Sub-class metagenomics

This is a sub-class that is compatible to data obtained from either 16S rRNA marker-gene sequencing or shot-gun metagenomics sequencing. It inherits all methods from the abstract class omics and only adapts the initialize function. It supports BIOM format data (v2.1.0 from http://biom-format.org/) in both HDF5 and JSON format, also pre-existing data structures can be used or text files. When omics data is very large, data loading becomes very expensive. It is therefore recommended to use the reset() method to reset your changes. Every omics class creates an internal memory efficient back-up of the data, the resetting of changes is an instant process.

Super class

omics -> metagenomics

Active bindings

treeData: A "phylo" class, see as.phylo.

Methods

Inherited methods

`metagenomics$new()`

Initializes the metagenomics class object with metagenomics$new()

Usage

metagenomics$new(
  countData = NULL,
  metaData = NULL,
  featureData = NULL,
  treeData = NULL,
  biomData = NULL,
  feature_names = c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
)

Arguments

countData: A path to an existing file or a dense/sparse Matrix format.
metaData: A path to an existing file, data.table or data.frame.
featureData: A path to an existing file, data.table or data.frame.
treeData: A path to an existing newick file or class "phylo", see read.tree.
biomData: A path to an existing biom file, version 2.1.0 (http://biom-format.org/), see h5read.
feature_names: A character vector to name the feature names that fit the supplied featureData.

Returns

A new metagenomics object.

`metagenomics$write_biom()`

Creates a BIOM file in HDF5 format of the loaded items via 'new()', which is compatible to the python biom-format version 2.1, see http://biom-format.org.

Usage

metagenomics$write_biom(filename)

Arguments

filename: A character variable of either the full path of filename of the biom file (e.g. output.biom)

Examples

library("OmicFlow")

metadata_file <- system.file("extdata", "metadata.tsv", package = "OmicFlow")
counts_file <- system.file("extdata", "counts.tsv", package = "OmicFlow")
features_file <- system.file("extdata", "features.tsv", package = "OmicFlow")
tree_file <- system.file("extdata", "tree.newick", package = "OmicFlow")

taxa <- metagenomics$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file,
 treeData = tree_file
)

taxa$write_biom(filename = "output.biom")
file.remove("output.biom")

`metagenomics$foldchange()`

Differential feature expression (DFE) Total Sample Sum (TSS) transformed values for both paired and non-paired test.

The function performs feature agglomeration, subsetting to remove NAs in condition.group, finding samplepairs and finally feature scaling prior to fold-change computation. Based on the transform method, fold-changes will be computed either via subtraction or division.

Usage

metagenomics$foldchange(
  condition.group,
  condition_A,
  condition_B,
  group_by = NULL,
  feature_rank = "FEATURE_ID",
  feature_filter = NULL,
  paired = FALSE,
  normalize = FALSE,
  pvalue.threshold = 0.05,
  logfold.threshold = 0.06,
  abundance.threshold = 0
)

Arguments

condition.group: A character variable of an existing column name in metaData, wherein the conditions A and B are located.
condition_A: A character value or vector of characters.
condition_B: A character value or vector of characters.
group_by: A character variable of an existing column in metaData to split the table in chunks prior to fold-change computation (default: NULL).
feature_rank: A character or vector of characters in the featureData to aggregate via feature_merge() (default: "FEATURE_ID").
feature_filter: A character or vector of characters to remove features via regex pattern (default: NULL).
paired: A boolean value, the paired is only applicable when a SAMPLEPAIR_ID column exists within the metaData. See wilcox.test and samplepair_subset().
normalize: A boolean value wether to normalize via Total Sample Sums (TSS) or not (default: FALSE).
pvalue.threshold: A numeric value used as a p-value threshold to label and color significant features (default: 0.05).
logfold.threshold: A numeric value used as a fold-change threshold to label and color significantly expressed features (default: 0.06).
abundance.threshold: A numeric value used as an abundance threshold to size the scatter dots based on their mean abundance (default: 0.01).

Returns

data A long data.table table.
volcano_plot A ggplot object.
A A data.table table for (each) condition A.
B A data.table table for (each) condition B.

Examples

library("ggplot2")
library("OmicFlow")

metadata_file <- system.file("extdata", "metadata.tsv", package = "OmicFlow")
counts_file <- system.file("extdata", "counts.tsv", package = "OmicFlow")
features_file <- system.file("extdata", "features.tsv", package = "OmicFlow")

obj <- metagenomics$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file
)

dfe <- obj$foldchange(feature_rank = "Genus",
                      paired = FALSE,
                      condition.group = "treatment",
                      condition_A = c("healthy"),
                      condition_B = c("tumor"))

`metagenomics$clone()`

The objects of this class are cloneable with this method.

Usage

metagenomics$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `metagenomics$write_biom()`
## ------------------------------------------------

library("OmicFlow")

metadata_file <- system.file("extdata", "metadata.tsv", package = "OmicFlow")
counts_file <- system.file("extdata", "counts.tsv", package = "OmicFlow")
features_file <- system.file("extdata", "features.tsv", package = "OmicFlow")
tree_file <- system.file("extdata", "tree.newick", package = "OmicFlow")

taxa <- metagenomics$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file,
 treeData = tree_file
)
#> ✔ metaData template passed the JSON validation.
#> ℹ Checking for duplicated identifiers ..
#> ✔ featureData is loaded.
#> ✔ countData is loaded.
#> ✔ treeData is loaded.
#> ℹ Final steps .. cleaning & creating back-up
#> 
#> ── <metagenomics> object 
#> metaData: 9 variables × 4 samples
#> countData: 4 samples × 242 features
#> featureData: 7 attributes × 242 features
#> treeData: 242 tips × 241 nodes

taxa$write_biom(filename = "output.biom")
file.remove("output.biom")
#> [1] TRUE


## ------------------------------------------------
## Method `metagenomics$foldchange()`
## ------------------------------------------------

library("ggplot2")
library("OmicFlow")

metadata_file <- system.file("extdata", "metadata.tsv", package = "OmicFlow")
counts_file <- system.file("extdata", "counts.tsv", package = "OmicFlow")
features_file <- system.file("extdata", "features.tsv", package = "OmicFlow")

obj <- metagenomics$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file
)
#> ✔ metaData template passed the JSON validation.
#> ℹ Checking for duplicated identifiers ..
#> ✔ featureData is loaded.
#> ✔ countData is loaded.
#> ℹ Final steps .. cleaning & creating back-up
#> 
#> ── <metagenomics> object 
#> metaData: 9 variables × 4 samples
#> countData: 4 samples × 242 features
#> featureData: 7 attributes × 242 features

dfe <- obj$foldchange(feature_rank = "Genus",
                      paired = FALSE,
                      condition.group = "treatment",
                      condition_A = c("healthy"),
                      condition_B = c("tumor"))
#> 
#> ── <metagenomics> object 
#> metaData: 9 variables × 4 samples
#> countData: 4 samples × 64 features
#> featureData: 7 attributes × 64 features

See also

Super class

Active bindings

Methods

Public methods

metagenomics$new()

Usage

Arguments

Returns

metagenomics$write_biom()

Usage

Arguments

Examples

metagenomics$foldchange()

Usage

Arguments

Returns

Examples

metagenomics$clone()

Usage

Arguments

Examples

`metagenomics$new()`

`metagenomics$write_biom()`

`metagenomics$foldchange()`

`metagenomics$clone()`