omics: Abstract omics class

Description

This is the abstract class 'omics', contains a variety of methods that are inherited and applied in the omics classes: metagenomics, proteomics and metabolomics.

Arguments

Value

A list of components:

div A data.frame from diversity.
stats A pairwise statistics from pairwise_wilcox_test.
plot A ggplot object.

A list of components:

data A data.table of feature compositions.
palette A setNames palette from colormap.

A list of components:

distmat A distance dissimilarity in matrix format.
stats A statistical test as a data.frame.
pcs principal components as a data.frame.
scree_plot A ggplot object.
anova_plot A ggplot object.
scores_plot A ggplot object.

dfe A long data.table table.
volcano_plot A ggplot object.

Public fields

countData: A path to an existing file, data.table, data.frame, matrix or sparseMatrix with zero values.
featureData: A path to an existing file, data.table or data.frame.
metaData: A path to an existing file, data.table or data.frame.
.valid_schema: Boolean value for schema validation via JSON
.feature_id: A character, default name for the feature identifiers.
.sample_id: A character, default name for the sample identifiers.
.samplepair_id: A character, default name for the sample pair identifiers.

Methods

Public methods

Method `new()`

Wrapper function that is inherited and adapted for each omics class. The omics classes requires a metadata samplesheet, that is validated by the metadata_schema.json. It requires a column SAMPLE_ID and optionally a SAMPLEPAIR_ID or FEATURE_ID can be supplied. The SAMPLE_ID will be used to link the metaData to the countData, and will act as the key during subsetting of other columns. To create a new object use new() method. Do notice that the abstract class only checks if the metadata is valid! The countData and featureData will not be checked, these are handled by the sub-classes. Using the omics class to load your data is not supported and still experimental.

Usage

omics$new(countData = NULL, featureData = NULL, metaData = NULL)

Arguments

countData: A path to an existing file, data.table, data.frame, matrix or sparseMatrix with zero values.

featureData

A path to an existing file, data.table or data.frame.

metaData

A path to an existing file, data.table or data.frame.

Returns

A new omics object.

Method `validate()`

Validates an input metadata against the JSON schema. The metadata should look as follows and should not contain any empty spaces. For example; 'sample 1' is not allowed, whereas 'sample1' is allowed!

Acceptable column headers:

SAMPLE_ID (required)
SAMPLEPAIR_ID (optional)
FEATURE_ID (optional)
CONTRAST_ (optional), used for autoFlow().
VARIABLE_ (optional), not supported yet.

This function is used during the creation of a new object via new() to validate the supplied metadata via a filepath or existing data.table or data.frame.

Usage

omics$validate()

Returns

None

Method `removeZeros()`

Removes empty (zero) values by row and column from the countData. This method is performed automatically during subsetting of the object.

Usage

omics$removeZeros()

Returns

object in place

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")

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

obj$removeZeros()

Method `removeNAs()`

Remove NAs from metaData and updates the countData.

Usage

omics$removeNAs(column)

Arguments

column: The column from where NAs should be removed, this can be either a wholenumbers or characters. Vectors are also supported.

Returns

object in place

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

Method `feature_subset()`

Feature subset (based on featureData), automatically applies removeZeros().

Usage

omics$feature_subset(...)

Arguments

...: Expressions that return a logical value, and are defined in terms of the variables in featureData. Only rows for which all conditions evaluate to TRUE are kept.

Returns

object in place

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

Method `sample_subset()`

Sample subset (based on metaData), automatically applies removeZeros().

Usage

omics$sample_subset(...)

Arguments

...: Expressions that return a logical value, and are defined in terms of the variables in metaData. Only rows for which all conditions evaluate to TRUE are kept.

Returns

object in place

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

Method `samplepair_subset()`

Samplepair subset (based on metaData), automatically applies removeZeros().

Usage

omics$samplepair_subset(num_unique_pairs = NULL)

Arguments

num_unique_pairs: An integer value to define the number of pairs to subset. The default is NULL, meaning the maximum number of unique pairs will be used to subset the data. Let's say you have three samples for each pair, then the num_unique_pairs will be set to 3.

Returns

object in place

Method `feature_merge()`

Agglomerates features by column, automatically applies removeZeros().

Usage

omics$feature_merge(feature_rank, feature_filter = NULL)

Arguments

feature_rank: A character value or vector of columns to aggregate from the featureData.

feature_filter

A character value or vector of characters to remove features via regex pattern.

Returns

object in place

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")
obj <- metagenomics$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file,
)
obj$feature_merge(feature_rank = c("Kingdom", "Phylum"))
obj$feature_merge(feature_rank = "Genus", feature_filter = c("uncultured", "metagenome"))

Method `transform()`

Performs transformation on the positive values from the countData.

Usage

omics$transform(FUN)

Arguments

FUN: A function such as log2, log

Returns

object in place

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")
obj <- metagenomics$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file,
)
obj$transform(log2)

Method `normalize()`

Relative abundance computation by column sums on the countData.

Usage

omics$normalize()

Returns

object in place

Examples

library("OmicFlow")

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

obj$normalize()

Method `rankstat()`

Rank statistics based on featureData

Usage

omics$rankstat(feature_ranks, unique = FALSE)

Arguments

feature_ranks: A vector of characters or integers that match the featureData.

unique

A boolean value to display only unique entries in feature_ranks.

Details

Counts the number of features identified for each column, for example in case of 16S metagenomics it would be the number of OTUs or ASVs on different taxonomy levels.

Returns

A ggplot object.

Examples

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

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

plt <- obj$rankstat(feature_ranks = c("Kingdom", "Phylum", "Family", "Genus", "Species")) plt

Method `alpha_diversity()`

Alpha diversity based on diversity

Usage

omics$alpha_diversity(
  col_name,
  metric = c("shannon", "invsimpson", "simpson"),
  Brewer.palID = "Set2",
  evenness = FALSE,
  paired = FALSE,
  p.adjust.method = "fdr"
)

Arguments

col_name: A character variable from the metaData.

metric

An alpha diversity metric as input to diversity.

Brewer.palID

A character name for the palette set to be applied, see brewer.pal or colormap.

evenness

A boolean wether to divide diversity by number of species, see specnumber.

paired

A boolean value to perform paired analysis in wilcox.test and samplepair subsetting via samplepair_subset()

p.adjust.method

A character variable to specify the p.adjust.method to be used, default is 'fdr'.

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$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file,
)
plt <- obj$alpha_diversity(col_name = "treatment",
                           metric = "shannon")

Method `composition()`

Creates a table most abundant compositional features. Also assigns a color blind friendly palette for visualizations.

Usage

omics$composition(
  feature_rank,
  feature_filter = NULL,
  col_name = NULL,
  normalize = TRUE,
  feature_top = c(10, 15),
  Brewer.palID = "RdYlBu"
)

Arguments

feature_rank: A character variable in featureData to aggregate via feature_merge().

feature_filter

A character or vector of characters to removes features by regex pattern.

col_name

Optional, a character or vector of characters to add to the final compositional data output.

normalize

A boolean value, whether to normalize() by total sample sums (Default: TRUE).

feature_top

A wholenumber of the top features to visualize, the max is 15, due to a limit of palettes.

Brewer.palID

A character name for the palette set to be applied, see brewer.pal or colormap.

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$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file,
)
result <- obj$composition(feature_rank = "Genus",
                          feature_filter = c("uncultured"),
                          feature_top = 10)
plt <- composition_plot(data = result$data,
                        palette = result$palette,
                        feature_rank = "Genus")

Method `distance()`

Compute a distance metric from countData

Usage

omics$distance(metric, normalized = TRUE, weighted = TRUE, threads = 1)

Arguments

metric: A dissimilarity metric to be applied on the countData, thus far supports 'bray', 'jaccard', 'cosine', 'manhattan', 'jsd' (jensen-shannon divergence), 'canberra' and 'unifrac' when a tree is provided via treeData, see distance().

normalized

A boolean value, whether to normalize() by total sample sums (Default: TRUE).

weighted

A boolean value, to use abundances (weighted = TRUE) or absence/presence (weighted=FALSE) (default: TRUE).

threads

A wholenumber, indicating the number of threads to use (Default: 1).

Returns

A column x column dist object.

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")
obj <- metagenomics$new(
    metaData = metadata_file,
    countData = counts_file,
    featureData = features_file
)
obj$feature_subset(Kingdom == "Bacteria")
dist <- obj$distance(metric = "bray")

Method `ordination()`

Ordination of countData with statistical testing.

Usage

omics$ordination(
  metric = "bray",
  method = c("pcoa", "nmds"),
  group_by,
  distmat = NULL,
  weighted = TRUE,
  normalize = TRUE,
  threads = 1,
  perm_design = NULL,
  perm = 999
)

Arguments

metric: A dissimilarity or similarity metric to be applied on the countData, thus far supports 'bray', 'jaccard', 'cosine', 'manhattan', 'jsd' (jensen-shannon divergence), 'canberra' and 'unifrac' when a tree is provided via treeData, see distance().

method

Ordination method, supports "pcoa" and "nmds", see wcmdscale.

group_by

A character variable in metaData to be used for the pairwise_adonis or pairwise_anosim statistical test.

distmat

A custom distance matrix in either dist or Matrix format.

weighted

A boolean value, whether to compute weighted or unweighted dissimilarities (Default: TRUE).

normalize

A boolean value, whether to normalize() by total sample sums (Default: TRUE).

threads

A wholenumber, indicating the number of threads to use (Default: 1).

perm_design

A function that takes metaData and constructs a permutation design with how (default: NULL).

perm

A wholenumber, number of permutations to compare against the null hypothesis of adonis2 and anosim (default: perm=999).

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$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file,
)
pcoa_plots <- obj$ordination(metric = "bray",
                             method = "pcoa",
                             group_by = "treatment",
                             weighted = TRUE,
                             normalize = TRUE)
pcoa_plots

Method `DFE()`

Differential feature expression (DFE) using the foldchange for both paired and non-paired test.

Usage

omics$DFE(
  feature_rank,
  feature_filter = NULL,
  paired = FALSE,
  normalize = TRUE,
  condition.group,
  condition_A,
  condition_B,
  pvalue.threshold = 0.05,
  foldchange.threshold = 0.06,
  abundance.threshold = 0
)

Arguments

feature_rank: A character or vector of characters in the featureData to aggregate via feature_merge().

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, whether to normalize() by total sample sums (Default: TRUE).

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.

pvalue.threshold

A numeric value used as a p-value threshold to label and color significant features (Default: 0.05).

foldchange.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 relative abundance (default: 0.01).

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$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file,
)
unpaired <- obj$DFE(feature_rank = "Genus",
                    paired = FALSE,
                    condition.group = "treatment",
                    condition_A = c("healthy"),
                    condition_B = c("tumor"))

Method `autoFlow()`

Automated Omics Analysis based on the metaData, see validate(). For now only works with headers that start with prefix CONTRAST_.

Usage

omics$autoFlow(
  feature_ranks = c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species"),
  feature_contrast = c("Phylum", "Family", "Genus"),
  feature_filter = c("uncultured"),
  distance_metrics = c("unifrac"),
  beta_div_table = NULL,
  alpha_div_table = NULL,
  normalize = TRUE,
  weighted = TRUE,
  pvalue.threshold = 0.05,
  perm = 999,
  threads = 1,
  filename = paste0(getwd(), "/report.html")
)

Arguments

feature_ranks: A character vector as input to rankstat().

feature_contrast

A character vector of feature columns in the featureData to aggregate via feature_merge().

feature_filter

A character vector to filter unwanted features, default: c("uncultured")

distance_metrics

A character vector specifying what (dis)similarity metrics to use, default c("unifrac")

beta_div_table

A path to pre-computed distance matrix, expects tsv/csv/txt file.

alpha_div_table

A path to pre-computed alpha diversity with rarefraction depth, expects tsv/csv/txt from qiime2, see read_rarefraction_qiime.

normalize

A boolean value, whether to normalize() by total sample sums (Default: TRUE).

weighted

A boolean value, whether to compute weighted or unweighted dissimilarities (Default: TRUE).

pvalue.threshold

A numeric value, the p-value is used to include/exclude composition and foldchanges plots coming from alpha- and beta diversity analysis (Default: 0.05).

perm

A wholenumber, number of permutations to compare against the null hypothesis of adonis2 or anosim (default: perm=999).

threads

Number of threads to use, only used in ordination() when beta_div_table is not supplied (default: 1).

filename

A character to name the HTML report, it can also be a filepath (e.g. "/path/to/report.html"). Default: "report.html" in your current work directory.

Returns

A report in HTML format

Details

Every class is created with the R6Class method. Methods are either public or private, and only the public components are inherited by other omic classes. The omics class by default uses a sparseMatrix and data.table data structures for quick and efficient data manipulation and returns the object by reference, same as the R6 class. The method by reference is very efficient when dealing with big data.

Examples

Run this code


## ------------------------------------------------
## Method `omics$removeZeros`
## ------------------------------------------------

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,
)

obj$removeZeros()


## ------------------------------------------------
## Method `omics$removeNAs`
## ------------------------------------------------

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,
)

obj$removeNAs(column = "treatment")


## ------------------------------------------------
## Method `omics$feature_subset`
## ------------------------------------------------

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,
)

obj$feature_subset(Genus == "Streptococcus")


## ------------------------------------------------
## Method `omics$sample_subset`
## ------------------------------------------------

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,
)

obj$sample_subset(treatment == "tumor")


## ------------------------------------------------
## Method `omics$feature_merge`
## ------------------------------------------------

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,
)

obj$feature_merge(feature_rank = c("Kingdom", "Phylum"))
obj$feature_merge(feature_rank = "Genus", feature_filter = c("uncultured", "metagenome"))


## ------------------------------------------------
## Method `omics$transform`
## ------------------------------------------------

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,
)

obj$transform(log2)


## ------------------------------------------------
## Method `omics$normalize`
## ------------------------------------------------

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,
)

obj$normalize()


## ------------------------------------------------
## Method `omics$rankstat`
## ------------------------------------------------

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,
)

plt <- obj$rankstat(feature_ranks = c("Kingdom", "Phylum", "Family", "Genus", "Species"))
plt

## ------------------------------------------------
## Method `omics$alpha_diversity`
## ------------------------------------------------

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,
)

plt <- obj$alpha_diversity(col_name = "treatment",
                           metric = "shannon")


## ------------------------------------------------
## Method `omics$composition`
## ------------------------------------------------

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,
)

result <- obj$composition(feature_rank = "Genus",
                          feature_filter = c("uncultured"),
                          feature_top = 10)

plt <- composition_plot(data = result$data,
                        palette = result$palette,
                        feature_rank = "Genus")


## ------------------------------------------------
## Method `omics$distance`
## ------------------------------------------------

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
)

obj$feature_subset(Kingdom == "Bacteria")
dist <- obj$distance(metric = "bray")

## ------------------------------------------------
## Method `omics$ordination`
## ------------------------------------------------

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,
)

pcoa_plots <- obj$ordination(metric = "bray",
                             method = "pcoa",
                             group_by = "treatment",
                             weighted = TRUE,
                             normalize = TRUE)
pcoa_plots


## ------------------------------------------------
## Method `omics$DFE`
## ------------------------------------------------

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,
)

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

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Description

Arguments

Value

Public fields

Methods

Public methods

Method new()

Usage

Arguments

Returns

Method validate()

Usage

Returns

Method removeZeros()

Usage

Returns

Examples

Method removeNAs()

Usage

Arguments

Returns

Examples

Method feature_subset()

Usage

Arguments

Returns

Examples

Method sample_subset()

Usage

Arguments

Returns

Examples

Method samplepair_subset()

Usage

Arguments

Returns

Method feature_merge()

Usage

Arguments

Returns

Examples

Method transform()

Usage

Arguments

Returns

Examples

Method normalize()

Usage

Returns

Examples

Method rankstat()

Usage

Arguments

Details

Returns

Examples

Method alpha_diversity()

Usage

Arguments

Examples

Method composition()

Usage

Arguments

Examples

Method distance()

Usage

Arguments

Returns

Examples

Method ordination()

Usage

Arguments

Examples

Method DFE()

Usage

Arguments

Examples

Method autoFlow()

Usage

Arguments

Method `new()`

Method `validate()`

Method `removeZeros()`

Method `removeNAs()`

Method `feature_subset()`

Method `sample_subset()`

Method `samplepair_subset()`

Method `feature_merge()`

Method `transform()`

Method `normalize()`

Method `rankstat()`

Method `alpha_diversity()`

Method `composition()`

Method `distance()`

Method `ordination()`

Method `DFE()`

Method `autoFlow()`