Title:	Fast and Efficient (Automated) Analysis of Sparse Omics Data
Version:	1.3.0
Date:	2025-08-27
Description:	A generalised data structure for fast and efficient loading and data munching of sparse omics data. The 'OmicFlow' requires an up-front validated metadata template from the user, which serves as a guide to connect all the pieces together by aligning them into a single object that is defined as an 'omics' class. Once this unified structure is established, users can perform manual subsetting, visualisation, and statistical analysis, or leverage the automated 'autoFlow' method to generate a comprehensive report.
License:	MIT + file LICENSE
URL:	https://github.com/agusinac/OmicFlow
BugReports:	https://github.com/agusinac/OmicFlow/issues
Config/testthat/edition:	3
Encoding:	UTF-8
RoxygenNote:	7.3.2
Depends:	R (≥ 4.3.0), R6, data.table, Matrix
Imports:	ape, gghalves, ggpubr, ggrepel, jsonlite, jsonvalidate, magrittr, patchwork, purrr, rbiom, RColorBrewer, rhdf5, rstatix, slam, stats, tools, utils, vegan, viridis, yyjsonr, methods, ggplot2
Suggests:	DT, downloadthis, rmarkdown, cli, testthat (≥ 3.0.0)
NeedsCompilation:	no
Packaged:	2025-08-27 12:09:57 UTC; Z289224
Author:	Alem Gusinac [aut, cre], Thomas Ederveen [aut], Annemarie Boleij [aut, fnd]
Maintainer:	Alem Gusinac <alem.gusinac@gmail.com>
Repository:	CRAN
Date/Publication:	2025-09-01 17:20:19 UTC

OmicFlow: Fast and Efficient (Automated) Analysis of Sparse Omics Data

Description

A generalised data structure for fast and efficient loading and data munching of sparse omics data. The 'OmicFlow' requires an up-front validated metadata template from the user, which serves as a guide to connect all the pieces together by aligning them into a single object that is defined as an 'omics' class. Once this unified structure is established, users can perform manual subsetting, visualisation, and statistical analysis, or leverage the automated 'autoFlow' method to generate a comprehensive report.

Author(s)

Maintainer: Alem Gusinac alem.gusinac@gmail.com (ORCID)

Authors:

• Thomas Ederveen (ORCID)

• Annemarie Boleij (ORCID) [funder]

See Also

Useful links:

• https://github.com/agusinac/OmicFlow

• Report bugs at https://github.com/agusinac/OmicFlow/issues

Color map of a variable

Description

Creates an object of hexcode colors with names given a vector of characters. This function is built into the ordination method from the abstract class omics and inherited by other omics classes, such as; metagenomics and proteomics.

Usage

colormap(data, col_name, Brewer.palID = "Set2")

Arguments

Value

A setNames.

Examples

library("data.table")
dt <- data.table(
  "SAMPLE_ID" = c("sample_1", "sample_2", "sample_3"),
  "treatment" = c("healthy", "tumor", NA)
)

colors <- colormap(data = dt,
                   col_name = "treatment")

Checks if column exists in table

Description

Mainly used within omics and other functions to check if given column name does exist in the table and is not completely empty (containing NAs).

Usage

column_exists(column, table)

Arguments

Value

A boolean value.

Compositional plot

Description

Creates a stacked barchart of features. It is possible to both show barcharts for each sample or group them by a categorical variable. The function is compatible with the class omics method composition().

Usage

composition_plot(
  data,
  palette,
  feature_rank,
  title_name = NULL,
  group_by = NULL
)

Arguments

Value

A ggplot2 object to be further modified

Examples

library("ggplot2")

# Create mock_data as data.frame (data.table is also supported)
mock_data <- data.frame(
  SAMPLE_ID = rep(paste0("Sample", 1:10), each = 5),
  Genus = rep(c("GenusA","GenusB","GenusC","GenusD","GenusE"), times = 10),
  value = c(
    0.1119, 0.1303, 0.0680, 0.5833, 0.1065,      # Sample1
    0.2080, 0.1179, 0.0211, 0.4578, 0.1951,      # Sample2
    0.4219, 0.1189, 0.2320, 0.1037, 0.1235,      # Sample3
    0.4026, 0.0898, 0.1703, 0.1063, 0.2309,      # Sample4
    0.1211, 0.0478, 0.5721, 0.1973, 0.0618,      # Sample5
    0.2355, 0.0293, 0.2304, 0.1520, 0.3528,      # Sample6
    0.2904, 0.0347, 0.3651, 0.0555, 0.2544,      # Sample7
    0.4138, 0.0299, 0.0223, 0.4996, 0.0345,      # Sample8
    0.4088, 0.0573, 0.0155, 0.2888, 0.2296,      # Sample9
    0.4941, 0.0722, 0.2331, 0.1023, 0.0983       # Sample10
  ),
  Group = rep(c("Group1","Group2","Group1",
                "Group1","Group2","Group2",
                 "Group1","Group1","Group1","Group2"),
               each = 5)
)

# Create a colormap
mock_palette <- c(
  GenusA = "#1f77b4",  # blue
  GenusB = "#ff7f0e",  # orange
  GenusC = "#2ca02c",  # green
  GenusD = "#d62728",  # red
  GenusE = "#9467bd"   # purple
)

# Optionally: Use OmicFlow::colormap()
mock_palette <- colormap(
  data = mock_data,
  col_name = "Genus",
  Brewer.palID = "RdYlBu"
)

composition_plot(
  data = mock_data,
  palette = mock_palette,
  feature_rank = "Genus",
  title_name = "Mock Genus Composition"
)

composition_plot(
  data = mock_data,
  palette = mock_palette,
  feature_rank = "Genus",
  title_name = "Mock Genus Composition by Group",
  group_by = "Group"
)

Sparse implementation of Alpha Diversity Metrics

Description

Computes the alpha diversity based on Shannon index, simpson or invsimpson. Code is adapted from diversity and uses sparseMatrix in triplet format over the dense matrix. The code is much faster and memory efficient, while still being mathematical correct. This function is built into the class omics with method alpha_diversity() and inherited by other omics classes, such as; metagenomics and proteomics.

Usage

diversity(
  x,
  metric = c("shannon", "simpson", "invsimpson"),
  normalize = TRUE,
  base = exp(1)
)

Arguments

Value

A numeric vector with type double.

See Also

diversity

Examples

library("Matrix")

n_row <- 1000
n_col <- 100
density <- 0.2
num_entries <- n_row * n_col
num_nonzero <- round(num_entries * density)

set.seed(123)
positions <- sample(num_entries, num_nonzero, replace=FALSE)
row_idx <- ((positions - 1) %% n_row) + 1
col_idx <- ((positions - 1) %/% n_row) + 1

values <- runif(num_nonzero, min = 0, max = 1)
sparse_mat <- sparseMatrix(
  i = row_idx,
  j = col_idx,
  x = values,
  dims = c(n_row, n_col)
)

# Alpha diversity is computed on column level
## Transpose the sparseMatrix if required with t() from Matrix R package.
result <- OmicFlow::diversity(
  x = sparse_mat,
  metric = "shannon"
) 

Diversity plot

Description

Creates an Alpha diversity plot. This function is built into the class omics with method alpha_diversity(). It computes the pairwise wilcox test, paired or non-paired, given a data frame and adds useful labelling.

Usage

diversity_plot(
  data,
  values,
  col_name,
  palette,
  method,
  paired = FALSE,
  p.adjust.method = "fdr"
)

Arguments

Value

A ggplot2 object to be further modified

Examples

library("ggplot2")
 
n_row <- 1000
n_col <- 100
density <- 0.2
num_entries <- n_row * n_col
num_nonzero <- round(num_entries * density)

set.seed(123)
positions <- sample(num_entries, num_nonzero, replace=FALSE)
row_idx <- ((positions - 1) %% n_row) + 1
col_idx <- ((positions - 1) %/% n_row) + 1

values <- runif(num_nonzero, min = 0, max = 1)
sparse_mat <- Matrix::sparseMatrix(
   i = row_idx,
   j = col_idx,
   x = values,
   dims = c(n_row, n_col)
 )

div <- OmicFlow::diversity(
  x = sparse_mat,
  metric = "shannon"
)

dt <- data.table::data.table(
  "values" = div,
  "treatment" = c(rep("healthy", n_col / 2), rep("tumor", n_col / 2))
)

colors <- OmicFlow::colormap(dt, "treatment")

diversity_plot(
 data = dt,
 values = "values",
 col_name = "treatment",
 palette = colors,
 method = "shannon",
 paired = FALSE,
 p.adjust.method = "fdr"
)

Computes Log2(A) - Log2(B) Fold Change of (non-) paired data.

Description

Computes (non-)paired Log2(A) - Log2(B) Fold Change. This function is built into the class omics with method DFE() and inherited by other omics classes, such as; metagenomics and proteomics. The function handles zero's, and doesn't return +/- infinites.

Usage

foldchange(
  data,
  feature_rank,
  condition_A,
  condition_B,
  condition_labels,
  paired = FALSE
)

Arguments

Value

A data.table

Examples

#-------------------------#
##      NON-PAIRED       ##
#-------------------------#
# Load required library
library(data.table)

# Define parameters and variables
sample_ids <- c("S1_A", "S2_A", "S3_A", "S4_B", "S5_B", "S6_B")
feature_ids <- c("Feature1", "Feature2", "Feature3")

# Simulated abundance matrix (features x samples)
abundances <- matrix(
  c(
    # Feature1 (e.g. GenusA)
    100, 120, 110,  55, 60, 65,
    # Feature2 (e.g. GenusB)
    50, 65, 60,    130, 120, 125,
    # Feature3 (e.g. GenusC)
    80, 85, 90,     80, 85, 90
  ),
  nrow = 3, byrow = TRUE,
  dimnames = list(feature_ids, sample_ids)
)

# A wide table with columns as samples, rows as features
# And an additional column as the feature_rank, a column for feature comparison.
mock_data <- data.table(
  Genus = feature_ids,  # feature_rank column (e.g. "Genus")
  S1_A = abundances[ , 1],
  S2_A = abundances[ , 2],
  S3_A = abundances[ , 3],
  S4_B = abundances[ , 4],
  S5_B = abundances[ , 5],
  S6_B = abundances[ , 6]
)
print(mock_data)

# It uses substring matching, and multiple conditions can be used
res <- foldchange(
  data = mock_data,
  feature_rank = "Genus",
  condition_A = c("_A", "_B"),
  condition_B = c("_B", "_A"),

  # This can also be a column wherein, conditions A and B are present
  condition_labels = sample_ids, 
  paired = FALSE
)
print(res)

#---------------------#
##      PAIRED       ##
#---------------------#
library(data.table)

# Define paired sample ids for 3 pairs:
paired_ids <- paste0("Pair", 1:3)

# Features:
feature_ids <- c("Feature1", "Feature2", "Feature3")

# Simulate abundances for each paired sample:
# For each pair, we have two samples: condition A and condition B.
# Make sure the length of condition A and condition B are the same!

# Construct the data.table with features as rows
mock_data_paired <- data.table(
  Genus = feature_ids,
  Pair1_A = c(100, 50, 80),
  Pair1_B = c(60, 130, 75),
  Pair2_A = c(120, 65, 85),
  Pair2_B = c(60, 120, 90),
  Pair3_A = c(110, 60, 90),
  Pair3_B = c(65, 125, 85) 
)

res <- foldchange(
  data = mock_data_paired,
  feature_rank = "Genus",
  condition_A = c("_A", "_B"),
  condition_B = c("_B", "_A"),

  # This can also be a column wherein, conditions A and B are present
  condition_labels = names(mock_data_paired)[-1], 
  paired = TRUE
)
print(res)

Sparse implementation of Hill numbers

Description

Computes the hill numbers for q is 0, 1 or 2. Code is adapted from hill_taxa and uses sparseMatrix in triplet format over the dense matrix. The code is much faster and memory efficient, while still being mathematical correct.

Usage

hill_taxa(x, q = 0, normalize = TRUE, base = exp(1))

Arguments

Value

A numeric vector with type double.

See Also

hill_taxa

Examples

library("Matrix")

n_row <- 1000
n_col <- 100
density <- 0.2
num_entries <- n_row * n_col
num_nonzero <- round(num_entries * density)

set.seed(123)
positions <- sample(num_entries, num_nonzero, replace=FALSE)
row_idx <- ((positions - 1) %% n_row) + 1
col_idx <- ((positions - 1) %/% n_row) + 1

values <- runif(num_nonzero, min = 0, max = 1)
sparse_mat <- sparseMatrix(
  i = row_idx,
  j = col_idx,
  x = values,
  dims = c(n_row, n_col)
)

result <- OmicFlow::hill_taxa(
 x = sparse_mat,
 q = 2
)

Sub-class metagenomics

Description

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

OmicFlow::omics -> metagenomics

Public fields

Methods

Public methods

• metagenomics$new()

• metagenomics$print()

• metagenomics$reset()

• metagenomics$removeZeros()

• metagenomics$write_biom()

Method 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

Returns

A new metagenomics object.

Method print()

Displays parameters of the metagenomics object via stdout.

Usage

metagenomics$print()

Returns

object in place

Examples

taxa_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
taxa <- readRDS(taxa_path)

# method 1 to call print function
taxa

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

Method reset()

Upon creation of a new metagenomics 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 $DFE.

Usage

metagenomics$reset()

Returns

object in place

Examples

library(ggplot2)

taxa_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
taxa <- readRDS(taxa_path)

# Performs modifications
taxa$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 removeZeros()

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

Usage

metagenomics$removeZeros()

Returns

object in place

Examples

taxa_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
taxa <- readRDS(taxa_path)

# Sample subset induces empty features
taxa$sample_subset(treatment == "tumor")

# Remove empty features from countData and treeData
taxa$removeZeros()

Method 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

Examples

taxa_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
taxa <- readRDS(taxa_path)

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

See Also

omics

Examples

## ------------------------------------------------
## Method `metagenomics$print`
## ------------------------------------------------

taxa_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
taxa <- readRDS(taxa_path)

# method 1 to call print function
taxa

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


## ------------------------------------------------
## Method `metagenomics$reset`
## ------------------------------------------------

library(ggplot2)

taxa_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
taxa <- readRDS(taxa_path)

# Performs modifications
taxa$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 `metagenomics$removeZeros`
## ------------------------------------------------

taxa_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
taxa <- readRDS(taxa_path)

# Sample subset induces empty features
taxa$sample_subset(treatment == "tumor")

# Remove empty features from countData and treeData
taxa$removeZeros()


## ------------------------------------------------
## Method `metagenomics$write_biom`
## ------------------------------------------------

taxa_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
taxa <- readRDS(taxa_path)

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

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.

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.

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

Methods

Public methods

• omics$new()

• omics$validate()

• omics$removeZeros()

• omics$removeNAs()

• omics$feature_subset()

• omics$sample_subset()

• omics$samplepair_subset()

• omics$feature_merge()

• omics$transform()

• omics$normalize()

• omics$rankstat()

• omics$alpha_diversity()

• omics$composition()

• omics$ordination()

• omics$DFE()

• omics$autoFlow()

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 handles 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

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

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

obj$removeZeros()

Method removeNAs()

Remove NAs from metaData and updates the countData.

Usage

omics$removeNAs(column)

Arguments

Returns

object in place

Examples

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

obj$removeNAs(column = "treatment")

Method feature_subset()

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

Usage

omics$feature_subset(...)

Arguments

Returns

object in place

Examples

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

obj$feature_subset(Genus == "Streptococcus")

Method sample_subset()

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

Usage

omics$sample_subset(...)

Arguments

Returns

object in place

Examples

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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

Returns

object in place

Method feature_merge()

Agglomerates features by column, automatically applies removeZeros().

Usage

omics$feature_merge(feature_rank, feature_filter = NULL)

Arguments

Returns

object in place

Examples

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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

Returns

object in place

Examples

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

obj$transform(log2)

Method normalize()

Relative abundance computation by column sums on the countData.

Usage

omics$normalize()

Returns

object in place

Examples

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

obj$normalize()

Method rankstat()

Rank statistics based on featureData

Usage

omics$rankstat(feature_ranks)

Arguments

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)

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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

Examples

library(ggplot2)

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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

Examples

library(ggplot2)

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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 ordination()

Ordination of countData with statistical testing.

Usage

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

Arguments

Examples

library(ggplot2)

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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

Examples

library(ggplot2)

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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,
  cpus = 1,
  filename = paste0(getwd(), "report.html")
)

Arguments

Returns

A report in HTML format

See Also

diversity_plot

composition_plot

ordination_plot, plot_pairwise_stats, pairwise_anosim, pairwise_adonis

volcano_plot, foldchange

Examples

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

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

obj$removeZeros()


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

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

obj$removeNAs(column = "treatment")


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

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

obj$feature_subset(Genus == "Streptococcus")


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

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

obj$sample_subset(treatment == "tumor")


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

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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


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

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

obj$transform(log2)


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

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

obj$normalize()


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

library(ggplot2)

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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

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

library(ggplot2)

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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


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

library(ggplot2)

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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$ordination`
## ------------------------------------------------

library(ggplot2)

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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


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

library(ggplot2)

obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)

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

Ordination plot

Description

Creates an ordination plot pre-computed principal components from wcmdscale. This function is built into the class omics with method ordination() and inherited by other omics classes, such as; metagenomics and proteomics.

Usage

ordination_plot(
  data,
  col_name,
  pair,
  dist_explained = NULL,
  dist_metric = NULL
)

Arguments

Value

A ggplot2 object to be further modified

Examples

library(ggplot2)

# Mock principal component scores
set.seed(123)
mock_data <- data.frame(
  SampleID = paste0("Sample", 1:10),
  PC1 = rnorm(10, mean = 0, sd = 1),
  PC2 = rnorm(10, mean = 0, sd = 1),
  groups = rep(c("Group1", "Group2"), each = 5)
)

# Basic usage
ordination_plot(
  data = mock_data,
  col_name = "groups",
  pair = c("PC1", "PC2")
)

# Adding variance/dissimilarity explained.
ordination_plot(
  data = mock_data,
  col_name = "groups",
  pair = c("PC1", "PC2"),
  dist_explained = c(45, 22),
  dist_metric = "bray-curtis"
)

Pairwise adonis2 (PERMANOVA) computation

Description

Computes pairwise adonis2, given a distance matrix and a vector of labels. This function is built into the class omics with method ordination() and inherited by other omics classes, such as; metagenomics and proteomics.

Usage

pairwise_adonis(x, groups, p.adjust.method = "bonferroni", perm = 999)

Arguments

Value

A data.frame of

• pairs that are used

• Degrees of freedom (Df)

• Sums of Squares of H_0

• F.Model of H_0

• R2 of H_0

• p value of F^p > F

• p adjusted

See Also

adonis2

Examples

# Create random data
set.seed(42)
mock_data <- matrix(rnorm(15 * 10), nrow = 15, ncol = 10)

# Create euclidean dissimilarity matrix
mock_dist <- dist(mock_data, method = "euclidean")

# Define group labels, should be equal to number of columns and rows to dist
mock_groups <- rep(c("A", "B", "C"), each = 5)

# Compute pairwise adonis (PERMANOVA)
result <- pairwise_adonis(x = mock_dist, 
                          groups = mock_groups, 
                          p.adjust.method = "bonferroni", 
                          perm = 99)

Pairwise anosim (ANOSIM) computation

Description

Computes pairwise anosim, given a distance matrix and a vector of labels. This function is built into the class omics with method ordination() and inherited by other omics classes, such as; metagenomics and proteomics.

Usage

pairwise_anosim(x, groups, p.adjust.method = "bonferroni", perm = 999)

Arguments

Value

A data.frame of

• pairs that are used

• R2 of H_0

• p value of F^p > F

• p adjusted

See Also

anosim

Examples

# Create random data
set.seed(42)
mock_data <- matrix(rnorm(15 * 10), nrow = 15, ncol = 10)

# Create euclidean dissimilarity matrix
mock_dist <- dist(mock_data, method = "euclidean")

# Define group labels, should be equal to number of columns and rows to dist
mock_groups <- rep(c("A", "B", "C"), each = 5)

# Compute pairwise anosim
result <- pairwise_anosim(x = mock_dist, 
                          groups = mock_groups, 
                          p.adjust.method = "bonferroni", 
                          perm = 99)

Create pairwise stats plot

Description

Creates a pairwise stats plot from pairwise_adonis or pairwise_anosim results. This function is built into the class omics with method ordination() and inherited by other omics classes, such as; metagenomics and proteomics.

Usage

plot_pairwise_stats(
  data,
  stats_col,
  group_col,
  label_col,
  y_axis_title = NULL,
  plot_title = NULL
)

Arguments

Value

A ggplot2 object to be further modified

Examples

library("ggplot2")

# Create random data
set.seed(42)
mock_data <- matrix(rnorm(15 * 10), nrow = 15, ncol = 10)

# Create euclidean dissimilarity matrix
mock_dist <- dist(mock_data, method = "euclidean")

# Define group labels, should be equal to number of columns and rows to dist
mock_groups <- rep(c("A", "B", "C"), each = 5)

# Compute pairwise adonis
adonis_res <- pairwise_adonis(x = mock_dist, 
                              groups = mock_groups, 
                              p.adjust.method = "bonferroni", 
                              perm = 99)
# Compute pairwise anosim
anosim_res <- pairwise_anosim(x = mock_dist, 
                              groups = mock_groups, 
                              p.adjust.method = "bonferroni", 
                              perm = 99)

# Visualize PERMANOVA pairwise stats
plot_pairwise_stats(data = adonis_res,
                    group_col = "pairs",
                    stats_col = "F.Model",
                    label_col = "p.adj",
                    y_axis_title = "Pseudo F test statistic",
                    plot_title = "PERMANOVA")

# Visualize ANOSIM pairwise stats
plot_pairwise_stats(data = anosim_res,
                    group_col = "pairs",
                    stats_col = "anosimR",
                    label_col = "p.adj",
                    y_axis_title = "ANOSIM R statistic",
                    plot_title = "ANOSIM")

Sub-class proteomics

Description

This is a sub-class that is compatible to preprocessed data obtained from https://fragpipe.nesvilab.org/. It inherits all methods from the abstract class omics and only adapts the initialize function. It supports pre-existing data structures or paths to 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

OmicFlow::omics -> proteomics

Public fields

Methods

Public methods

• proteomics$new()

• proteomics$print()

• proteomics$reset()

• proteomics$removeZeros()

Method new()

Initializes the proteomics class object with proteomics$new()

Usage

proteomics$new(countData = NA, metaData = NA, featureData = NA, treeData = NA)

Arguments

Returns

A new proteomics object.

Method print()

Displays parameters of the proteomics object via stdout.

Usage

proteomics$print()

Returns

object in place

Examples

prot_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
prot <- readRDS(prot_path)

# method 1 to call print function
prot

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

Method reset()

Upon creation of a new proteomics 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 $DFE.

Usage

proteomics$reset()

Returns

object in place

Examples

 
prot_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
prot <- readRDS(prot_path)

# Performs modifications
prot$transform(log2)

# resets
prot$reset()

Method removeZeros()

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

Usage

proteomics$removeZeros()

Returns

object in place

Examples

prot_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
prot <- readRDS(prot_path)

# Sample subset induces empty features
prot$sample_subset(treatment == "tumor")

# Remove empty features from countData and treeData
prot$removeZeros()

See Also

omics

Examples

## ------------------------------------------------
## Method `proteomics$print`
## ------------------------------------------------

prot_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
prot <- readRDS(prot_path)

# method 1 to call print function
prot

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


## ------------------------------------------------
## Method `proteomics$reset`
## ------------------------------------------------

 
prot_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
prot <- readRDS(prot_path)

# Performs modifications
prot$transform(log2)

# resets
prot$reset()


## ------------------------------------------------
## Method `proteomics$removeZeros`
## ------------------------------------------------

prot_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
prot <- readRDS(prot_path)

# Sample subset induces empty features
prot$sample_subset(treatment == "tumor")

# Remove empty features from countData and treeData
prot$removeZeros()

Loads a rarefied alpha diversity table from Qiime2

Description

Parses a QIIME2 table of rarefied data into a data.table as input to diversity_plot

Usage

read_rarefraction_qiime(filepath)

Arguments

Value

A data.table.

Converting a sparse matrix to data.table

Description

Wrapper function that converts a sparseMatrix to data.table

Usage

sparse_to_dtable(sparsemat)

Arguments

Value

A data.table class.

Volcano plot

Description

Creates a Volcano plot from the output of foldchange, it plots the foldchanges on the x-axis, log10 trasnformed p-values on the y-axis and adjusts the scatter size based on the percentage abundance of the features. This function is built into the class omics with method DFE() and inherited by other omics classes, such as; metagenomics and proteomics.

Usage

volcano_plot(
  data,
  logfold_col,
  pvalue_col,
  feature_rank,
  abundance_col,
  pvalue.threshold = 0.05,
  logfold.threshold = 0.6,
  abundance.threshold = 0.01,
  label_A = "A",
  label_B = "B"
)

Arguments

Value

A ggplot2 object to be further modified.

Examples

library(data.table)
library(ggplot2)

# Create mock data frame
mock_volcano_data <- data.table(

  # Feature names (feature_rank)
  Feature = paste0("Gene", 1:20),   

  # Log2 fold changes (X)        
  log2FC = c(1.2, -1.5, 0.3, -0.7, 2.3,    
             -2.0, 0.1, 0.5, -1.0, 1.8,
             -0.4, 0.7, -1.4, 1.5, 0.9,
             -2.1, 0.2, 1.0, -0.3, -1.8),
  
  # P-values (Y)
  pvalue = c(0.001, 0.02, 0.3, 0.04, 0.0005, 
             0.01, 0.7, 0.5, 0.02, 0.0008,
             0.15, 0.06, 0.01, 0.005, 0.3,
             0.02, 0.8, 0.04, 0.12, 0.03),
  
  # Mean (relative) abundance for point sizing
  rel_abun = runif(20, 0.01, 0.1)            
)

volcano_plot(
  data = mock_volcano_data,
  logfold_col = "log2FC",
  pvalue_col = "pvalue",
  abundance_col = "rel_abun",
  feature_rank = "Feature",
)