Package 'OmicFlow'

Description

Calculates the Bray-Curtis dissimilarity of a Matrix pairwise for each column.

Usage

bray(x, weighted = TRUE, threads = 1)

Arguments

Details

The Bray-Curtis dissimilarity between two samples $A$ and $B$, each of length $n$, is defined as:

$d(A,B) = \frac{\sum_{i}^n |A_i - B_i|}{\sum_{i}^n (A_i + B_i)}$

where $A_i$ and $B_i$ are the abundances of the $i$-th feature in sample $A$ and $B$, respectively. When weighted is set to FALSE, counts are replaced by presence/absence data.

Value

A column x column dist object.

References

Bray, J.R. & Curtis, J.T. (1957) An Ordination of the Upland Forest Communities of Southern Wisconsin. Ecological Monographs, 27(4), 325–349.

Examples

library("OmicFlow")

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

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

taxa$feature_subset(Kingdom == "Bacteria")
taxa$scale(method = "tss")

bray(taxa$countData)

Description

Calculates the Canberra dissimilarity of a Matrix pairwise for each column.

Usage

canberra(x, weighted = TRUE, threads = 1)

Arguments

Details

The Canberra dissimilarity between two samples $A$ and $B$, each of length $n$, is defined as:

$d(A,B) = \frac{1 / NZ} \sum_{i}^n \frac{|A_i - B_i|}{|A_i| + |B_i|}$

where $A_i$ and $B_i$ are the abundances of the $i$-th feature in sample $A$ and $B$, respectively. NZ are the number of non-zero entries. When weighted is set to FALSE, counts are replaced by presence/absence data.

Value

A column x column dist object.

References

Lance, G.N. & Williams, W.T. (1967) Mixed-data classificatory programs. I. Agglomerative systems. Australian Computer Journal, 1(1), 15-20.

Examples

library("OmicFlow")

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

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

taxa$feature_subset(Kingdom == "Bacteria")
taxa$scale(method = "tss")

canberra(taxa$countData)

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")

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.

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"
)

Description

Calculates the cosine disimilarity of a Marix pairwise for each column.

Usage

cosine(x, weighted = TRUE, threads = 1)

Arguments

Details

The cosine dissimilarity between two samples $A$ and $B$, each of length $n$, is defined as:

$d(A,B) = 1 - \frac{\sum_{i}^n A_i B_i}{\sqrt{\sum_{i}^n A_i^2} \sqrt{\sum_{i}^n B_i^2}}$

where $A_i$ and $B_i$ are the abundances of the $i$-th feature in sample $A$ and $B$, respectively. When weighted is set to FALSE, counts are replaced by presence/absence data.

Value

A column x column dist object.

References

Deza, M. M., & Deza, E. (2009). Encyclopedia of Distances. Springer Science & Business Media., 308.

Examples

library("OmicFlow")

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

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

taxa$feature_subset(Kingdom == "Bacteria")
taxa$scale(method = "tss")

cosine(taxa$countData)

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 mathematically 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

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"
)

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,
  group_by = NULL,
  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(
  "shannon" = div,
  "treatment" = c(rep("healthy", n_col / 2), rep("tumor", n_col / 2)),
  "sex" = c(rep("male", n_col / 4), rep("female", n_col / 4))
)

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

# Comparing two groups
plt <- OmicFlow::diversity_plot(
 data = dt,
 values = "shannon",
 col_name = "treatment",
 palette = colors,
 method = "shannon",
 paired = FALSE,
 p.adjust.method = "fdr"
)

# Performing a test while stratifying the plot in two groups
plt <- OmicFlow::diversity_plot(
 data = dt,
 values = "shannon",
 col_name = "treatment",
 group_by = "sex",
 palette = colors,
 method = "shannon",
 paired = FALSE,
 p.adjust.method = "fdr"
)

Description

Calculates the Euclidean dissimilarity of a Matrix pairwise for each column.

Usage

euclidean(x, weighted = TRUE, threads = 1)

Arguments

Details

The Euclidean dissimilarity between two samples $A$ and $B$, each of length $n$, is defined as:

$d(A,B) = \sqrt{\sum_{i=1}^n (A_i - B_i)^2}$

where $A_i$ and $B_i$ are the abundances of the $i$-th feature in sample $A$ and $B$, respectively. When weighted is set to FALSE, counts are replaced by presence/absence data.

Value

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")
tree_file <- system.file("extdata", "tree.newick", package = "OmicFlow")

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

taxa$feature_subset(Kingdom == "Bacteria")
taxa$scale(method = "tss")

euclidean(taxa$countData)

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
)

Description

Calculates the Jaccard dissimilarity of a Matrix pairwise for each column.

Usage

jaccard(x, weighted = TRUE, threads = 1)

Arguments

Details

The weighted Jaccard disimilarity between two samples $A$ and $B$, each of length $n$, is defined as:

$d(A,B) = 1 - \frac{ \sum_{i}^{n} \min(A_i, B_i) }{ \sum_{i}^{n} \max(A_i, B_i) }$

where $A_i$ and $B_i$ are the abundances of the $i$-th feature in sample $A$ and $B$, respectively. When weighted is set to FALSE, abundances are changed to 1 (classical Jaccard for binary data).

Value

A column x column dist object.

References

Jaccard, P. (1912) The distribution of the flora in the alpine zone. New Phytologist, 11(2), 37–50. library("OmicFlow")

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

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

taxa$feature_subset(Kingdom == "Bacteria") taxa$scale(method = "tss")

jaccard(taxa$countData)

Description

Calculates the Jensen-Shannon divergence of a Matrix pairwise for each column.

Usage

jsd(x, weighted = TRUE, threads = 1)

Arguments

Details

The Jensen-Shannon divergence between two probability distributions $A$ and $B$, each of length $n$, is defined as:

$d(A, B) = \frac{1}{2} D_{KL}(A \parallel M) + \frac{1}{2} D_{KL}(B \parallel M)$

where $M = \frac{1}{2} (A + B)$ is the mixture distribution, and $D_{KL}$ is the Kullback-Leibler divergence. When weighted is set to FALSE, counts are replaced by presence/absence data.

Value

A column x column dist object.

References

Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151.

Examples

library("OmicFlow")

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

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

taxa$feature_subset(Kingdom == "Bacteria")
taxa$scale(method = "tss")

jsd(taxa$countData)

Description

Calculates the Manhattan dissimilarity of a Matrix pairwise for each column.

Usage

manhattan(x, weighted = TRUE, threads = 1)

Arguments

Details

The Manhattan dissimilarity between two samples $A$ and $B$, each of length $n$, is defined as:

$d(A, B) = \sum_{i}^n |A_i - B_i|$

where $A_i$ and $B_i$ are the abundances of the $i$-th feature in sample $A$ and $B$, respectively. When weighted is set to FALSE, counts are replaced by presence/absence data.

Value

A column x column dist object.

References

Deza, M. M., & Deza, E. (2009). Encyclopedia of Distances. Springer Science & Business Media., 313.

Examples

library("OmicFlow")

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

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

taxa$feature_subset(Kingdom == "Bacteria")
taxa$scale(method = "tss")

manhattan(taxa$countData)

Description

Wrapper function that converts a sparseMatrix to data.table

Usage

matrix_to_dtable(x)

Arguments

Value

A data.table class.

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.

Value

• 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.

Super class

OmicFlow::omics -> metagenomics

Active bindings

Methods

Public methods

• metagenomics$new()

• metagenomics$write_biom()

• metagenomics$foldchange()

• metagenomics$clone()

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 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

library("OmicFlow")

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

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

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

Method foldchange()

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

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

Usage

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

Arguments

Examples

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

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

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

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

Method clone()

The objects of this class are cloneable with this method.

Usage

metagenomics$clone(deep = FALSE)

Arguments

See Also

omics

volcano_plot

Examples

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

library("OmicFlow")

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

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

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


## ------------------------------------------------
## Method `metagenomics$foldchange`
## ------------------------------------------------

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

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

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

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

Description

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.

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.

• 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

Active bindings

Methods

Public methods

• 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()

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 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

Returns

A new omics object.

Method 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

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.

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)

• 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 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()

Method 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"))

Method removeNAs()

Remove NAs from metaData and updates the countData.

Usage

omics$removeNAs(column)

Arguments

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 data synchronization.

Usage

omics$feature_subset(...)

Arguments

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")

Method sample_subset()

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

Usage

omics$sample_subset(...)

Arguments

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 synchronization.

Usage

omics$samplepair_subset(num_unique_pairs = NULL)

Arguments

Returns

object in place

Method feature_merge()

Agglomerates features by column, automatically applies synchronization.

Usage

omics$feature_merge(feature_rank, feature_filter = NULL)

Arguments

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 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

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)

Method rankstat()

Rank statistics based on featureData

Usage

omics$rankstat(feature_ranks, unique = FALSE)

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")
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 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

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,
  feature_top = c(10, 15),
  Brewer.palID = "RdYlBu"
)

Arguments

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,
  weighted = TRUE,
  threads = 1,
  normalized = TRUE,
  base = exp(1)
)

Arguments

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,
  threads = 1,
  perm_design = NULL,
  perm = 999
)

Arguments

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

Method 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

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"))

Method 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

Returns

List of plots/data or rendered HTML report

Method clone()

The objects of this class are cloneable with this method.

Usage

omics$clone(deep = FALSE)

Arguments

References

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

See Also

diversity_plot

composition_plot

bray, canberra, cosine, jaccard, jsd, manhattan, unifrac

ordination_plot, plot_pairwise_stats, pairwise_anosim, pairwise_adonis

volcano_plot

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
)

# 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
)

# method 1 to call print function
obj

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


## ------------------------------------------------
## 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
)

# 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,
)

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 == "Pseudomonas")


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

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)
pcoa_plots


## ------------------------------------------------
## 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$new(
 metaData = metadata_file,
 countData = counts_file,
 featureData = features_file
)
obj$scale(method = "clr")

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

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"
)

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,
  metadata = NULL,
  perm_design = NULL,
  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)

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,
  metadata = NULL,
  perm_design = NULL,
  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)

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")

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

Active bindings

Methods

Public methods

• proteomics$new()

• proteomics$clone()

Method new()

Initializes the proteomics class object with proteomics$new()

Usage

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

Arguments

Returns

A new proteomics object.

Method clone()

The objects of this class are cloneable with this method.

Usage

proteomics$clone(deep = FALSE)

Arguments

See Also

omics

Description

Calculates the UniFrac dissimilarity between samples based on phylogenetic branch lengths and abundance or presence/absence data.

Usage

unifrac(x, tree, weighted = TRUE, normalized = TRUE, threads = 1)

Arguments

Details

The UniFrac distance between two samples $A$ and $B$, with phylogenetic tree edges $i = 1 \ldots n$ of lengths $L_i$, is computed differently depending on the weighted and normalized flags. When weighted = FALSE, input counts are first converted to presence/absence data.

Weighted UniFrac (normalized = FALSE and weighted = TRUE):

$d(A,B) = \frac{\sum_{i}^n L_i |A_i - B_i|}{\sum_{i}^n L_i (A_i + B_i)}$

Normalized Weighted UniFrac (normalized = TRUE and weighted = TRUE):

$d(A,B) = \sum_{i}^n L_i |A_i - B_i|$

Unweighted UniFrac (weighted = FALSE, unweighted is always normalized):

$d(A,B) = \frac{\sum_{i}^n L_i |A_i - B_i|}{\sum_{i}^n L_i \max(A_i, B_i)}$

Value

A column x column dist object.

References

Lozupone, C., & Knight, R. (2005). UniFrac: a new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology, 71(12), 8228–8235.

Examples

library("OmicFlow")

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

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

taxa$feature_subset(Kingdom == "Bacteria")
taxa$scale(method = "tss")

# Weighted UniFrac
unifrac(x = taxa$countData, tree = taxa$treeData, weighted=TRUE, normalized=FALSE)

# Weighted Normalized UniFrac
unifrac(x = taxa$countData, tree = taxa$treeData, weighted=TRUE, normalized=TRUE)

# Unweighted UniFrac
unifrac(x = taxa$countData, tree = taxa$treeData, weighted=FALSE)

Description

Creates a Volcano plot from the output of foldchange method from class omics, 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",
)