Package 'romic' reference manual

Title:	R for High-Dimensional Omic Data
Description:	Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. 'romic' takes advantage of these transformations to create interactive 'shiny' apps for exploratory data analysis such as an interactive heatmap.
Authors:	Sean Hackett [aut, cre] , Calico Life Sciences LLC [cph, fnd]
Maintainer:	Sean Hackett <[email protected]>
License:	MIT + file LICENSE
Version:	1.1.3
Built:	2025-02-12 05:12:19 UTC
Source:	https://github.com/cran/romic

Add PCA Loadings

Description

Add Principal Components Analysis Loadings to a tidy or triple omics dataset.

Usage

add_pcs(
  tomic,
  value_var = NULL,
  center_rows = TRUE,
  npcs = NULL,
  missing_val_method = "drop_samples",
  label_percent_varex = TRUE,
  verbose = TRUE
)
add_pcs(
  tomic,
  value_var = NULL,
  center_rows = TRUE,
  npcs = NULL,
  missing_val_method = "drop_samples",
  label_percent_varex = TRUE,
  verbose = TRUE
)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`value_var`	An abundance value to use with `hclust`
`center_rows`	center rows before performing PCA
`npcs`	number of principal component loadings to add to samples (default is number of samples)
`missing_val_method`	Approach to remove missing values: drop_features Drop features with missing values drop_samples Drop samples which are missing all features, then drop features impute Impute missing values
`label_percent_varex`	If true then PCs will be labelled by the percent of variability they explain.
`verbose`	extra reporting messages

Value

A tomic object with principal components added to samples.

Examples

add_pcs(brauer_2008_triple, npcs = 5)

add_pcs(brauer_2008_triple, npcs = 5)

Flow

Description

Using shiny comb through datasets by iterating between plotting steps, and lassoing steps to select points of interest.

Usage

app_flow(tomic)
app_flow(tomic)

Arguments

tomic

Either a tidy_omic or triple_omic object

Value

A shiny app

Examples


if (interactive()) {
  # library(reactlog)
  # reactlog_enable()
  app_flow(brauer_2008_triple)
  # shiny::reactlogShow()
}

if (interactive()) {
  # library(reactlog)
  # reactlog_enable()
  app_flow(brauer_2008_triple)
  # shiny::reactlogShow()
}

Interactive Heatmap

Description

Generate a shiny interactive heatmap that allows for on demand filtering, ordering and faceting by variables of interest.

Usage

app_heatmap(tomic)
app_heatmap(tomic)

Arguments

tomic

Either a tidy_omic or triple_omic object

Value

A shiny app

Examples


if (interactive()) {
  app_heatmap(brauer_2008_tidy)
}
if (interactive()) {
  app_heatmap(brauer_2008_tidy)
}

PC Plot

Description

Generate a Shiny interactive scatter plot which allows visualization of features, measurements, and samples (with principal components added).

Usage

app_pcs(tomic)
app_pcs(tomic)

Arguments

tomic

Either a tidy_omic or triple_omic object

Value

A shiny app

Examples

if (interactive()) {
  app_pcs(brauer_2008_tidy)
}
if (interactive()) {
  app_pcs(brauer_2008_tidy)
}

Brauer 2008

Description

An RNA expression (microarray) dataset looking at how yeast gene expression changes as nutrient sources and nutrient richness changes.

brauer_2008 formatted as a tidy_omic object

brauer_2008 formatted as a triple_omic object

Usage

brauer_2008

brauer_2008_tidy

brauer_2008_triple
brauer_2008

brauer_2008_tidy

brauer_2008_triple

Format

A tibble with 18,000 rows and 8 columns:

name: Common gene name
BP: Gene ontology biological process of the gene
MF: Gene ontology molecular function of the gene
sample: Sample name
nutrient: Which nutrient limits growth (Glucose, Nitrogen, Phosphorous, Sulfur, Uracil, Leucine
DR: Dilution rate of the culture - basically how fast the cells are growing
expression: Expression level of the gene, log2 observation relative to a replicate of G0.3

An object of class tidy_omic (inherits from tomic, general) of length 2.

An object of class triple_omic (inherits from tomic, general) of length 4.

Details

This version of the dataset contains only 500 genes randomly selected from the ~6K genes in the complete dataset.

Source

https://pubmed.ncbi.nlm.nih.gov/17959824/

Center T* Omic

Description

Center each measurement by subtracting the mean.

Usage

center_tomic(tomic, measurement_vars = "all")
center_tomic(tomic, measurement_vars = "all")

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`measurement_vars`	measurement variables to center

Value

A tomic object where one or more measurements have been centered on a feature-by-feature basis.

Examples

center_tomic(brauer_2008_tidy)
center_tomic(brauer_2008_tidy)

Check T*Omic

Description

Check a tidy or triple 'omic object for common pathologies, such as a mismatch between data and schema and non-uniqueness of primary keys.

Usage

check_tomic(tomic, fast_check = TRUE)
check_tomic(tomic, fast_check = TRUE)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`fast_check`	if TRUE then skip some checks which are slow and that are generally only needed when a `tomic` object is first created.

Value

0 invisibly

Examples

check_tomic(brauer_2008_triple)
check_tomic(brauer_2008_triple)

Convert Wide to Tidy Omic

Description

Convert a wide dataset of species' abundances (gene product, metabolites, lipids, ...) into a triple_omic dataset (one observation per row)

Usage

convert_wide_to_tidy_omic(
  wide_df,
  feature_pk,
  feature_vars = NULL,
  sample_var = "sample",
  measurement_var = "abundance",
  omic_type_tag = "general",
  verbose = TRUE
)
convert_wide_to_tidy_omic(
  wide_df,
  feature_pk,
  feature_vars = NULL,
  sample_var = "sample",
  measurement_var = "abundance",
  omic_type_tag = "general",
  verbose = TRUE
)

Arguments

`wide_df`	a data.frame (or tibble) containing 1+ columns of feature attributes and many columns of samples
`feature_pk`	A unique identifier for features
`feature_vars`	a character vector of additional feature-level variables (or NULL if there are no additional variables)
`sample_var`	variable name to use for samples
`measurement_var`	variable name to use for measurements
`omic_type_tag`	an optional subtype of omic data: metabolomics, lipidomics, proteomics, genomics, general
`verbose`	extra reporting messages

Value

A tidy_omic object as produced by create_tidy_omic.

Examples


library(dplyr)

wide_measurements <- brauer_2008_triple[["measurements"]] %>%
  tidyr::spread(sample, expression)

wide_df <- brauer_2008_triple[["features"]] %>%
  left_join(wide_measurements, by = "name")

convert_wide_to_tidy_omic(wide_df,
  feature_pk = "name",
  feature_vars = c("BP", "MF", "systematic_name")
)
library(dplyr)

wide_measurements <- brauer_2008_triple[["measurements"]] %>%
  tidyr::spread(sample, expression)

wide_df <- brauer_2008_triple[["features"]] %>%
  left_join(wide_measurements, by = "name")

convert_wide_to_tidy_omic(wide_df,
  feature_pk = "name",
  feature_vars = c("BP", "MF", "systematic_name")
)

Create Tidy Omic

Description

A tidy omics object contains a formatted dataset and a summary of the experimental design.

Usage

create_tidy_omic(
  df,
  feature_pk,
  feature_vars = NULL,
  sample_pk,
  sample_vars = NULL,
  omic_type_tag = "general",
  verbose = TRUE
)
create_tidy_omic(
  df,
  feature_pk,
  feature_vars = NULL,
  sample_pk,
  sample_vars = NULL,
  omic_type_tag = "general",
  verbose = TRUE
)

Arguments

`df`	a data.frame (or tibble) containing some combination of feature, sample and observation-level variables
`feature_pk`	A unique identifier for features
`feature_vars`	a character vector of additional feature-level variables (or NULL if there are no additional variables)
`sample_pk`	A unique identifier for samples
`sample_vars`	a character vector of additional sample-level variables (or NULL if there are no additional variables)
`omic_type_tag`	an optional subtype of omic data: metabolomics, lipidomics, proteomics, genomics, general
`verbose`	extra reporting messages

Value

An S3 tidy_omic/tomic object built on a list:

data

A tibble with one row per measurement (i.e., features x samples)

design

A list which organized the dataset's meta-data:

feature_pk: variable specifying a unique feature
sample_pk: variable specifying a unique sample
features: tibble of feature attributes
samples: tibble of sample attributes
measurements: tibble of measurement attributes

Examples


library(dplyr)

measurement_df <- tidyr::expand_grid(
  feature_id = 1:10,
  sample_id = LETTERS[1:5]
) %>%
  dplyr::mutate(value = rnorm(n()))

feature_df <- tibble(
  feature_id = 1:10,
  feature_group = rep(c("a", "b"), each = 5)
)
sample_df <- tibble(
  sample_id = LETTERS[1:5],
  sample_group = c("a", "a", "b", "b", "b")
)

triple_omic <- create_triple_omic(
  measurement_df, feature_df, sample_df,
  "feature_id", "sample_id"
)
raw_tidy_omic <- triple_to_tidy(triple_omic)$data

create_tidy_omic(raw_tidy_omic,
  feature_pk = "feature_id",
  feature_vars = "feature_group", sample_pk = "sample_id",
  sample_vars = "sample_group"
)
library(dplyr)

measurement_df <- tidyr::expand_grid(
  feature_id = 1:10,
  sample_id = LETTERS[1:5]
) %>%
  dplyr::mutate(value = rnorm(n()))

feature_df <- tibble(
  feature_id = 1:10,
  feature_group = rep(c("a", "b"), each = 5)
)
sample_df <- tibble(
  sample_id = LETTERS[1:5],
  sample_group = c("a", "a", "b", "b", "b")
)

triple_omic <- create_triple_omic(
  measurement_df, feature_df, sample_df,
  "feature_id", "sample_id"
)
raw_tidy_omic <- triple_to_tidy(triple_omic)$data

create_tidy_omic(raw_tidy_omic,
  feature_pk = "feature_id",
  feature_vars = "feature_group", sample_pk = "sample_id",
  sample_vars = "sample_group"
)

A triple omics class contains three data.frames, one for features, one for samples, and one for abundances. This is a good format when there is a large amount of meta data associated with features or samples.

Usage

create_triple_omic(
  measurement_df,
  feature_df = NULL,
  sample_df = NULL,
  feature_pk,
  sample_pk,
  omic_type_tag = "general"
)
create_triple_omic(
  measurement_df,
  feature_df = NULL,
  sample_df = NULL,
  feature_pk,
  sample_pk,
  omic_type_tag = "general"
)

Arguments

`measurement_df`	A data.frame (or tibble) of measurements - one row for each combination of feature and sample
`feature_df`	A data.frame (or tibble) of features - one row per feature
`sample_df`	A data.frame (or tibble) of samples - one row per sample
`feature_pk`	A unique identifier for features
`sample_pk`	A unique identifier for samples
`omic_type_tag`	an optional subtype of omic data: metabolomics, lipidomics, proteomics, genomics, general

Details

for now primary keys are unique (rather than allowing for a multi-index)

Value

An S3 triple_omic/tomic object built on a list:

features

A tibble of feature meta-data (one row per feature)

samples

A tibble of sample meta-data (one row per sample)

measurements

A tibble with one row per measurement (i.e., features x samples)

design

A list which organized the dataset's meta-data:

feature_pk: variable specifying a unique feature
sample_pk: variable specifying a unique sample
features: tibble of feature attributes
samples: tibble of sample attributes
measurements: tibble of measurement attributes

Examples


library(dplyr)

measurement_df <- tidyr::expand_grid(
  feature_id = 1:10,
  sample_id = LETTERS[1:5]
) %>%
  dplyr::mutate(value = rnorm(n()))

feature_df <- tibble(
  feature_id = 1:10,
  feature_group = rep(c("a", "b"), each = 5)
)
sample_df <- tibble(
  sample_id = LETTERS[1:5],
  sample_group = c("a", "a", "b", "b", "b")
)

triple_omic <- create_triple_omic(
  measurement_df, feature_df, sample_df,
  "feature_id", "sample_id"
)
library(dplyr)

measurement_df <- tidyr::expand_grid(
  feature_id = 1:10,
  sample_id = LETTERS[1:5]
) %>%
  dplyr::mutate(value = rnorm(n()))

feature_df <- tibble(
  feature_id = 1:10,
  feature_group = rep(c("a", "b"), each = 5)
)
sample_df <- tibble(
  sample_id = LETTERS[1:5],
  sample_group = c("a", "a", "b", "b", "b")
)

triple_omic <- create_triple_omic(
  measurement_df, feature_df, sample_df,
  "feature_id", "sample_id"
)

Downsample Heatmap

Description

Combine rows to speed up rendering of large heatmaps

Usage

downsample_heatmap(
  tidy_data,
  value_var,
  design,
  max_display_features = 1000,
  verbose = TRUE
)
downsample_heatmap(
  tidy_data,
  value_var,
  design,
  max_display_features = 1000,
  verbose = TRUE
)

Arguments

`tidy_data`	The data frame from a `tidy_omic` object containing ordered feature and sample primary keys defined by ordered_featureId and ordered_sampleId.
`value_var`	which variable in "measurements" to use for quantification.
`design`	a list summarizing the design of the tidy dataset
`max_display_features`	aggregate and downsample distinct feature to this number to speed to up heatmap rendering.
`verbose`	extra reporting messages

Value

tidy_data with rows collapsed if the number of distinct features is greater than max_display_features

Export T*Omic in Tidy Format

Description

Export a data table including all fields from features, samples and measurements.

Usage

export_tomic_as_tidy(tomic, dir_path, name_preamble, verbose = TRUE)
export_tomic_as_tidy(tomic, dir_path, name_preamble, verbose = TRUE)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`dir_path`	path to save outputs
`name_preamble`	start of output file name
`verbose`	extra reporting messages

Value

Export one table which is one row per peak, which includes all feature and sample attributes.

Examples


if (interactive()) {
  export_tomic_as_tidy(brauer_2008_triple, "/tmp", "brauer")
}
if (interactive()) {
  export_tomic_as_tidy(brauer_2008_triple, "/tmp", "brauer")
}

Export T*Omic as Triple

Description

Export features, samples and measurements tables

Usage

export_tomic_as_triple(tomic, dir_path, name_preamble, verbose = TRUE)
export_tomic_as_triple(tomic, dir_path, name_preamble, verbose = TRUE)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`dir_path`	path to save outputs
`name_preamble`	start of output file name
`verbose`	extra reporting messages

Value

Export three tables:

features: one row per features measured (i.e., a metabolite)
sample: one row per sample
measurements: one row per measurement (i.e., one metabolite in one sample)

Examples

if (interactive()) {
  export_tomic_as_triple(brauer_2008_triple, "/tmp", "brauer")
}
if (interactive()) {
  export_tomic_as_triple(brauer_2008_triple, "/tmp", "brauer")
}

Export T*Omic as Wide Data

Description

abundances form a matrix with metabolites as rows and samples as columns. Use transpose to treat samples as rows filename

Usage

export_tomic_as_wide(
  tomic,
  dir_path,
  name_preamble,
  value_var = NULL,
  transpose = FALSE,
  verbose = TRUE
)
export_tomic_as_wide(
  tomic,
  dir_path,
  name_preamble,
  value_var = NULL,
  transpose = FALSE,
  verbose = TRUE
)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`dir_path`	path to save outputs
`name_preamble`	start of output file name
`value_var`	measurement variable to use for the matrix
`transpose`	if TRUE then samples will be stored as rows
`verbose`	extra reporting messages

Value

Export one table which contains metabolites as rows and samples as columns.

Examples


if (interactive()) {
  export_tomic_as_wide(brauer_2008_triple, "/tmp", "brauer")
}
if (interactive()) {
  export_tomic_as_wide(brauer_2008_triple, "/tmp", "brauer")
}

Filter T* Omics

Description

Filter a tidy or triple omic to entries of interest.

Usage

filter_tomic(
  tomic,
  filter_type,
  filter_table,
  filter_value,
  filter_variable = NULL
)
filter_tomic(
  tomic,
  filter_type,
  filter_table,
  filter_value,
  filter_variable = NULL
)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`filter_type`	category filter filter_variable to categories specified in filter_value range filter filter_variable to using the range (i.e., lower and upper limit) provided in filter_value apply a quosure as a `filter_value` to a table of interest
`filter_table`	table where the filter should be applied
`filter_value`	values to filter based on
`filter_variable`	variable to apply the filter to

Value

A tomic object where a subset of features, samples or measurmenets have been filtered.

Examples


filter_tomic(
  brauer_2008_triple,
  filter_type = "category",
  filter_table = "features",
  filter_variable = "BP",
  filter_value = c("biological process unknown", "vacuolar acidification")
)

filter_tomic(
  brauer_2008_triple,
  filter_type = "category",
  filter_table = "samples",
  filter_variable = "DR",
  filter_value = 0.05
)

filter_tomic(
  brauer_2008_tidy,
  filter_type = "range",
  filter_table = "samples",
  filter_variable = "DR",
  filter_value = c(0, 0.2)
)

filter_tomic(
  brauer_2008_triple,
  filter_type = "quo",
  filter_table = "features",
  filter_value = rlang::quo(BP == "biological process unknown")
)
filter_tomic(
  brauer_2008_triple,
  filter_type = "category",
  filter_table = "features",
  filter_variable = "BP",
  filter_value = c("biological process unknown", "vacuolar acidification")
)

filter_tomic(
  brauer_2008_triple,
  filter_type = "category",
  filter_table = "samples",
  filter_variable = "DR",
  filter_value = 0.05
)

filter_tomic(
  brauer_2008_tidy,
  filter_type = "range",
  filter_table = "samples",
  filter_variable = "DR",
  filter_value = c(0, 0.2)
)

filter_tomic(
  brauer_2008_triple,
  filter_type = "quo",
  filter_table = "features",
  filter_value = rlang::quo(BP == "biological process unknown")
)

Filter Input

Description

UI components for the filter module.

Usage

filterInput(id, filter_table)
filterInput(id, filter_table)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`filter_table`	table to filter

Value

A shiny UI

Filter Server

Description

Server components for the filter module.

Usage

filterServer(id, tidy_omic, filter_table)
filterServer(id, tidy_omic, filter_table)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`tidy_omic`	an object of class tidy_omic produced by `create_tidy_omic`
`filter_table`	table to filter

Value

A tidy_omic with some features and/or samples filtered.

Format Names for Plotting

Description

Wrap long names over multiple lines so that they will look better on plots.

Usage

format_names_for_plotting(chars, width = 40, truncate_at = 80)
format_names_for_plotting(chars, width = 40, truncate_at = 80)

Arguments

`chars`	a character vector (or a variable that can be converted to one)
`width`	Positive integer giving target line width (in number of characters). A width less than or equal to 1 will put each word on its own line.
`truncate_at`	max character length

Value

a reformatted character vector of the same length as the input.

Examples

chars <- "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer
  ac arcu semper erat porttitor egestas. Etiam sagittis, sapien at mattis."

format_names_for_plotting(chars)
chars <- "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer
  ac arcu semper erat porttitor egestas. Etiam sagittis, sapien at mattis."

format_names_for_plotting(chars)

Get Design Table

Description

Get a tabular summary of all variables.

Usage

get_design_tbl(tomic)
get_design_tbl(tomic)

Arguments

tomic

Either a tidy_omic or triple_omic object

Value

a tibble reflecting the tomic object's design.

Examples

get_design_tbl(brauer_2008_triple)
get_design_tbl(brauer_2008_triple)

Get Tomic Table

Description

Extract one of the specific tables from a tomic object

Usage

get_tomic_table(tomic, table_type)
get_tomic_table(tomic, table_type)

Arguments

tomic

Either a tidy_omic or triple_omic object

table_type

The type of table to extract from the tomic object.

tidy: one row per measurements with feature and sample attributes added. Equivalent to the $data field of a tidy omic object
measurements: one row per measurements defined a feature and sample foreign key. Equivalent to the $measurements field of a triple omic object
features: one row per feature defined by a feature primary key. Equivalent to the $features field of a triple omic object
samples: one row per sample defined by a sample primary key. Equivalent to the $samples field of a triple omic object

Value

a tibble matching the table_type of the tomic object

Examples

get_tomic_table(brauer_2008_triple, "samples")
get_tomic_table(brauer_2008_tidy, "features")
get_tomic_table(brauer_2008_triple, "samples")
get_tomic_table(brauer_2008_tidy, "features")

ggBivariate Output

Description

UI components for the ggBivariate module.

Usage

ggBivOutput(id, return_brushed_points = FALSE)
ggBivOutput(id, return_brushed_points = FALSE)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`return_brushed_points`	Return values selected on the plot

Value

A shiny UI

ggBivariate Server

Description

Server components for the ggBivariate module.

Usage

ggBivServer(id, tomic, plot_table, return_brushed_points = FALSE)
ggBivServer(id, tomic, plot_table, return_brushed_points = FALSE)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`tomic`	Either a `tidy_omic` or `triple_omic` object
`plot_table`	table containing the data to be plotted
`return_brushed_points`	Return values selected on the plot

Value

a tomic_table if return_brushed_points is TRUE, and 0 otherwise

ggplot Output

Description

UI components for the ggplot module.

Usage

ggplotOutput(
  id,
  default_data_type = "samples",
  default_plot_type = "univariate"
)
ggplotOutput(
  id,
  default_data_type = "samples",
  default_plot_type = "univariate"
)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`default_data_type`	Default data type selection
`default_plot_type`	Default plot type selection

Value

A shiny UI

ggplot Server

Description

Server components for the ggplot module.

Usage

ggplotServer(id, tomic, return_brushed_points = FALSE)
ggplotServer(id, tomic, return_brushed_points = FALSE)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`tomic`	Either a `tidy_omic` or `triple_omic` object
`return_brushed_points`	Return values selected on the plot

Value

a tibble of selected observations if return_brushed_points is TRUE. Otherwise, returns NULL.

ggUnivariate Output

Description

UI components for the ggUnivariate module.

Usage

ggUnivOutput(id, return_brushed_points = FALSE)
ggUnivOutput(id, return_brushed_points = FALSE)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`return_brushed_points`	Return values selected on the plot

Value

A shiny UI

ggUnivariate Server

Description

Server components for the ggUnivariate module

Usage

ggUnivServer(id, tomic, plot_table, return_brushed_points = FALSE)
ggUnivServer(id, tomic, plot_table, return_brushed_points = FALSE)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`tomic`	Either a `tidy_omic` or `triple_omic` object
`plot_table`	table containing the data to be plotted
`return_brushed_points`	Return values selected on the plot

Value

a tomic_table if return_brushed_points is TRUE, and 0 otherwise.

Hierarchical clustering order

Description

Format and hierarchically cluster a data.frame. If hclust could not normally be produced (usually because no samples are in common for a feature) pad the matrix with zeros and still calculate the distance

Usage

hclust_order(
  df,
  feature_pk,
  sample_pk,
  value_var,
  cluster_dim,
  distance_measure = "dist",
  hclust_method = "ward.D2"
)
hclust_order(
  df,
  feature_pk,
  sample_pk,
  value_var,
  cluster_dim,
  distance_measure = "dist",
  hclust_method = "ward.D2"
)

Arguments

`df`	data.frame to cluster
`feature_pk`	variable uniquely defining a row
`sample_pk`	variable uniquely defining a sample
`value_var`	An abundance value to use with `hclust`
`cluster_dim`	rows, columns, or both
`distance_measure`	variable to use for computing dis-similarity corr pearson correlation dist euclidean distance
`hclust_method`	method from stats::hclust to use for clustering

Value

a list containing a hierarchically clustered set of rows and/or columns

Examples


library(dplyr)

df <- tidyr::crossing(letters = LETTERS, numbers = 1:10) %>%
  mutate(noise = rnorm(n()))
hclust_order(df, "letters", "numbers", "noise", "rows")
library(dplyr)

df <- tidyr::crossing(letters = LETTERS, numbers = 1:10) %>%
  mutate(noise = rnorm(n()))
hclust_order(df, "letters", "numbers", "noise", "rows")

Impute Missing Values

Description

Impute missing values using K-nearest neighbors imputation

Usage

impute_missing_values(
  tomic,
  impute_var_name = "imputed",
  value_var = NULL,
  ...
)
impute_missing_values(
  tomic,
  impute_var_name = "imputed",
  value_var = NULL,
  ...
)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`impute_var_name`	variable to create for imputed measurements
`value_var`	An abundance value to use with `hclust`
`...`	additional arguments to pass to impute.knn

Value

A tomic object with imputed measurements.

Examples

impute_missing_values(brauer_2008_triple)

impute_missing_values(brauer_2008_triple)

Infer Tomic Table Type

Description

From a tomic_table, choose whether it reflects features, samples or measurements

Usage

infer_tomic_table_type(tomic, tomic_table)
infer_tomic_table_type(tomic, tomic_table)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`tomic_table`	A table taken from a tidy (i.e., augmented measurements) or triple omic dataset

Value

features, samples or measurements

Lasso Input

Description

UI components for the lasso module.

Usage

lassoInput(id)
lassoInput(id)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.

Value

A shiny UI

Lasso Server

Description

Take a subset of entries from a tomic table (generally selected using the lasso function) and then either filter a tomic object to these entries or tag the entries of interest with a user-specified variable.

Usage

lassoServer(id, tomic, tomic_table)
lassoServer(id, tomic, tomic_table)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`tomic`	Either a `tidy_omic` or `triple_omic` object
`tomic_table`	A table taken from a tidy (i.e., augmented measurements) or triple omic dataset

Value

A tomic object amended based on the lasso selection.

Organize Input

Description

UI components for the organize input module.

Usage

organizeInput(id)
organizeInput(id)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.

Value

A shiny UI

Organize Servers

Description

Server components for the organize input module.

Usage

organizeServer(id, tidy_omic, feature_vars, sample_vars, value_var)
organizeServer(id, tidy_omic, feature_vars, sample_vars, value_var)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`tidy_omic`	an object of class tidy_omic produced by `create_tidy_omic`
`feature_vars`	variables available for arranging features
`sample_vars`	variables available for arrange samples
`value_var`	An abundance value to use with `hclust`

Value

A tomic with sorted features and/or samples.

Bivariate Plot

Description

Create a scatter or boxplot from a tomic dataset.

Usage

plot_bivariate(
  tomic_table,
  x_var,
  y_var,
  color_var = NULL,
  shape_var = NULL,
  alpha_var = NULL,
  size_var = NULL
)
plot_bivariate(
  tomic_table,
  x_var,
  y_var,
  color_var = NULL,
  shape_var = NULL,
  alpha_var = NULL,
  size_var = NULL
)

Arguments

`tomic_table`	A table taken from a tidy (i.e., augmented measurements) or triple omic dataset
`x_var`	x-axis variable
`y_var`	y-axis variable
`color_var`	coloring variable (NULL to suppress coloring)
`shape_var`	shape variable (NULL to suppress shape)
`alpha_var`	alpha variable or numeric for constant alpha (NULL to suppress alpha)
`size_var`	size variable or integer/numeric for constant size (NULL to suppress size)

Value

a ggplot2 grob

Examples

library(dplyr)

brauer_augmented <- brauer_2008_tidy %>%
  add_pcs(npcs = 5) %>%
  tomic_to("triple_omic")

tomic_table <- brauer_augmented$samples
plot_bivariate(tomic_table, "PC1", "PC2", "nutrient", "nutrient", 0.5, 10)
plot_bivariate(tomic_table, "PC1", "PC2", NULL)
plot_bivariate(tomic_table, "nutrient", "PC2", "nutrient")
library(dplyr)

brauer_augmented <- brauer_2008_tidy %>%
  add_pcs(npcs = 5) %>%
  tomic_to("triple_omic")

tomic_table <- brauer_augmented$samples
plot_bivariate(tomic_table, "PC1", "PC2", "nutrient", "nutrient", 0.5, 10)
plot_bivariate(tomic_table, "PC1", "PC2", NULL)
plot_bivariate(tomic_table, "nutrient", "PC2", "nutrient")

Plot Heatmap

Description

Generate a heatmap visualization of a features x samples matrix of measurements.

Usage

plot_heatmap(
  tomic,
  feature_var = NULL,
  sample_var = NULL,
  value_var = NULL,
  cluster_dim = "both",
  distance_measure = "dist",
  hclust_method = "ward.D2",
  change_threshold = Inf,
  plot_type = "grob",
  max_display_features = 800,
  x_label = NULL,
  y_label = NULL,
  colorbar_label = NULL
)
plot_heatmap(
  tomic,
  feature_var = NULL,
  sample_var = NULL,
  value_var = NULL,
  cluster_dim = "both",
  distance_measure = "dist",
  hclust_method = "ward.D2",
  change_threshold = Inf,
  plot_type = "grob",
  max_display_features = 800,
  x_label = NULL,
  y_label = NULL,
  colorbar_label = NULL
)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`feature_var`	variable from "features" to use as a unique feature label.
`sample_var`	variable from "samples" to use as a unique sample label.
`value_var`	which variable in "measurements" to use for quantification.
`cluster_dim`	rows, columns, or both
`distance_measure`	variable to use for computing dis-similarity corr pearson correlation dist euclidean distance
`hclust_method`	method from stats::hclust to use for clustering
`change_threshold`	values with a more extreme absolute change will be thresholded to this value.
`plot_type`	plotly (for interactivity) or grob (for a static ggplot)
`max_display_features`	aggregate and downsample distinct feature to this number to speed to up heatmap rendering.
`x_label`	label for x-axis (if NULL then use `feature_var`)
`y_label`	label for y-axis (if NULL then use `sample_var`)
`colorbar_label`	label for color-bar; default is log2 abundance

Value

a ggplot2 grob

Examples


library(dplyr)

tomic <- brauer_2008_triple %>%
  filter_tomic(
    filter_type = "category",
    filter_table = "features",
    filter_variable = "BP",
    filter_value = c(
      "protein biosynthesis",
      "rRNA processing", "response to stress"
    )
  )

plot_heatmap(
  tomic = tomic,
  value_var = "expression",
  change_threshold = 5,
  cluster_dim = "rows",
  plot_type = "grob",
  distance_measure = "corr"
)
library(dplyr)

tomic <- brauer_2008_triple %>%
  filter_tomic(
    filter_type = "category",
    filter_table = "features",
    filter_variable = "BP",
    filter_value = c(
      "protein biosynthesis",
      "rRNA processing", "response to stress"
    )
  )

plot_heatmap(
  tomic = tomic,
  value_var = "expression",
  change_threshold = 5,
  cluster_dim = "rows",
  plot_type = "grob",
  distance_measure = "corr"
)

Univariate Plot

Description

Create a histogram from a tomic dataset.

Usage

plot_univariate(tomic_table, x_var, color_var = NULL)
plot_univariate(tomic_table, x_var, color_var = NULL)

Arguments

`tomic_table`	A table taken from a tidy (i.e., augmented measurements) or triple omic dataset
`x_var`	x-axis variable
`color_var`	coloring variable (NULL to suppress coloring)

Value

A ggplot2 grob

Examples

library(dplyr)

brauer_augmented <- brauer_2008_tidy %>%
  add_pcs(npcs = 5) %>%
  tomic_to("triple_omic")

plot_univariate(brauer_augmented$samples, "PC1", "nutrient")
plot_univariate(brauer_augmented$measurements, "expression", NULL)
library(dplyr)

brauer_augmented <- brauer_2008_tidy %>%
  add_pcs(npcs = 5) %>%
  tomic_to("triple_omic")

plot_univariate(brauer_augmented$samples, "PC1", "nutrient")
plot_univariate(brauer_augmented$measurements, "expression", NULL)

Plot Saver Input

Description

UI components for the plot saver module.

Usage

plotsaverInput(id, ui_format = "tall")
plotsaverInput(id, ui_format = "tall")

Arguments

id

An ID string that corresponds with the ID used to call the module's UI function.

ui_format

Set UI appearance

tall: stack all UI elements
wide: UI elements are side-by-side

Value

a shiny UI

Plot Saver Server

Description

Server components for the plot saver module.

Usage

plotsaverServer(id, grob, filename = "grob.png")
plotsaverServer(id, grob, filename = "grob.png")

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`grob`	a ggplot2 plot
`filename`	filename for saving plot. The extension will be respected by ggsave.

Value

None

Prepare Example Datasets

Description

Format example datasets and add them to the package.

Usage

prepare_example_datasets(seed = 1234)
prepare_example_datasets(seed = 1234)

Arguments

seed

a seed value used to reproducibly sample random genes.

Value

None; used for side-effects.

Reconcile Triple Omic

Description

If some samples, feature or measurements have been dropped; update other tables.

Usage

reconcile_triple_omic(triple_omic)
reconcile_triple_omic(triple_omic)

Arguments

triple_omic

an object of class triple_omic produced by create_triple_omic

Value

a triple_omic object

Remove Missing Values

Description

Account for missing values by dropping features, samples or using imputation.

Usage

remove_missing_values(
  tomic,
  value_var = NULL,
  missing_val_method = "drop_samples",
  verbose = TRUE
)
remove_missing_values(
  tomic,
  value_var = NULL,
  missing_val_method = "drop_samples",
  verbose = TRUE
)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`value_var`	An abundance value to use with `hclust`
`missing_val_method`	Approach to remove missing values: drop_features Drop features with missing values drop_samples Drop samples which are missing all features, then drop features impute Impute missing values
`verbose`	extra reporting messages

Value

A tomic object where missing values have been accounted for.

Examples

remove_missing_values(brauer_2008_triple)

remove_missing_values(brauer_2008_triple)

Shiny Filter Test

Description

Tests the shiny filter module as a stand-alone application.

Usage

shiny_filter_test(tidy_omic, filter_table = "features")
shiny_filter_test(tidy_omic, filter_table = "features")

Arguments

`tidy_omic`	an object of class tidy_omic produced by `create_tidy_omic`
`filter_table`	table to filter

Value

A shiny app

Examples

if (interactive()) {
  shiny_filter_test(brauer_2008_tidy)
}
if (interactive()) {
  shiny_filter_test(brauer_2008_tidy)
}

Shiny ggBivariate Test

Description

Test the shiny ggBivariate module as a stand-alone application.

Usage

shiny_ggbiv_test(tomic, plot_table = "samples")
shiny_ggbiv_test(tomic, plot_table = "samples")

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`plot_table`	table containing the data to be plotted

Value

a shiny app

Examples


if (interactive()) {
  shiny_ggbiv_test(
    add_pcs(brauer_2008_triple, npcs = 5),
    plot_table = "samples"
  )
  shiny_ggbiv_test(
    brauer_2008_triple,
    plot_table = "measurements"
  )
}
if (interactive()) {
  shiny_ggbiv_test(
    add_pcs(brauer_2008_triple, npcs = 5),
    plot_table = "samples"
  )
  shiny_ggbiv_test(
    brauer_2008_triple,
    plot_table = "measurements"
  )
}

Shiny ggplot Test

Description

Test the shiny ggplot module as a stand-alone application.

Usage

shiny_ggplot_test(tomic)
shiny_ggplot_test(tomic)

Arguments

tomic

Either a tidy_omic or triple_omic object

Value

A shiny app

Examples


if (interactive()) {
  shiny_ggplot_test(add_pcs(brauer_2008_triple, npcs = 5))
  shiny_ggplot_test(brauer_2008_triple)
}
if (interactive()) {
  shiny_ggplot_test(add_pcs(brauer_2008_triple, npcs = 5))
  shiny_ggplot_test(brauer_2008_triple)
}

Shiny ggUnivariate Test

Description

Test the shiny ggUnivariate module as a stand-alone application.

Usage

shiny_gguniv_test(tomic, plot_table = "samples")
shiny_gguniv_test(tomic, plot_table = "samples")

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`plot_table`	table containing the data to be plotted

Value

A shiny app

Examples


if (interactive()) {
  shiny_gguniv_test(
    add_pcs(brauer_2008_triple, npcs = 5),
    plot_table = "samples"
  )
  shiny_gguniv_test(brauer_2008_triple, plot_table = "measurements")
  shiny_gguniv_test(brauer_2008_triple, plot_table = "features")
}
if (interactive()) {
  shiny_gguniv_test(
    add_pcs(brauer_2008_triple, npcs = 5),
    plot_table = "samples"
  )
  shiny_gguniv_test(brauer_2008_triple, plot_table = "measurements")
  shiny_gguniv_test(brauer_2008_triple, plot_table = "features")
}

Shiny Lasso Test

Description

Tests the shiny lasso module as a stand-alone application.

Usage

shiny_lasso_test(tomic, tomic_table)
shiny_lasso_test(tomic, tomic_table)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`tomic_table`	A table taken from a tidy (i.e., augmented measurements) or triple omic dataset

Value

A shiny app

Examples

if (interactive()) {
  tomic <- brauer_2008_triple
  tomic_table <- tomic[["samples"]] %>% dplyr::filter(nutrient == "G")
  shiny_lasso_test(tomic, tomic_table)
}
if (interactive()) {
  tomic <- brauer_2008_triple
  tomic_table <- tomic[["samples"]] %>% dplyr::filter(nutrient == "G")
  shiny_lasso_test(tomic, tomic_table)
}

Shiny Lasso Test w/ Reactive Values

Description

Tests the shiny lasso module as a stand-alone application when the tomic is a reativeVal.

Usage

shiny_lasso_test_reactval(tomic, tomic_table)
shiny_lasso_test_reactval(tomic, tomic_table)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`tomic_table`	A table taken from a tidy (i.e., augmented measurements) or triple omic dataset

Value

A shiny app

Examples

if (interactive()) {
  tomic <- brauer_2008_triple
  tomic_table <- tomic[["samples"]] %>% dplyr::filter(nutrient == "G")
  shiny_lasso_test_reactval(tomic, tomic_table)

  tomic_table <- tomic[["measurements"]] %>% dplyr::filter(expression < -3)
  shiny_lasso_test_reactval(tomic, tomic_table)
}
if (interactive()) {
  tomic <- brauer_2008_triple
  tomic_table <- tomic[["samples"]] %>% dplyr::filter(nutrient == "G")
  shiny_lasso_test_reactval(tomic, tomic_table)

  tomic_table <- tomic[["measurements"]] %>% dplyr::filter(expression < -3)
  shiny_lasso_test_reactval(tomic, tomic_table)
}

Shiny Organize Test

Description

Tests the shiny organization module as stand-alone application.

Usage

shiny_organize_test(tidy_omic, feature_vars, sample_vars, value_var)
shiny_organize_test(tidy_omic, feature_vars, sample_vars, value_var)

Arguments

`tidy_omic`	an object of class tidy_omic produced by `create_tidy_omic`
`feature_vars`	variables available for arranging features
`sample_vars`	variables available for arrange samples
`value_var`	An abundance value to use with `hclust`

Value

a shiny app

Examples


if (interactive()) {
  shiny_organize_test(
    brauer_2008_tidy,
    feature_vars = c("BP", "MF"),
    sample_vars = c("sample", "nutrient", "DR"),
    value_var = "expression"
  )
}
if (interactive()) {
  shiny_organize_test(
    brauer_2008_tidy,
    feature_vars = c("BP", "MF"),
    sample_vars = c("sample", "nutrient", "DR"),
    value_var = "expression"
  )
}

Shiny Plot Saver Test

Description

Test the shiny plotsaver module as a stand-alone application.

Usage

shiny_plotsaver_test()
shiny_plotsaver_test()

Value

a shiny app

Examples


if (interactive()) {
  shiny_plotsaver_test()
}
if (interactive()) {
  shiny_plotsaver_test()
}

Shiny Sort Test

Description

Test the shiny sorting module as a stand-alone app.

Usage

shiny_sort_test(triple_omic, valid_sort_vars, value_var)
shiny_sort_test(triple_omic, valid_sort_vars, value_var)

Arguments

`triple_omic`	an object of class triple_omic produced by `create_triple_omic`
`valid_sort_vars`	variables available for categorical arranging
`value_var`	An abundance value to use with `hclust`

Value

a shiny app

Examples


if (interactive()) {
  shiny_sort_test(brauer_2008_triple,
    valid_sort_vars = c("sample", "nutrient", "DR"),
    value_var = "expression"
  )
}
if (interactive()) {
  shiny_sort_test(brauer_2008_triple,
    valid_sort_vars = c("sample", "nutrient", "DR"),
    value_var = "expression"
  )
}

Sort Triple Omic

Description

Sort a dataset's features or samples

Usage

sort_tomic(
  tomic,
  sort_type,
  sort_table,
  sort_variables = NULL,
  value_var = NULL
)
sort_tomic(
  tomic,
  sort_type,
  sort_table,
  sort_variables = NULL,
  value_var = NULL
)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`sort_type`	hclust Arrange samples by hierarchical clustering of a provided `value_var` arrange Arrange samples by the factor or alphanumeric ordering of a set of `sort_variables`
`sort_table`	samples or features
`sort_variables`	A set of attributes in sort_table to sort with in `arrange`.
`value_var`	An abundance value to use with `hclust`

Details

sort_tomic supports the reordering of features or samples using either hierarchical clustering or based on the levels of other variables. Sorting occurs by turning either the feature or sample primary key into a factor whose levels reflect the sort.

Value

A tomic object where feature or sample primary keys have been turned into a factor reflecting how they are sorted.

Examples


library(dplyr)

sort_tomic(brauer_2008_triple,
  sort_type = "arrange", sort_table = "samples",
  sort_variables = c("nutrient", "DR")
) %>%
  sort_tomic(
    sort_type = "hclust",
    sort_table = "features",
    value_var = "expression"
  )
library(dplyr)

sort_tomic(brauer_2008_triple,
  sort_type = "arrange", sort_table = "samples",
  sort_variables = c("nutrient", "DR")
) %>%
  sort_tomic(
    sort_type = "hclust",
    sort_table = "features",
    value_var = "expression"
  )

Sort Triple Arrange

Description

Sort a triple_omic object based on the values of one or more variables.

Usage

sort_triple_arrange(triple_omic, sort_table, sort_variables)
sort_triple_arrange(triple_omic, sort_table, sort_variables)

Arguments

`triple_omic`	an object of class triple_omic produced by `create_triple_omic`
`sort_table`	samples or features
`sort_variables`	A set of attributes in sort_table to sort with in `arrange`.

Value

A triple_omic with sorted features or samples.

Sort Triple Hclust

Description

Sort a triple_omic object using hierarchical clustering

Usage

sort_triple_hclust(triple_omic, sort_table, value_var)
sort_triple_hclust(triple_omic, sort_table, value_var)

Arguments

`triple_omic`	an object of class triple_omic produced by `create_triple_omic`
`sort_table`	samples or features
`value_var`	An abundance value to use with `hclust`

Value

A triple_omic with clustered features or samples.

Sort Input

Description

UI components for the sort module.

Usage

sortInput(id, sort_table)
sortInput(id, sort_table)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`sort_table`	table to sort

Value

A shiny UI

Sort Server

Description

Server components for the sort module.

Usage

sortServer(id, tomic, sort_table, valid_sort_vars = NULL, value_var = NULL)
sortServer(id, tomic, sort_table, valid_sort_vars = NULL, value_var = NULL)

Arguments

`id`	An ID string that corresponds with the ID used to call the module's UI function.
`tomic`	Either a `tidy_omic` or `triple_omic` object
`sort_table`	samples or features
`valid_sort_vars`	variables available for categorical arranging
`value_var`	An abundance value to use with `hclust`

Value

A sorted tomic object.

Tidy omic to triple omic

Description

Convert a tidy_omic object into a triple_omic object.

Usage

tidy_to_triple(tidy_omic)
tidy_to_triple(tidy_omic)

Arguments

tidy_omic

an object of class tidy_omic produced by create_tidy_omic

Details

The data table will be converted into features, samples, and measurements tables using the design to determine which variables belong in each table. The design will be preserved as-is.

Value

A triple_omic object as created by create_triple_omic

Examples

tidy_to_triple(brauer_2008_tidy)
tidy_to_triple(brauer_2008_tidy)

T* Omic Sort Status

Description

Determine whether features &/or samples have been sorted and stored as ordered_featureId and ordered_sampleId.

Usage

tomic_sort_status(tomic)
tomic_sort_status(tomic)

Arguments

tomic

Either a tidy_omic or triple_omic object

Value

length 1 character string indicating whether the tomic is sorted.

Examples


tomic_sort_status(brauer_2008_tidy)
tomic_sort_status(brauer_2008_tidy)

T* Omic To

Description

Takes in any romic reprsentation of a dataset and returns a specific representation.

Usage

tomic_to(tomic, to_class)
tomic_to(tomic, to_class)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`to_class`	The class to return, either `tidy_omic` or `triple_omic`

Value

tomic transformed to to_class class (or un-transformed if it started that way).

Examples


tomic_to(brauer_2008_tidy, "triple_omic")
tomic_to(brauer_2008_tidy, "triple_omic")

Triple Omic to Tidy Omic

Description

Convert a triple_omic object into a tidy_omic oobject.

Usage

triple_to_tidy(triple_omic)
triple_to_tidy(triple_omic)

Arguments

triple_omic

an object of class triple_omic produced by create_triple_omic

Details

Features, samples and measurements will be merged into a single data table, and the design will be preserved as-is.

Value

A tidy_omic object as created by create_tidy_omic.

Examples


library(dplyr)

measurement_df <- tidyr::expand_grid(
  feature_id = 1:10,
  sample_id = LETTERS[1:5]
) %>%
  dplyr::mutate(value = rnorm(n()))

feature_df <- tibble(
  feature_id = 1:10,
  feature_group = rep(c("a", "b"), each = 5)
)
sample_df <- tibble(
  sample_id = LETTERS[1:5],
  sample_group = c("a", "a", "b", "b", "b")
)

triple_omic <- create_triple_omic(
  measurement_df, feature_df, sample_df,
  "feature_id", "sample_id"
)
triple_to_tidy(triple_omic)
library(dplyr)

measurement_df <- tidyr::expand_grid(
  feature_id = 1:10,
  sample_id = LETTERS[1:5]
) %>%
  dplyr::mutate(value = rnorm(n()))

feature_df <- tibble(
  feature_id = 1:10,
  feature_group = rep(c("a", "b"), each = 5)
)
sample_df <- tibble(
  sample_id = LETTERS[1:5],
  sample_group = c("a", "a", "b", "b", "b")
)

triple_omic <- create_triple_omic(
  measurement_df, feature_df, sample_df,
  "feature_id", "sample_id"
)
triple_to_tidy(triple_omic)

Try brushedPoints

Description

This function wraps brushedPoints in a try statement to catch cases where the brushing is out-of-sync with the df that is selected.

Usage

try_brushedPoints(...)
try_brushedPoints(...)

Arguments

...

args to pass to brushedPoints

Value

a df of brushed points

Update Tidy Omic

Description

Update a Tidy 'Omics data and schema to reflect newly added fields.

Usage

update_tidy_omic(tidy_omic, updated_tidy_data, new_variable_tables = c())
update_tidy_omic(tidy_omic, updated_tidy_data, new_variable_tables = c())

Arguments

`tidy_omic`	an object of class tidy_omic produced by `create_tidy_omic`
`updated_tidy_data`	a tibble of data to use to update `tidy_omic`.
`new_variable_tables`	a named character vector of newly added variables in `updated_tidy_data` (names) and the table features, samples, measurements they apply to (values).

Value

a tidy_omic object with an updated schema and/or data.

Examples


library(dplyr)

tidy_omic <- brauer_2008_tidy
updated_tidy_data <- tidy_omic$data %>%
  mutate(new_sample_var = "foo") %>%
  select(-DR)
new_variable_tables <- c("new_sample_var" = "samples")
library(dplyr)

tidy_omic <- brauer_2008_tidy
updated_tidy_data <- tidy_omic$data %>%
  mutate(new_sample_var = "foo") %>%
  select(-DR)
new_variable_tables <- c("new_sample_var" = "samples")

Update T* Omic

Description

Provide an updated features, samples or measurements table to a tomic.

Usage

update_tomic(tomic, tomic_table)
update_tomic(tomic, tomic_table)

Arguments

`tomic`	Either a `tidy_omic` or `triple_omic` object
`tomic_table`	A table taken from a tidy (i.e., augmented measurements) or triple omic dataset

Value

A tomic object with updated features, samples or measurements.

Examples


library(dplyr)
updated_features <- brauer_2008_triple$features %>%
  dplyr::filter(BP == "biological process unknown") %>%
  dplyr::mutate(chromosome = purrr::map_int(systematic_name, function(x) {
    which(LETTERS == stringr::str_match(x, "Y([A-Z])")[2])
  }))

update_tomic(brauer_2008_triple, updated_features)
library(dplyr)
updated_features <- brauer_2008_triple$features %>%
  dplyr::filter(BP == "biological process unknown") %>%
  dplyr::mutate(chromosome = purrr::map_int(systematic_name, function(x) {
    which(LETTERS == stringr::str_match(x, "Y([A-Z])")[2])
  }))

update_tomic(brauer_2008_triple, updated_features)

Var Partial Match

Description

Partial string matching of a provided variable to the variables available in a table

Usage

var_partial_match(x, df)
var_partial_match(x, df)

Arguments

`x`	a variable name or regex match to a variable name
`df`	a data.frame or tibble

Value

a single variable from df

Package 'romic'

Help Index

Add PCA Loadings

Description

Usage

Arguments

Value

Examples

Flow

Description

Usage

Arguments

Value

Examples

Interactive Heatmap

Description

Usage

Arguments

Value

Examples

PC Plot

Description

Usage

Arguments

Value

Examples

Brauer 2008

Description

Usage

Format

Details

Source

Center T* Omic

Description

Usage

Arguments

Value

Examples

Check Tidy Omic

Description

Usage

Arguments

Value

Check T*Omic

Description

Usage

Arguments

Value

Examples

Check Triple Omic

Description

Usage

Arguments

Value

Convert Wide to Tidy Omic

Description

Usage

Arguments

Value

Examples

Create Tidy Omic

Description

Usage

Arguments

Value

Examples

Create Triple Omic

Description

Usage

Arguments

Details

Value

Examples

Downsample Heatmap

Description

Usage

Arguments

Value

Export T*Omic in Tidy Format

Description