omics: Abstract omics class
In OmicFlow: Fast and Efficient (Automated) Analysis of Sparse Omics Data
omics R Documentation
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
countDataA path to an existing file, data.table or data.frame.
featureDataA path to an existing file, data.table or data.frame.
metaDataA path to an existing file, data.table or data.frame.
.valid_schemaBoolean value for schema validation via JSON
.feature_idA character, default name for the feature identifiers.
.sample_idA character, default name for the sample identifiers.
.samplepair_idA character, default name for the sample pair identifiers.
Methods
Public methods
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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$new(countData = NULL, featureData = NULL, metaData = NULL)
Arguments
countDataA path to an existing file, data.table or data.frame.
featureDataA path to an existing file, data.table or data.frame.
metaDataA path to an existing file, data.table or data.frame.
Returns
A new omics object.
Method validate()
Validates an input metadata against the JSON schema. The metadata should look as follows and should not contain any empty spaces.
For example; 'sample 1' is not allowed, whereas 'sample1' is allowed!
Acceptable column headers:
SAMPLE_ID (required)
SAMPLEPAIR_ID (optional)
FEATURE_ID (optional)
CONTRAST_ (optional), used for autoFlow().
VARIABLE_ (optional), not supported yet.
This function is used during the creation of a new object via new() to validate the supplied metadata
via a filepath or existing data.table or data.frame.
Usage
omics$validate()
Returns
None
Method removeZeros()
Removes empty (zero) values by row and column from the countData.
This method is performed automatically during subsetting of the object.
Usage
omics$removeZeros()
Returns
object in place
Examples
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
obj$removeZeros()
Method removeNAs()
Remove NAs from metaData and updates the countData.
Usage
omics$removeNAs(column)
Arguments
columnThe column from where NAs should be removed, this can be either a wholenumbers or characters. Vectors are also supported.
Returns
object in place
Examples
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
obj$removeNAs(column = "treatment")
Method feature_subset()
Feature subset (based on featureData), automatically applies removeZeros().
Usage
omics$feature_subset(...)
Arguments
...Expressions that return a logical value, and are defined in terms of the variables in featureData.
Only rows for which all conditions evaluate to TRUE are kept.
Returns
object in place
Examples
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
obj$feature_subset(Genus == "Streptococcus")
Method sample_subset()
Sample subset (based on metaData), automatically applies removeZeros().
Usage
omics$sample_subset(...)
Arguments
...Expressions that return a logical value, and are defined in terms of the variables in metaData.
Only rows for which all conditions evaluate to TRUE are kept.
Returns
object in place
Examples
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
obj$sample_subset(treatment == "tumor")
Method samplepair_subset()
Samplepair subset (based on metaData), automatically applies removeZeros().
Usage
omics$samplepair_subset(num_unique_pairs = NULL)
Arguments
num_unique_pairsAn integer value to define the number of pairs to subset. The default is NULL,
meaning the maximum number of unique pairs will be used to subset the data.
Let's say you have three samples for each pair, then the num_unique_pairs will be set to 3.
Returns
object in place
Method feature_merge()
Agglomerates features by column, automatically applies removeZeros().
Usage
omics$feature_merge(feature_rank, feature_filter = NULL)
Arguments
feature_rankA character value or vector of columns to aggregate from the featureData.
feature_filterA character value or vector of characters to remove features via regex pattern.
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$transform(FUN)
Arguments
FUNA function such as log2, log
Returns
object in place
Examples
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
obj$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$rankstat(feature_ranks)
Arguments
feature_ranksA vector of characters or integers that match the featureData.
Details
Counts the number of features identified for each column, for example in case of 16S metagenomics it would be the number of OTUs or ASVs on different taxonomy levels.
Returns
A ggplot object.
Examples
library(ggplot2)
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
plt <- obj$rankstat(feature_ranks = c("Kingdom", "Phylum", "Family", "Genus", "Species"))
plt
Method alpha_diversity()
Alpha diversity based on diversity
Usage
omics$alpha_diversity(
col_name,
metric = c("shannon", "invsimpson", "simpson"),
Brewer.palID = "Set2",
evenness = FALSE,
paired = FALSE,
p.adjust.method = "fdr"
)
Arguments
col_nameA character variable from the metaData.
metricAn alpha diversity metric as input to diversity.
Brewer.palIDA character name for the palette set to be applied, see brewer.pal or colormap.
evennessA boolean wether to divide diversity by number of species, see specnumber.
pairedA boolean value to perform paired analysis in wilcox.test and samplepair subsetting via samplepair_subset()
p.adjust.methodA character variable to specify the p.adjust.method to be used, default is 'fdr'.
Examples
library(ggplot2)
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
plt <- obj$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$composition(
feature_rank,
feature_filter = NULL,
col_name = NULL,
normalize = TRUE,
feature_top = c(10, 15),
Brewer.palID = "RdYlBu"
)
Arguments
feature_rankA character variable in featureData to aggregate via feature_merge().
feature_filterA character or vector of characters to removes features by regex pattern.
col_nameOptional, a character or vector of characters to add to the final compositional data output.
normalizeA boolean value, whether to normalize() by total sample sums (Default: TRUE).
feature_topA wholenumber of the top features to visualize, the max is 15, due to a limit of palettes.
Brewer.palIDA character name for the palette set to be applied, see brewer.pal or colormap.
Examples
library(ggplot2)
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
result <- obj$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$ordination(
metric = c("bray", "jaccard", "unifrac"),
method = c("pcoa", "nmds"),
group_by,
distmat = NULL,
weighted = TRUE,
normalize = TRUE,
cpus = 1,
perm = 999
)
Arguments
metricA dissimilarity or similarity metric to be applied on the countData,
thus far supports 'bray', 'jaccard' and 'unifrac' when a tree is provided via treeData, see bdiv_distmat.
methodOrdination method, supports "pcoa" and "nmds", see wcmdscale.
group_byA character variable in metaData to be used for the pairwise_adonis or pairwise_anosim statistical test.
distmatA custom distance matrix in either dist or Matrix format.
weightedA boolean value, whether to compute weighted or unweighted dissimilarities (Default: TRUE).
normalizeA boolean value, whether to normalize() by total sample sums (Default: TRUE).
cpusA wholenumber, indicating the number of processes to spawn (Default: 1) in bdiv_distmat.
permA wholenumber, number of permutations to compare against the null hypothesis of adonis2 and anosim (default: perm=999).
Examples
library(ggplot2)
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
pcoa_plots <- obj$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$DFE(
feature_rank,
feature_filter = NULL,
paired = FALSE,
normalize = TRUE,
condition.group,
condition_A,
condition_B,
pvalue.threshold = 0.05,
foldchange.threshold = 0.06,
abundance.threshold = 0
)
Arguments
feature_rankA character or vector of characters in the featureData to aggregate via feature_merge().
feature_filterA character or vector of characters to remove features via regex pattern (Default: NULL).
pairedA boolean value, the paired is only applicable when a SAMPLEPAIR_ID column exists within the metaData. See wilcox.test and samplepair_subset().
normalizeA boolean value, whether to normalize() by total sample sums (Default: TRUE).
condition.groupA character variable of an existing column name in metaData, wherein the conditions A and B are located.
condition_AA character value or vector of characters.
condition_BA character value or vector of characters.
pvalue.thresholdA numeric value used as a p-value threshold to label and color significant features (Default: 0.05).
foldchange.thresholdA numeric value used as a fold-change threshold to label and color significantly expressed features (Default: 0.06).
abundance.thresholdA numeric value used as an abundance threshold to size the scatter dots based on their mean relative abundance (default: 0.01).
Examples
library(ggplot2)
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
unpaired <- obj$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$autoFlow(
feature_ranks = c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species"),
feature_contrast = c("Phylum", "Family", "Genus"),
feature_filter = c("uncultured"),
distance_metrics = c("unifrac"),
beta_div_table = NULL,
alpha_div_table = NULL,
normalize = TRUE,
weighted = TRUE,
pvalue.threshold = 0.05,
perm = 999,
cpus = 1,
filename = paste0(getwd(), "/report.html")
)
Arguments
feature_ranksA character vector as input to rankstat().
feature_contrastA character vector of feature columns in the featureData to aggregate via feature_merge().
feature_filterA character vector to filter unwanted features, default: c("uncultured")
distance_metricsA character vector specifying what (dis)similarity metrics to use, default c("unifrac")
beta_div_tableA path to pre-computed distance matrix, expects tsv/csv/txt file.
alpha_div_tableA path to pre-computed alpha diversity with rarefraction depth, expects tsv/csv/txt from qiime2, see read_rarefraction_qiime.
normalizeA boolean value, whether to normalize() by total sample sums (Default: TRUE).
weightedA boolean value, whether to compute weighted or unweighted dissimilarities (Default: TRUE).
pvalue.thresholdA numeric value, the p-value is used to include/exclude composition and foldchanges plots coming from alpha- and beta diversity analysis (Default: 0.05).
permA wholenumber, number of permutations to compare against the null hypothesis of adonis2 or anosim (default: perm=999).
cpusNumber of cores to use, only used in ordination() when beta_div_table is not supplied.
filenameA character to name the HTML report, it can also be a filepath (e.g. "/path/to/report.html"). Default: "report.html" in your current work directory.
Returns
A report in HTML format
See Also
diversity_plot
composition_plot
ordination_plot, plot_pairwise_stats, pairwise_anosim, pairwise_adonis
volcano_plot, foldchange
Examples
## ------------------------------------------------
## Method `omics$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"))
OmicFlow documentation built on Sept. 9, 2025, 5:24 p.m.
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| omics | R Documentation |
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:
-
divA data.frame from diversity. -
statsA pairwise statistics from pairwise_wilcox_test. -
plotA ggplot object.
A list of components:
-
dataA data.table of feature compositions. -
paletteA setNames palette from colormap.
A list of components:
-
distmatA distance dissimilarity in matrix format. -
statsA statistical test as a data.frame. -
pcsprincipal components as a data.frame. -
scree_plotA ggplot object. -
anova_plotA ggplot object. -
scores_plotA ggplot object.
-
dfeA long data.table table. -
volcano_plotA ggplot object.
Public fields
countDataA path to an existing file, data.table or data.frame.
featureDataA path to an existing file, data.table or data.frame.
metaDataA path to an existing file, data.table or data.frame.
.valid_schemaBoolean value for schema validation via JSON
.feature_idA character, default name for the feature identifiers.
.sample_idA character, default name for the sample identifiers.
.samplepair_idA character, default name for the sample pair identifiers.
Methods
Public methods
Method new()
Wrapper function that is inherited and adapted for each omics class.
The omics classes requires a metadata samplesheet, that is validated by the metadata_schema.json.
It requires a column SAMPLE_ID and optionally a SAMPLEPAIR_ID or FEATURE_ID can be supplied.
The SAMPLE_ID will be used to link the metaData to the countData, and will act as the key during subsetting of other columns.
To create a new object use new() method. Do notice that the abstract class only checks if the metadata is valid!
The countData and featureData will not be checked, these are handles by the sub-classes.
Using the omics class to load your data is not supported and still experimental.
Usage
omics$new(countData = NULL, featureData = NULL, metaData = NULL)
Arguments
countDataA path to an existing file, data.table or data.frame.
featureDataA path to an existing file, data.table or data.frame.
metaDataA path to an existing file, data.table or data.frame.
Returns
A new omics object.
Method validate()
Validates an input metadata against the JSON schema. The metadata should look as follows and should not contain any empty spaces.
For example; 'sample 1' is not allowed, whereas 'sample1' is allowed!
Acceptable column headers:
SAMPLE_ID (required)
SAMPLEPAIR_ID (optional)
FEATURE_ID (optional)
CONTRAST_ (optional), used for
autoFlow().VARIABLE_ (optional), not supported yet.
This function is used during the creation of a new object via new() to validate the supplied metadata
via a filepath or existing data.table or data.frame.
Usage
omics$validate()
Returns
None
Method removeZeros()
Removes empty (zero) values by row and column from the countData.
This method is performed automatically during subsetting of the object.
Usage
omics$removeZeros()
Returns
object in place
Examples
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
obj$removeZeros()
Method removeNAs()
Remove NAs from metaData and updates the countData.
Usage
omics$removeNAs(column)
Arguments
columnThe column from where NAs should be removed, this can be either a wholenumbers or characters. Vectors are also supported.
Returns
object in place
Examples
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
obj$removeNAs(column = "treatment")
Method feature_subset()
Feature subset (based on featureData), automatically applies removeZeros().
Usage
omics$feature_subset(...)
Arguments
...Expressions that return a logical value, and are defined in terms of the variables in
featureData. Only rows for which all conditions evaluate to TRUE are kept.
Returns
object in place
Examples
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
obj$feature_subset(Genus == "Streptococcus")
Method sample_subset()
Sample subset (based on metaData), automatically applies removeZeros().
Usage
omics$sample_subset(...)
Arguments
...Expressions that return a logical value, and are defined in terms of the variables in
metaData. Only rows for which all conditions evaluate to TRUE are kept.
Returns
object in place
Examples
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
obj$sample_subset(treatment == "tumor")
Method samplepair_subset()
Samplepair subset (based on metaData), automatically applies removeZeros().
Usage
omics$samplepair_subset(num_unique_pairs = NULL)
Arguments
num_unique_pairsAn integer value to define the number of pairs to subset. The default is NULL, meaning the maximum number of unique pairs will be used to subset the data. Let's say you have three samples for each pair, then the
num_unique_pairswill be set to 3.
Returns
object in place
Method feature_merge()
Agglomerates features by column, automatically applies removeZeros().
Usage
omics$feature_merge(feature_rank, feature_filter = NULL)
Arguments
feature_rankA character value or vector of columns to aggregate from the
featureData.feature_filterA character value or vector of characters to remove features via regex pattern.
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$transform(FUN)
Arguments
FUNA function such as
log2,log
Returns
object in place
Examples
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
obj$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$rankstat(feature_ranks)
Arguments
feature_ranksA vector of characters or integers that match the
featureData.
Details
Counts the number of features identified for each column, for example in case of 16S metagenomics it would be the number of OTUs or ASVs on different taxonomy levels.
Returns
A ggplot object.
Examples
library(ggplot2)
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
plt <- obj$rankstat(feature_ranks = c("Kingdom", "Phylum", "Family", "Genus", "Species"))
plt
Method alpha_diversity()
Alpha diversity based on diversity
Usage
omics$alpha_diversity(
col_name,
metric = c("shannon", "invsimpson", "simpson"),
Brewer.palID = "Set2",
evenness = FALSE,
paired = FALSE,
p.adjust.method = "fdr"
)Arguments
col_nameA character variable from the
metaData.metricAn alpha diversity metric as input to diversity.
Brewer.palIDA character name for the palette set to be applied, see brewer.pal or colormap.
evennessA boolean wether to divide diversity by number of species, see specnumber.
pairedA boolean value to perform paired analysis in wilcox.test and samplepair subsetting via
samplepair_subset()p.adjust.methodA character variable to specify the p.adjust.method to be used, default is 'fdr'.
Examples
library(ggplot2)
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
plt <- obj$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$composition( feature_rank, feature_filter = NULL, col_name = NULL, normalize = TRUE, feature_top = c(10, 15), Brewer.palID = "RdYlBu" )
Arguments
feature_rankA character variable in
featureDatato aggregate viafeature_merge().feature_filterA character or vector of characters to removes features by regex pattern.
col_nameOptional, a character or vector of characters to add to the final compositional data output.
normalizeA boolean value, whether to
normalize()by total sample sums (Default: TRUE).feature_topA wholenumber of the top features to visualize, the max is 15, due to a limit of palettes.
Brewer.palIDA character name for the palette set to be applied, see brewer.pal or colormap.
Examples
library(ggplot2)
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
result <- obj$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$ordination(
metric = c("bray", "jaccard", "unifrac"),
method = c("pcoa", "nmds"),
group_by,
distmat = NULL,
weighted = TRUE,
normalize = TRUE,
cpus = 1,
perm = 999
)Arguments
metricA dissimilarity or similarity metric to be applied on the
countData, thus far supports 'bray', 'jaccard' and 'unifrac' when a tree is provided viatreeData, see bdiv_distmat.methodOrdination method, supports "pcoa" and "nmds", see wcmdscale.
group_byA character variable in
metaDatato be used for the pairwise_adonis or pairwise_anosim statistical test.distmatA custom distance matrix in either dist or Matrix format.
weightedA boolean value, whether to compute weighted or unweighted dissimilarities (Default: TRUE).
normalizeA boolean value, whether to
normalize()by total sample sums (Default: TRUE).cpusA wholenumber, indicating the number of processes to spawn (Default: 1) in bdiv_distmat.
permA wholenumber, number of permutations to compare against the null hypothesis of adonis2 and anosim (default:
perm=999).
Examples
library(ggplot2)
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
pcoa_plots <- obj$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$DFE( feature_rank, feature_filter = NULL, paired = FALSE, normalize = TRUE, condition.group, condition_A, condition_B, pvalue.threshold = 0.05, foldchange.threshold = 0.06, abundance.threshold = 0 )
Arguments
feature_rankA character or vector of characters in the
featureDatato aggregate viafeature_merge().feature_filterA character or vector of characters to remove features via regex pattern (Default: NULL).
pairedA boolean value, the paired is only applicable when a
SAMPLEPAIR_IDcolumn exists within themetaData. See wilcox.test andsamplepair_subset().normalizeA boolean value, whether to
normalize()by total sample sums (Default: TRUE).condition.groupA character variable of an existing column name in
metaData, wherein the conditions A and B are located.condition_AA character value or vector of characters.
condition_BA character value or vector of characters.
pvalue.thresholdA numeric value used as a p-value threshold to label and color significant features (Default: 0.05).
foldchange.thresholdA numeric value used as a fold-change threshold to label and color significantly expressed features (Default: 0.06).
abundance.thresholdA numeric value used as an abundance threshold to size the scatter dots based on their mean relative abundance (default: 0.01).
Examples
library(ggplot2)
obj_path <- system.file("extdata", "mock_taxa.rds", package = "OmicFlow", mustWork = TRUE)
obj <- readRDS(obj_path)
unpaired <- obj$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$autoFlow(
feature_ranks = c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species"),
feature_contrast = c("Phylum", "Family", "Genus"),
feature_filter = c("uncultured"),
distance_metrics = c("unifrac"),
beta_div_table = NULL,
alpha_div_table = NULL,
normalize = TRUE,
weighted = TRUE,
pvalue.threshold = 0.05,
perm = 999,
cpus = 1,
filename = paste0(getwd(), "/report.html")
)Arguments
feature_ranksA character vector as input to
rankstat().feature_contrastA character vector of feature columns in the
featureDatato aggregate viafeature_merge().feature_filterA character vector to filter unwanted features, default:
c("uncultured")distance_metricsA character vector specifying what (dis)similarity metrics to use, default
c("unifrac")beta_div_tableA path to pre-computed distance matrix, expects tsv/csv/txt file.
alpha_div_tableA path to pre-computed alpha diversity with rarefraction depth, expects tsv/csv/txt from qiime2, see read_rarefraction_qiime.
normalizeA boolean value, whether to
normalize()by total sample sums (Default: TRUE).weightedA boolean value, whether to compute weighted or unweighted dissimilarities (Default: TRUE).
pvalue.thresholdA numeric value, the p-value is used to include/exclude composition and foldchanges plots coming from alpha- and beta diversity analysis (Default: 0.05).
permA wholenumber, number of permutations to compare against the null hypothesis of adonis2 or anosim (default:
perm=999).cpusNumber of cores to use, only used in
ordination()when beta_div_table is not supplied.filenameA character to name the HTML report, it can also be a filepath (e.g.
"/path/to/report.html"). Default: "report.html" in your current work directory.
Returns
A report in HTML format
See Also
diversity_plot
composition_plot
ordination_plot, plot_pairwise_stats, pairwise_anosim, pairwise_adonis
volcano_plot, foldchange
Examples
## ------------------------------------------------
## Method `omics$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"))
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