Abstract omics class

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

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.

References

Aitchison, J. (1986) The Statistical Analysis of Compositional Data. Chapman and Hall, London, 416 p.

Active bindings

metaData: A data.table with SAMPLE_ID column.
featureData: A data.table with FEATURE_ID column.
countData: A dense or sparse Matrix.

Methods

`omics$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 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 or a dense/sparse Matrix format.
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.

`omics$copy()`

Create a copy of the object-class

This method is very similar to the existing clone() function, except it also resets the back-up of the OmicFlow data types that is invoked with reset()

Usage

omics$copy(deep = FALSE)

Arguments

deep: A boolean value to create a shallow or deep copy.

Returns

A copy of omics object

Examples

library("OmicFlow")

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

obj <- omics$new(
 metaData = metadata_file,
 countData = counts_file
)

# Perform a modification and copy
obj$scale()

cloned <- obj$copy(deep=TRUE)
cloned$scale(method = "clr")
cloned$reset() # resets to data after clone creation.

`omics$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)
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

`omics$print()`

Displays parameters of the omics class via stdout.

Usage

omics$print()

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")

obj <- omics$new(
 metaData = metadata_file,
 countData = counts_file
)

# method 1 to call print function
obj

# method 2 to call print function
obj$print()

`omics$reset()`

Upon creation of a new omics object a small backup of the original data is created. Since modification of the object is done by reference and duplicates are not made, it is possible to reset changes to the class. The methods from the abstract class omics also contains a private method to prevent any changes to the original object when using methods such as ordination alpha_diversity or foldchange.

Usage

omics$reset()

Returns

object in place

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")

taxa <- omics$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file
)

# Performs modifications
taxa$scale(transform = log2)

# resets
taxa$reset()

# An inbuilt reset function prevents unwanted modification to the taxa object.
taxa$rankstat(feature_ranks = c("Kingdom", "Phylum", "Family", "Genus", "Species"))

`omics$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")

`omics$feature_subset()`

Feature subset (based on featureData), automatically applies data synchronization.

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 == "Pseudomonas")

`omics$sample_subset()`

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

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")

`omics$samplepair_subset()`

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

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

`omics$feature_merge()`

Agglomerates features by column, automatically applies synchronization.

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"))

`omics$scale()`

Feature scaling on the countData. The scale function is able to apply transformations element-wise on the positive values, (optional: add pseudocounts) and perform normalisation or standardisation methods.

Usage

omics$scale(
  method = "tss",
  transform = NULL,
  base = exp(1),
  pseudocount = NULL
)

Arguments

method: A character to choose a standardisation/normalisation method, options: tss, clr, binary, hellinger, none (default: "tss").
transform: A function to apply on the positive values of countData, skip standardisation/normalisation with method = "none" (default: NULL).
base: Input for log to use natural logarithmic scale, log2, log10 or other (default: exp(1)) in CLR.
pseudocount: A numeric value to replace zero's (default: NULL).

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$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file,
)
# standard relative abundance computation
obj$scale()

# CLR
obj$reset()
obj$scale(method = "clr")

# transform
obj$reset()
obj$scale(method = "none", transform = log2)

`omics$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")

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

`omics$alpha_diversity()`

Alpha diversity based on diversity

Usage

omics$alpha_diversity(
  col_name,
  metric = c("shannon", "invsimpson", "simpson"),
  Brewer.palID = "Set2",
  group_by = NULL,
  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.
group_by: A column name to perform grouped statistical test in diversity_plot (default: NULL).
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'.

Returns

A list of components:

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

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")

`omics$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,
  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.
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.

Returns

A list of components:

data A data.table of feature compositions.
palette A setNames palette from 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")

`omics$distance()`

Compute a distance metric from countData

Usage

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

Arguments

metric: A dissimilarity metric to be applied on the countData, thus far supports 'bray', 'jaccard', 'cosine', 'manhattan', 'aitchison', 'euclidean', 'jsd' (jensen-shannon divergence), 'canberra' and 'unifrac' when a tree is provided via treeData, see distance().
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).
normalized: A boolean value, whether to normalize weighted UniFrac distances to be between 0 and 1. Unweighted UniFrac is always normalized (default: TRUE).
base: Input for log to use natural logarithmic scale, log2, log10 or other (default: exp(1)).
pseudocount: A numeric value to replace zero's, used in scale() (default: 1e-15).

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")

`omics$ordination()`

Ordination of countData with statistical testing.

Usage

omics$ordination(
  metric = "bray",
  method = c("pcoa", "nmds"),
  group_by,
  distmat = NULL,
  weighted = 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).
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).

Returns

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.

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)
pcoa_plots

`omics$foldchange()`

Differential feature expression (DFE) on log-transformed values for both paired and non-paired test.

The function performs feature agglomeration, subsetting to remove NAs in condition.group and finding samplepairs. It expects that the data is already log-transformed, this can be accomplished via scale()

Usage

omics$foldchange(
  condition.group,
  condition_A,
  condition_B,
  group_by = NULL,
  feature_rank = "FEATURE_ID",
  feature_filter = NULL,
  paired = 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). When disabled then column names will end with _in_all.
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().
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 <- omics$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file
)
obj$scale(method = "clr")

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

`omics$autoFlow()`

Automated Omics Analysis based on the metaData, see validate(). For now only works with headers that start with prefix CONTRAST_. If the data is from the class omics or proteomics than FDR adjusted p-values are computed for the volcano plots. Log-transformed values will lead to the skipping of composition() and alpha_diversity() methods.

Usage

omics$autoFlow(
  feature_contrast = "FEATURE_ID",
  feature_filter = NULL,
  feature_ranks = NULL,
  distance_metrics = c("bray"),
  distmat = NULL,
  weighted = TRUE,
  pvalue.threshold = 0.05,
  logfold.threshold = 1,
  abundance.threshold = 0.01,
  perm = 999,
  threads = 1,
  report = TRUE,
  filename = paste0(getwd(), "/report.html")
)

Arguments

feature_contrast: A character vector of feature columns in the featureData to aggregate via feature_merge() (default: "FEATURE_ID").
feature_filter: A character vector to filter unwanted features, (default: NULL).
feature_ranks: A character vector as input to rankstat() (default: NULL).
distance_metrics: A character vector specifying what (dis)similarity metrics to use (default: c("bray")) When you are working with log-transformed data it is advised to use the euclidean.
distmat: A path to an existing file or a dense/sparse Matrix format (default: NULL).
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).
logfold.threshold: A numeric value used as a fold-change threshold to label and color significantly expressed features, see foldchange() (Default: 1).
abundance.threshold: A numeric value used as an abundance threshold to size the scatter dots based on their mean abundance, see foldchange() (default: 0.01).
perm: A wholenumber, number of permutations to compare against the null hypothesis of adonis2 or anosim (default: 999).
threads: Number of threads to use, only used in distance() when distmat is not supplied (default: 1).
report: A boolean value to create a HTML markdown report (default: FALSE). If FALSE a nested list of the plots and data is returned.
filename: A character to name the HTML report to be saved in the current working directory (default: paste0(getwd(), "/report.html")). The getwd() is required for rmarkdown to save it in the right path.

Returns

List of plots/data or rendered HTML report

`omics$clone()`

The objects of this class are cloneable with this method.

Usage

omics$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `omics$copy()`
## ------------------------------------------------

library("OmicFlow")

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

obj <- omics$new(
 metaData = metadata_file,
 countData = counts_file
)
#> ✔ metaData template passed the JSON validation.
#> ℹ Checking for duplicated identifiers ..
#> ✔ countData is loaded.
#> ! Created placeholder featureData.

# Perform a modification and copy
obj$scale()

cloned <- obj$copy(deep=TRUE)
cloned$scale(method = "clr")
cloned$reset() # resets to data after clone creation.


## ------------------------------------------------
## Method `omics$print()`
## ------------------------------------------------

library("OmicFlow")

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

obj <- omics$new(
 metaData = metadata_file,
 countData = counts_file
)
#> ✔ metaData template passed the JSON validation.
#> ℹ Checking for duplicated identifiers ..
#> ✔ countData is loaded.
#> ! Created placeholder featureData.

# method 1 to call print function
obj
#> 
#> ── <omics> object 
#> metaData: 9 variables × 4 samples
#> countData: 4 samples × 242 features
#> featureData: 0 attributes × 242 features

# method 2 to call print function
obj$print()
#> 
#> ── <omics> object 
#> metaData: 9 variables × 4 samples
#> countData: 4 samples × 242 features
#> featureData: 0 attributes × 242 features


## ------------------------------------------------
## Method `omics$reset()`
## ------------------------------------------------

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")

taxa <- omics$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.

# Performs modifications
taxa$scale(transform = log2)

# resets
taxa$reset()

# An inbuilt reset function prevents unwanted modification to the taxa object.
taxa$rankstat(feature_ranks = c("Kingdom", "Phylum", "Family", "Genus", "Species"))



## ------------------------------------------------
## 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,
)
#> ✔ 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

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,
)
#> ✔ 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

obj$feature_subset(Genus == "Pseudomonas")
#> 
#> ── <metagenomics> object 
#> metaData: 9 variables × 3 samples
#> countData: 3 samples × 4 features
#> featureData: 7 attributes × 4 features


## ------------------------------------------------
## 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,
)
#> ✔ 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

obj$sample_subset(treatment == "tumor")
#> 
#> ── <metagenomics> object 
#> metaData: 9 variables × 2 samples
#> countData: 2 samples × 115 features
#> featureData: 7 attributes × 115 features


## ------------------------------------------------
## 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,
)
#> ✔ 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

obj$feature_merge(feature_rank = c("Kingdom", "Phylum"))
#> 
#> ── <metagenomics> object 
#> metaData: 9 variables × 4 samples
#> countData: 4 samples × 28 features
#> featureData: 7 attributes × 28 features
obj$feature_merge(feature_rank = "Genus", feature_filter = c("uncultured", "metagenome"))
#> 
#> ── <metagenomics> object 
#> metaData: 9 variables × 4 samples
#> countData: 4 samples × 20 features
#> featureData: 7 attributes × 20 features


## ------------------------------------------------
## Method `omics$scale()`
## ------------------------------------------------

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

# CLR
obj$reset()
obj$scale(method = "clr")

# transform
obj$reset()
obj$scale(method = "none", transform = log2)


## ------------------------------------------------
## 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,
)
#> ✔ 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

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,
)
#> ✔ 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

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,
)
#> ✔ 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

result <- obj$composition(feature_rank = "Genus",
                          feature_filter = c("uncultured"),
                          feature_top = 10)
#> 
#> ── <metagenomics> object 
#> metaData: 9 variables × 4 samples
#> countData: 4 samples × 63 features
#> featureData: 7 attributes × 63 features

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
)
#> ✔ 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

obj$feature_subset(Kingdom == "Bacteria")
#> 
#> ── <metagenomics> object 
#> metaData: 9 variables × 4 samples
#> countData: 4 samples × 185 features
#> featureData: 7 attributes × 185 features
dist <- obj$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,
)
#> ✔ 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

pcoa_plots <- obj$ordination(metric = "bray",
                             method = "pcoa",
                             group_by = "treatment",
                             weighted = TRUE)
#> 'nperm' >= set of all permutations: complete enumeration.
#> Set of permutations < 'minperm'. Generating entire set.
pcoa_plots
#> $dist
#>           S100      S103      S115      S120
#> S100 0.0000000 1.0000000 0.8845188 1.0000000
#> S103 1.0000000 0.0000000 1.0000000 0.9470058
#> S115 0.8845188 1.0000000 0.0000000 1.0000000
#> S120 1.0000000 0.9470058 1.0000000 0.0000000
#> 
#> $anova_data
#>              pairs Df SumsOfSqs   F.Model        R2 p.value p.adj
#> 1 tumor vs healthy  1 0.4197984 0.8395968 0.2956747       1     1
#> 
#> $pcs
#>           PC1           PC2           PC3  groups samples
#>         <num>         <num>         <num>  <char>  <char>
#> 1:  0.3808548  0.000000e+00  4.422594e-01   tumor       1
#> 2: -0.3808548 -4.735029e-01 -2.256762e-16   tumor       2
#> 3:  0.3808548 -2.787913e-17 -4.422594e-01 healthy       3
#> 4: -0.3808548  4.735029e-01 -2.603956e-16 healthy       4
#> 
#> $scree_plot
#> Warning: Removed 7 rows containing missing values or values outside the scale range
#> (`geom_col()`).

#> 
#> $anova_plot

#> 
#> $scores_plot
#> Too few points to calculate an ellipse
#> Too few points to calculate an ellipse
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_path()`).

#> 


## ------------------------------------------------
## Method `omics$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 <- omics$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file
)
#> ✔ metaData template passed the JSON validation.
#> ℹ Checking for duplicated identifiers ..
#> ✔ featureData is loaded.
#> ✔ countData is loaded.
obj$scale(method = "clr")

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

Details

References

See also

Active bindings

Methods

Public methods

omics$new()

Usage

Arguments

Returns

omics$copy()

Usage

Arguments

Returns

Examples

omics$validate()

Usage

Returns

omics$print()

Usage

Returns

Examples

omics$reset()

Usage

Returns

Examples

omics$removeNAs()

Usage

Arguments

Returns

Examples

omics$feature_subset()

Usage

Arguments

Returns

Examples

omics$sample_subset()

Usage

Arguments

Returns

Examples

omics$samplepair_subset()

Usage

Arguments

Returns

omics$feature_merge()

Usage

Arguments

Returns

Examples

omics$scale()

Usage

Arguments

Returns

Examples

omics$rankstat()

Usage

Arguments

Details

Returns

Examples

omics$alpha_diversity()

Usage

Arguments

Returns

Examples

omics$composition()

Usage

Arguments

Returns

Examples

omics$distance()

Usage

Arguments

Returns

Examples

omics$ordination()

Usage

Arguments

`omics$new()`

`omics$copy()`

`omics$validate()`

`omics$print()`

`omics$reset()`

`omics$removeNAs()`

`omics$feature_subset()`

`omics$sample_subset()`

`omics$samplepair_subset()`

`omics$feature_merge()`

`omics$scale()`

`omics$rankstat()`

`omics$alpha_diversity()`

`omics$composition()`

`omics$distance()`

`omics$ordination()`

`omics$foldchange()`

`omics$autoFlow()`

`omics$clone()`