonstration, we will be using a parser from the taxa package meant for tabular data.
Included in metacoder is an example dataset that is a subset of the Human Microbiome Project data. This dataset has two parts:
- An abundance matrix called
hmp_otus, with samples in columns and OTUs in rows - A sample information table called
hmp_samples, with samples as rows and columns of information describing the samples (e.g. gender).
This is the preferred way to encode this type of abundance information in metacoder and taxa. Lets take a look at this data:
library(metacoder)print(hmp_otus)#> # A tibble: 1,000 x 52#> otu_id lineage `700… `700… `700… `700… `700… `700… `700… `700… `700… `700… `700… `700…#> <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>#> 1 OTU_9… r__Roo… 0 2 1 0 0 0 0 0 0 0 2 6#> 2 OTU_9… r__Roo… 0 0 0 0 0 0 0 0 0 0 0 0#> 3 OTU_9… r__Roo… 0 1 0 0 0 0 0 0 0 0 1 1#> 4 OTU_9… r__Roo… 8 36 10 5 66 38 8 4 17 21 0 0#> 5 OTU_9… r__Roo… 3 25 0 0 0 1 0 0 0 0 10 67#> 6 OTU_9… r__Roo… 42 277 16 22 85 211 20 33 44 52 0 0#> 7 OTU_9… r__Roo… 4 17 21 1 74 12 5 15 13 115 0 0#> 8 OTU_9… r__Roo… 0 0 0 0 0 0 0 0 4 1 0 0#> 9 OTU_9… r__Roo… 0 0 0 0 0 0 0 0 0 0 0 7#> 10 OTU_9… r__Roo… 0 0 0 0 1 0 0 0 0 0 0 0#> # ... with 990 more rows, and 38 more variables: `700103488` <int>, `700096869` <int>,#> # `700107379` <int>, `700096422` <int>, `700102417` <int>, `700114168` <int>,#> # `700037540` <int>, `700106397` <int>, `700113498` <int>, `700033743` <int>,#> # `700105205` <int>, `700024238` <int>, `700034183` <int>, `700038390` <int>,#> # `700015973` <int>, `700038124` <int>, `700107206` <int>, `700037403` <int>,#> # `700098429` <int>, `700101224` <int>, `700114615` <int>, `700024234` <int>,#> # `700108596` <int>, `700101076` <int>, `700105882` <int>, `700016902` <int>,#> # `700102242` <int>, `700038231` <int>, `700109394` <int>, `700102530` <int>,#> # `700108229` <int>, `700099013` <int>, `700098680` <int>, `700106938` <int>,#> # `700014916` <int>, `700095535` <int>, `700102367` <int>, `700101358` <int>print(hmp_samples)#> # A tibble: 50 x 3#> # Groups: body_site, sex [10]#> sample_id sex body_site#> <chr> <chr> <chr>#> 1 700035949 female Nose#> 2 700097855 female Nose#> 3 700100489 female Nose#> 4 700111314 female Nose#> 5 700033744 female Nose#> 6 700109581 male Nose#> 7 700111044 male Nose#> 8 700101365 male Nose#> 9 700100431 male Nose#> 10 700016050 male Nose#> # ... with 40 more rows
We can parse the taxonomic information in the abundance matrix using a parser from taxa:
obj <- parse_tax_data(hmp_otus, class_cols = "lineage", class_sep = ";",class_key = c(tax_rank = "info", tax_name = "taxon_name"),class_regex = "^(.+)__(.+)$")
This returns a taxmap object. The taxmap class is designed to store any number of tables, lists, or vectors associated with taxonomic information and facilitate manipulating the data in a cohesive way. Here is what that object looks like:
print(obj)#> <Taxmap>#> 174 taxa: ab. Root, ac. Proteobacteria ... gr. Blautia, gs. Clostridium#> 174 edges: NA->ab, ab->ac, ab->ad, ab->ae ... cm->gq, cf->gr, cw->gs#> 2 data sets:#> tax_data:#> # A tibble: 1,000 x 53#> taxo… otu_… line… `700… `700… `700… `700… `700… `700… `700… `700… `700… `700…#> <chr> <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>#> 1 dm OTU_… r__R… 0 2 1 0 0 0 0 0 0 0#> 2 dn OTU_… r__R… 0 0 0 0 0 0 0 0 0 0#> 3 do OTU_… r__R… 0 1 0 0 0 0 0 0 0 0#> # ... with 997 more rows, and 40 more variables: `700032425` <int>,#> # `700024855` <int>, `700103488` <int>, `700096869` <int>, `700107379` <int>,#> # `700096422` <int>, `700102417` <int>, `700114168` <int>, `700037540` <int>,#> # `700106397` <int>, `700113498` <int>, `700033743` <int>, `700105205` <int>,#> # `700024238` <int>, `700034183` <int>, `700038390` <int>, `700015973` <int>,#> # `700038124` <int>, `700107206` <int>, `700037403` <int>, `700098429` <int>,#> # `700101224` <int>, `700114615` <int>, `700024234` <int>, `700108596` <int>,#> # `700101076` <int>, `700105882` <int>, `700016902` <int>, `700102242` <int>,#> # `700038231` <int>, `700109394` <int>, `700102530` <int>, `700108229` <int>,#> # `700099013` <int>, `700098680` <int>, `700106938` <int>, `700014916` <int>,#> # `700095535` <int>, `700102367` <int>, `700101358` <int>#> class_data:#> # A tibble: 5,922 x 5#> taxon_id input_index tax_rank tax_name regex_match#> <chr> <int> <chr> <chr> <chr>#> 1 ab 1 r Root r__Root#> 2 ac 1 p Proteobacteria p__Proteobacteria#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacteria#> # ... with 5,919 more rows#> 0 functions:
Abundance matrix manipulations
Removing low-abundance counts
Low-abundance sequences might be the result of sequencing error, so typically we remove any counts/OTUs with less than some number of reads. Lets set all counts with less than 5 reads to zero:
obj$data$tax_data <- zero_low_counts(obj, "tax_data", min_count = 5)#> Converting to zero all counts less than 5.#> No `cols` specified, so using all numeric columns:#> 700035949, 700097855, 700100489 ... 700095535, 700102367, 700101358#> Zeroing 4325 of 50000 counts less than 5.
There might now be some OTUs with no "real" reads. Lets check:
no_reads <- rowSums(obj$data$tax_data[, hmp_samples$sample_id]) == 0sum(no_reads)#> [1] 211
It appears that 211 of 1000 OTUs now have no reads. We can remove those OTUs and their associated taxa with filter_obs:
obj <- filter_obs(obj, "tax_data", ! no_reads, drop_taxa = TRUE)print(obj)#> <Taxmap>#> 155 taxa: ab. Root, ac. Proteobacteria ... gs. Clostridium#> 155 edges: NA->ab, ab->ac, ab->ad, ab->ae ... dk->gp, cm->gq, cw->gs#> 2 data sets:#> tax_data:#> # A tibble: 789 x 51#> taxo… `700… `700… `700… `700… `700… `700… `700… `700… `700… `700… `700… `700…#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 dm 0 0 0 0 0 0 0 0 0 0 0 6.00#> 2 dn 0 0 0 0 0 0 0 0 0 0 0 0#> 3 do 0 0 0 0 0 0 0 0 0 0 0 0#> # ... with 786 more rows, and 38 more variables: `700103488` <dbl>,#> # `700096869` <dbl>, `700107379` <dbl>, `700096422` <dbl>, `700102417` <dbl>,#> # `700114168` <dbl>, `700037540` <dbl>, `700106397` <dbl>, `700113498` <dbl>,#> # `700033743` <dbl>, `700105205` <dbl>, `700024238` <dbl>, `700034183` <dbl>,#> # `700038390` <dbl>, `700015973` <dbl>, `700038124` <dbl>, `700107206` <dbl>,#> # `700037403` <dbl>, `700098429` <dbl>, `700101224` <dbl>, `700114615` <dbl>,#> # `700024234` <dbl>, `700108596` <dbl>, `700101076` <dbl>, `700105882` <dbl>,#> # `700016902` <dbl>, `700102242` <dbl>, `700038231` <dbl>, `700109394` <dbl>,#> # `700102530` <dbl>, `700108229` <dbl>, `700099013` <dbl>, `700098680` <dbl>,#> # `700106938` <dbl>, `700014916` <dbl>, `700095535` <dbl>, `700102367` <dbl>,#> # `700101358` <dbl>#> class_data:#> # A tibble: 5,922 x 5#> taxon_id input_index tax_rank tax_name regex_match#> <chr> <int> <chr> <chr> <chr>#> 1 ab 1 r Root r__Root#> 2 ac 1 p Proteobacteria p__Proteobacteria#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacteria#> # ... with 5,919 more rows#> 0 functions:
Note how there are fewer taxa now, as well as fewer OTUs. This coordinated manipulation of taxonomic and abundance data is one of the main benefits of using the taxmap class.
Accounting for un-even sampling
These are raw counts, but people typically work with rarefied counts or proportions to avoid sampling depth biasing the results. The function rarefy_obs will return the rarefied counts for a table in a taxmap object, but lets use proportions for this demonstration:
obj$data$tax_data <- calc_obs_props(obj, "tax_data")#> No `cols` specified, so using all numeric columns:#> 700035949, 700097855, 700100489 ... 700095535, 700102367, 700101358print(obj)#> <Taxmap>#> 155 taxa: ab. Root, ac. Proteobacteria ... gs. Clostridium#> 155 edges: NA->ab, ab->ac, ab->ad, ab->ae ... dk->gp, cm->gq, cw->gs#> 2 data sets:#> tax_data:#> # A tibble: 789 x 51#> taxon… `7000… `7000… `7001… `7001… `7000… `700… `700… `700… `700… `700… `700…#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 dm 0 0 0 0 0 0 0 0 0 0 0#> 2 dn 0 0 0 0 0 0 0 0 0 0 0#> 3 do 0 0 0 0 0 0 0 0 0 0 0#> # ... with 786 more rows, and 39 more variables: `700024855` <dbl>,#> # `700103488` <dbl>, `700096869` <dbl>, `700107379` <dbl>, `700096422` <dbl>,#> # `700102417` <dbl>, `700114168` <dbl>, `700037540` <dbl>, `700106397` <dbl>,#> # `700113498` <dbl>, `700033743` <dbl>, `700105205` <dbl>, `700024238` <dbl>,#> # `700034183` <dbl>, `700038390` <dbl>, `700015973` <dbl>, `700038124` <dbl>,#> # `700107206` <dbl>, `700037403` <dbl>, `700098429` <dbl>, `700101224` <dbl>,#> # `700114615` <dbl>, `700024234` <dbl>, `700108596` <dbl>, `700101076` <dbl>,#> # `700105882` <dbl>, `700016902` <dbl>, `700102242` <dbl>, `700038231` <dbl>,#> # `700109394` <dbl>, `700102530` <dbl>, `700108229` <dbl>, `700099013` <dbl>,#> # `700098680` <dbl>, `700106938` <dbl>, `700014916` <dbl>, `700095535` <dbl>,#> # `700102367` <dbl>, `700101358` <dbl>#> class_data:#> # A tibble: 5,922 x 5#> taxon_id input_index tax_rank tax_name regex_match#> <chr> <int> <chr> <chr> <chr>#> 1 ab 1 r Root r__Root#> 2 ac 1 p Proteobacteria p__Proteobacteria#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacteria#> # ... with 5,919 more rows#> 0 functions:
Getting per-taxon information
Currently, we have values for the abundance of each OTU, not each taxon. To get information on the taxa, we can sum the abundance per-taxon like so:
obj$data$tax_abund <- calc_taxon_abund(obj, "tax_data",cols = hmp_samples$sample_id)#> Summing per-taxon counts from 50 columns for 155 taxaprint(obj)#> <Taxmap>#> 155 taxa: ab. Root, ac. Proteobacteria ... gs. Clostridium#> 155 edges: NA->ab, ab->ac, ab->ad, ab->ae ... dk->gp, cm->gq, cw->gs#> 3 data sets:#> tax_data:#> # A tibble: 789 x 51#> taxon… `7000… `7000… `7001… `7001… `7000… `700… `700… `700… `700… `700… `700…#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 dm 0 0 0 0 0 0 0 0 0 0 0#> 2 dn 0 0 0 0 0 0 0 0 0 0 0#> 3 do 0 0 0 0 0 0 0 0 0 0 0#> # ... with 786 more rows, and 39 more variables: `700024855` <dbl>,#> # `700103488` <dbl>, `700096869` <dbl>, `700107379` <dbl>, `700096422` <dbl>,#> # `700102417` <dbl>, `700114168` <dbl>, `700037540` <dbl>, `700106397` <dbl>,#> # `700113498` <dbl>, `700033743` <dbl>, `700105205` <dbl>, `700024238` <dbl>,#> # `700034183` <dbl>, `700038390` <dbl>, `700015973` <dbl>, `700038124` <dbl>,#> # `700107206` <dbl>, `700037403` <dbl>, `700098429` <dbl>, `700101224` <dbl>,#> # `700114615` <dbl>, `700024234` <dbl>, `700108596` <dbl>, `700101076` <dbl>,#> # `700105882` <dbl>, `700016902` <dbl>, `700102242` <dbl>, `700038231` <dbl>,#> # `700109394` <dbl>, `700102530` <dbl>, `700108229` <dbl>, `700099013` <dbl>,#> # `700098680` <dbl>, `700106938` <dbl>, `700014916` <dbl>, `700095535` <dbl>,#> # `700102367` <dbl>, `700101358` <dbl>#> class_data:#> # A tibble: 5,922 x 5#> taxon_id input_index tax_rank tax_name regex_match#> <chr> <int> <chr> <chr> <chr>#> 1 ab 1 r Root r__Root#> 2 ac 1 p Proteobacteria p__Proteobacteria#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacteria#> # ... with 5,919 more rows#> tax_abund:#> # A tibble: 155 x 51#> taxon… `7000… `700097… `7001… `70011… `7000… `700109… `70011… `70010… `70010…#> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 ab 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00#> 2 ac 0.206 0.0262 0 0.252 0.225 0.00187 0 0.00736 0.0938#> 3 ad 0 0.00269 0 0.0417 0 0 0.00439 0 0.00708#> # ... with 152 more rows, and 41 more variables: `700016050` <dbl>,#> # `700032425` <dbl>, `700024855` <dbl>, `700103488` <dbl>, `700096869` <dbl>,#> # `700107379` <dbl>, `700096422` <dbl>, `700102417` <dbl>, `700114168` <dbl>,#> # `700037540` <dbl>, `700106397` <dbl>, `700113498` <dbl>, `700033743` <dbl>,#> # `700105205` <dbl>, `700024238` <dbl>, `700034183` <dbl>, `700038390` <dbl>,#> # `700015973` <dbl>, `700038124` <dbl>, `700107206` <dbl>, `700037403` <dbl>,#> # `700098429` <dbl>, `700101224` <dbl>, `700114615` <dbl>, `700024234` <dbl>,#> # `700108596` <dbl>, `700101076` <dbl>, `700105882` <dbl>, `700016902` <dbl>,#> # `700102242` <dbl>, `700038231` <dbl>, `700109394` <dbl>, `700102530` <dbl>,#> # `700108229` <dbl>, `700099013` <dbl>, `700098680` <dbl>, `700106938` <dbl>,#> # `700014916` <dbl>, `700095535` <dbl>, `700102367` <dbl>, `700101358` <dbl>#> 0 functions:
Note that there is now an additional table with one row per taxon.
We can also easily calculate the number of samples have reads for each taxon:
obj$data$tax_occ <- calc_n_samples(obj, "tax_abund", groups = hmp_samples$body_site)#> No `cols` specified, so using all numeric columns:#> 700035949, 700097855, 700100489 ... 700095535, 700102367, 700101358print(obj)#> <Taxmap>#> 155 taxa: ab. Root, ac. Proteobacteria ... gs. Clostridium#> 155 edges: NA->ab, ab->ac, ab->ad, ab->ae ... dk->gp, cm->gq, cw->gs#> 4 data sets:#> tax_data:#> # A tibble: 789 x 51#> taxon… `7000… `7000… `7001… `7001… `7000… `700… `700… `700… `700… `700… `700…#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 dm 0 0 0 0 0 0 0 0 0 0 0#> 2 dn 0 0 0 0 0 0 0 0 0 0 0#> 3 do 0 0 0 0 0 0 0 0 0 0 0#> # ... with 786 more rows, and 39 more variables: `700024855` <dbl>,#> # `700103488` <dbl>, `700096869` <dbl>, `700107379` <dbl>, `700096422` <dbl>,#> # `700102417` <dbl>, `700114168` <dbl>, `700037540` <dbl>, `700106397` <dbl>,#> # `700113498` <dbl>, `700033743` <dbl>, `700105205` <dbl>, `700024238` <dbl>,#> # `700034183` <dbl>, `700038390` <dbl>, `700015973` <dbl>, `700038124` <dbl>,#> # `700107206` <dbl>, `700037403` <dbl>, `700098429` <dbl>, `700101224` <dbl>,#> # `700114615` <dbl>, `700024234` <dbl>, `700108596` <dbl>, `700101076` <dbl>,#> # `700105882` <dbl>, `700016902` <dbl>, `700102242` <dbl>, `700038231` <dbl>,#> # `700109394` <dbl>, `700102530` <dbl>, `700108229` <dbl>, `700099013` <dbl>,#> # `700098680` <dbl>, `700106938` <dbl>, `700014916` <dbl>, `700095535` <dbl>,#> # `700102367` <dbl>, `700101358` <dbl>#> class_data:#> # A tibble: 5,922 x 5#> taxon_id input_index tax_rank tax_name regex_match#> <chr> <int> <chr> <chr> <chr>#> 1 ab 1 r Root r__Root#> 2 ac 1 p Proteobacteria p__Proteobacteria#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacteria#> # ... with 5,919 more rows#> tax_abund:#> # A tibble: 155 x 51#> taxon… `7000… `700097… `7001… `70011… `7000… `700109… `70011… `70010… `70010…#> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 ab 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00#> 2 ac 0.206 0.0262 0 0.252 0.225 0.00187 0 0.00736 0.0938#> 3 ad 0 0.00269 0 0.0417 0 0 0.00439 0 0.00708#> # ... with 152 more rows, and 41 more variables: `700016050` <dbl>,#> # `700032425` <dbl>, `700024855` <dbl>, `700103488` <dbl>, `700096869` <dbl>,#> # `700107379` <dbl>, `700096422` <dbl>, `700102417` <dbl>, `700114168` <dbl>,#> # `700037540` <dbl>, `700106397` <dbl>, `700113498` <dbl>, `700033743` <dbl>,#> # `700105205` <dbl>, `700024238` <dbl>, `700034183` <dbl>, `700038390` <dbl>,#> # `700015973` <dbl>, `700038124` <dbl>, `700107206` <dbl>, `700037403` <dbl>,#> # `700098429` <dbl>, `700101224` <dbl>, `700114615` <dbl>, `700024234` <dbl>,#> # `700108596` <dbl>, `700101076` <dbl>, `700105882` <dbl>, `700016902` <dbl>,#> # `700102242` <dbl>, `700038231` <dbl>, `700109394` <dbl>, `700102530` <dbl>,#> # `700108229` <dbl>, `700099013` <dbl>, `700098680` <dbl>, `700106938` <dbl>,#> # `700014916` <dbl>, `700095535` <dbl>, `700102367` <dbl>, `700101358` <dbl>#> tax_occ:#> # A tibble: 155 x 6#> taxon_id Nose Saliva Skin Stool Throat#> <chr> <int> <int> <int> <int> <int>#> 1 ab 10 10 10 10 10#> 2 ac 8 10 9 2 10#> 3 ad 5 10 8 10 10#> # ... with 152 more rows#> 0 functions:
Plotting taxonomic data
Now that we have per-taxon information, we can plot the information using heat trees. The code below plots the number of "Nose" samples that have reads for each taxon. It also plots the number of OTUs assigned to each taxon in the overall dataset.
heat_tree(obj,node_label = taxon_names,node_size = n_obs,node_color = Nose,node_size_axis_label = "OTU count",node_color_axis_label = "Samples with reads")
Note how we did not have to specify the full path to the variable "Nose", but just its name. This is a shorthand for convenience. We could have made the same plot using this command:
heat_tree(obj,node_label = obj$taxon_names(),node_size = obj$n_obs(),node_color = obj$data$tax_occ$Nose,node_size_axis_label = "OTU count",node_color_axis_label = "Samples with reads")
Comparing two treatments/groups
Usually we are interested in how groups of samples compare. For example, we might want to know which taxa differ between the nose and throat, or between men and women. The function compare_groups facilitates these comparisons:
obj$data$diff_table <- compare_groups(obj, dataset = "tax_abund",cols = hmp_samples$sample_id,groups = hmp_samples$sex)print(obj$data$diff_table)#> # A tibble: 155 x 7#> taxon_id treatment_1 treatment_2 log2_median_ratio median_diff mean_diff wilcox_p_val…#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>#> 1 ab female male 0 0 0 NaN#> 2 ac female male 0.380 0.0229 0.0379 0.470#> 3 ad female male -0.434 -0.0449 -0.0199 0.907#> 4 ae female male -1.68 -0.0475 -0.0753 0.335#> 5 af female male 0.649 0.116 0.0614 0.386#> 6 ag female male 0 0 -0.00275 0.732#> 7 ah female male 0 0 -0.00140 0.602#> 8 aj female male 1.24 0.0162 -0.0129 0.680#> 9 ak female male 0 0 0.00121 0.416#> 10 al female male -0.542 -0.0541 -0.0211 0.969#> # ... with 145 more rows
We can use this information to create what we call a "differential heat tree", which indicates which taxa are more abundant in each treatment:
heat_tree(obj,node_label = taxon_names,node_size = n_obs,node_color = log2_median_ratio,node_color_interval = c(-2, 2),node_color_range = c("cyan", "gray", "tan"),node_size_axis_label = "OTU count",node_color_axis_label = "Log 2 ratio of median proportions")
In this case, taxa colored tan are more abundant in women and those colored blue are more abundant in men. Note that we have not taken into account statistics significance when showing this, so lets do that. First, we need to correct for multiple comparisons:
obj$data$diff_table$wilcox_p_value <- p.adjust(obj$data$diff_table$wilcox_p_value,method = "fdr")
If we then look at the distribution of p-values, we can see that none are even close to significant:
hist(obj$data$diff_table$wilcox_p_value)
There is no need to graph this, but if there still were some significant differences, we could set any difference that is not significant to zero and repeat the last heat_tree command.
Comparing any number of treatments/groups
A single differential heat tree can compare two treatments, but what if you have more? Then we can make a matrix of heat trees, one for each pairwise comparison of treatments like so:
obj$data$diff_table <- compare_groups(obj, dataset = "tax_abund",cols = hmp_samples$sample_id,groups = hmp_samples$body_site)print(obj$data$diff_table)#> # A tibble: 1,550 x 7#> taxon_id treatment_1 treatment_2 log2_median_ratio median_diff mean_diff wilcox_p_val…#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>#> 1 ab Nose Saliva 0 0 0 NaN#> 2 ac Nose Saliva - 2.62 -0.167 -0.128 0.0172#> 3 ad Nose Saliva - 7.68 -0.274 -0.265 0.000163#> 4 ae Nose Saliva 5.36 0.616 0.595 0.0000108#> 5 af Nose Saliva - 1.23 -0.260 -0.159 0.0433#> 6 ag Nose Saliva -Inf -0.0228 -0.0436 0.0000874#> 7 ah Nose Saliva 0 0 0 NaN#> 8 aj Nose Saliva - 3.83 -0.103 -0.0803 0.00707#> 9 ak Nose Saliva -Inf -0.0174 -0.0174 0.00156#> 10 al Nose Saliva -Inf -0.258 -0.248 0.000149#> # ... with 1,540 more rows
There is a special function to plot this type of data called heat_tree_matrix:
heat_tree_matrix(obj,dataset = "diff_table",node_size = n_obs,node_label = taxon_names,node_color = log2_median_ratio,node_color_range = diverging_palette(),node_color_trans = "linear",node_color_interval = c(-3, 3),edge_color_interval = c(-3, 3),node_size_axis_label = "Number of OTUs",node_color_axis_label = "Log2 ratio median proportions")
More information
This document is only a short introduction to metacoder and there is much that is not covered. For more information, see our website at http://grunwaldlab.github.io/metacoder_documentation/ and our github repository at https://github.com/grunwaldlab/metacoder. There is also extensive help and examples in the function documentation that can be accessed by, for example, ?heat_tree.
Feedback
We welcome any kind of feedback! Let us know if you run into problems by submitting an issue on our Github repo: https://github.com/grunwaldlab/metacoder
Dependencies
The function that runs in silico PCR requires primersearch from the EMBOSS tool kit to be installed. This is not an R package, so it is not automatically installed. Type ?primersearch after installing and loading metacoder for installation instructions.
Citation
If you use metcoder in a publication, please cite our article in PLOS Computational Biology:
Foster ZSL, Sharpton TJ, Grünwald NJ (2017) Metacoder: An R package for visualization and manipulation of community taxonomic diversity data. PLOS Computational Biology 13(2): e1005404. https://doi.org/10.1371/journal.pcbi.1005404
License
This work is subject to the MIT License.
Acknowledgements
This package includes code from the R package ggrepel to handle label overlap avoidance with permission from the author of ggrepel Kamil Slowikowski. We included the code instead of depending on ggrepel because we are using functions internal to ggrepel that might change in the future. We thank Kamil Slowikowski for letting us use his code and would like to acknowledge his implementation of the label overlap avoidance used in metacoder.
News
News
metacoder 0.2.0
Bug fixes
- Fixed bug in
subtaxathat caused an error when all ofsubsetisFALSE. (issue #143) - Fixed bug in
filter_taxathat caused an error when all taxa are filtered out. (issue #144)
Breaking changes
- All taxmap-related manipulation functions have been moved to the taxa package.
heat_treenow uses thetaxmapclass defined in the taxa package.- Numerous changes (i.e. upgrades) to
primersearch
Improvements
- Upgraded
primersearchoutput to be cleaner and have info like the amplicon sequence and primer binding sites. - Added functions to identift and remove taxa with ambiguous names like "unknown"
- code from ggrepel package now used to avoid overlapping labels. Thanks Kamil Slowikowski!
- New function
heat_tree_matrixto make plotting a pairwise matrix of heat trees for comparing treatments. - New parser named
parse_mothur_tax_summaryfor mothur *.tax.summary file made by classify.seqs. - New parser named
parse_mothur_taxonomyfor mothur *.taxonomy file made by classify.seqs. - New parser named
parse_qiime_biomfor the QIIME BIOM output. - New parser named
parse_phyloseqto convert phyloseq objects. - New parser named
parse_newickto parse newick files. - New parser named
parse_unite_generalfor unite general FASTA release. (issue #154) - New parser named
parse_rdpfor RDP FASTA release. (issue #160) - New parser named
parse_silva_fastafor SILVA FASTA release. (issue #162) - New function
calc_obs_propsto calculate proportions from observation counts (issue #167 - New parser named
parse_greengenesfor the Greengenes database. (issue #?) - New writer named
write_greengenesto create an imitation of the Greengenes database format. - New writer named
write_rdpto create an imitation of the RDP database format. - New writer named
write_mothur_taxonomyto create an imitation of the mothur taxonomy format. - New writer named
write_unite_generalto create an imitation of the UNITE general FASTA release. - New writer named
write_silva_fastato create an imitation of the SILVA FASTA release. - New function named
compare_treatmentsto compare multiple samples in multiple treatments, applying a user-defined function. - New function named
calc_taxon_abundto sum observation values for each taxon. - Added
col_namesoption tocalc_taxon_abundto set names of output columns.
metacoder 0.1.3
Improvements
- Provided helpful error message when the
evaluation nested too deeply: infinite recursion / options(expressions=)?occurs due to too many labels being printed. heat_tree: improved how the predicted bondries of text is calcuated, so text with any rotation, justification, or newlines influences margins correctly (i.e. does not get cut off).heat_tree: Can now save multiple file outputs in different formats at once
Minor changes
heat_treenow gives a warning if infinite values are given to itextract_taxonomy: There is now a warning message if class regex does not match (issue #123)heat_tree: Increased lengend text size and reduced number of labelsextract_taxonomy: addedbatch_sizeoption to help deal with invalid IDs better- Added CITATION file
Breaking changes
- The
heat_treeoptionmargin_sizefuncion now takes four values instead of 2.
Bug fixes
heat_tree: Fixed bug when color is set explicitly (e.g. "grey") instead of raw numbers and the legend is not removed. Now a mixure of raw numbers and color names can be used.- Fixed bugs caused by dplyr version update
- Fixed bug in
heat_treethat made values not in the input taxmap object not associate with the right taxa. See this post. extract_taxonomy: Fixed an error that occured when not all inputs could be classified and sequences were supplied- Fixed bug in
primersearchthat cased the wrong primer sequence to be returned when primers match in the reverse direction - Fixed a bug in
parse_mothur_summarywhere "unclassified" had got changed to "untaxmap" during a search and replace - Fixed outdated example code for
extract_taxonomy - Fixed a bug in
mutate_taxaandmutate_obsthat made replacing columns result in new columns with duplicate names.
metacoder 0.1.2
Breaking changes
plot_taxonomyand theplotmethod have been renamedheat_tree.
New features
- New introduction vignette
- Various minor bug fixes
metacoder 0.1.1
Breaking changes
taxon_levelshave been replaced withn_supertaxato make names conceptually consistent. Note that this means what was1astaxon_levelsis now0asn_supertaxa.
New features
- Added
n_subtaxaandn_subtaxa_1functions - Added taxonomy parsing examples to vignettes
metacoder 0.1.0
Breaking changes
- Many options and functions have been renamed (#115)
New features
- dplyr functions for
taxmapobjects! - Added a
printmethod fortaxmapobjects - new SILVA example data set
extract_taxonomyworks onSeqFastadnaclass fromseqinrparse_mothur_summaryfunction: parses the mothur summary tableremove_redundant_namesfunction: removes components of names of taxa in subtaxa
Changes
- Core functions are much faster
- More tests
- Updated vignettes
- Many bug fixes and minor upgrades
- Legend now moves into plot if there is room (#118)