mbtools
is a collection of helpers that we use to analyze microbiome
data. It makes it easier to run some common analyses and is pretty
opinionated towards our own experiences.
mbtools
sees analyses as a workflow consisting of several analysis steps
and final outputs based on those. This is pretty similar to Qiime 2 which pioneered
that abstraction.
mbtools
mostly works on the following data types:
For mbtools
a workflow step is based on input data and a configuration,
thus having the function signature step(object, config)
.
Most steps can be chained with the pipe operator to yield workflows.
For instance, the following is a possible workflow with mbtools
:
library(mbtools)
config <- list(
demultiplex = config_demultiplex(barcodes = c("ACGTA", "AGCTT")),
preprocess = config_preprocess(truncLen = 200),
denoise = config_denoise()
)
output <- find_read_files("raw") %>%
demultiplex(config$demultiplex) %>%
quality_control() %>%
preprocess(config$preprocess) %>%
denoise(config$denoise)
This clearly logs the used workflow and the configuration. The configuration can also be saved and read in many formats, for instance yaml.
config.yaml:
preprocess:
threads: yes
out_dir: preprocessed
trimLeft: 10.0
truncLen: 200.0
maxEE: 2.0
denoise:
threads: yes
nbases: 2.5e+08
pool: no
bootstrap_confidence: 0.5
taxa_db: https://zenodo.org/record/1172783/files/silva_nr_v132_train_set.fa.gz?download=1
species_db: https://zenodo.org/record/1172783/files/silva_species_assignment_v132.fa.gz?download=1
hash: yes
This can now be reused by someone else:
config <- read_yaml("config.yml")
output <- find_read_files("raw") %>%
quality_control() %>%
preprocess(config$preprocess) %>%
denoise(config$denoise)
All other functions are usually functions that are meant to be inside more complex code or functions that produce plots and endpoints of an analysis. Most of them act on phyloseq objects and some on tidy data tables.
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