Type:	Package
Title:	R for High-Dimensional Omic Data
Version:	1.1.3
Maintainer:	Sean Hackett <sean@calicolabs.com>
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.
Depends:	R (≥ 3.2.3)
Imports:	checkmate, cli, dplyr, ggplot2, glue, purrr, readr, reshape2, rlang, shiny (≥ 1.5.0), stringr, tibble, tidyr (≥ 1.0.0)
Suggests:	knitr, impute, lazyeval, plotly, rmarkdown, usethis, testthat (≥ 3.0.0)
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.2.3
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2023-09-21 04:48:49 UTC; sean
Author:	Sean Hackett [aut, cre], Calico Life Sciences LLC [cph, fnd]
Repository:	CRAN
Date/Publication:	2023-09-21 05:40:02 UTC

romic: R for High-Dimensional Omic Data

Description

'romic' 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.

Author(s)

Maintainer: Sean Hackett sean@calicolabs.com (ORCID)

Other contributors:

• Calico Life Sciences LLC [copyright holder, funder]

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
)

Arguments

Value

A tomic object with principal components added to samples.

Examples

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)

Arguments

Value

A shiny app

Examples

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)

Arguments

Value

A shiny app

Examples

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)

Arguments

Value

A shiny app

Examples

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

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")

Arguments

Value

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

Examples

center_tomic(brauer_2008_tidy)

Check Tidy Omic

Description

Check a tidy omic dataset for consistency between the data and design and validate that the dataset follows the tidy_omic/tomic specification.

Usage

check_tidy_omic(tidy_omic, fast_check = TRUE)

Arguments

Value

0 invisibly

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)

Arguments

Value

0 invisibly

Examples

check_tomic(brauer_2008_triple)

Check Triple Omic

Description

Check a triple omic dataset for consistency between the data and design and validate that the dataset follows the triple_omic/tomic specification.

Usage

check_triple_omic(triple_omic, fast_check = TRUE)

Arguments

Value

0 invisibly

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
)

Arguments

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")
)

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
)

Arguments

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"
)

Create Triple Omic

Description

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"
)

Arguments

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"
)

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
)

Arguments

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)

Arguments

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")
}

Export T*Omic as Triple

Description

Export features, samples and measurements tables

Usage

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

Arguments

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")
}

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
)

Arguments

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")
}

Filter Input

Description

UI components for the filter module.

Usage

filterInput(id, filter_table)

Arguments

Value

A shiny UI

Filter Server

Description

Server components for the filter module.

Usage

filterServer(id, tidy_omic, filter_table)

Arguments

Value

A tidy_omic with some features and/or samples filtered.

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
)

Arguments

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")
)

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)

Arguments

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)

Get Design Table

Description

Get a tabular summary of all variables.

Usage

get_design_tbl(tomic)

Arguments

Value

a tibble reflecting the tomic object's design.

Examples

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)

Arguments

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")

ggBivariate Output

Description

UI components for the ggBivariate module.

Usage

ggBivOutput(id, return_brushed_points = FALSE)

Arguments

Value

A shiny UI

ggBivariate Server

Description

Server components for the ggBivariate module.

Usage

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

Arguments

Value

a tomic_table if return_brushed_points is TRUE, and 0 otherwise

ggUnivariate Output

Description

UI components for the ggUnivariate module.

Usage

ggUnivOutput(id, return_brushed_points = FALSE)

Arguments

Value

A shiny UI

ggUnivariate Server

Description

Server components for the ggUnivariate module

Usage

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

Arguments

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"
)

Arguments

Value

A shiny UI

ggplot Server

Description

Server components for the ggplot module.

Usage

ggplotServer(id, tomic, return_brushed_points = FALSE)

Arguments

Value

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

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"
)

Arguments

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")

Impute Missing Values

Description

Impute missing values using K-nearest neighbors imputation

Usage

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

Arguments

Value

A tomic object with imputed measurements.

Examples

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)

Arguments

Value

features, samples or measurements

Lasso Input

Description

UI components for the lasso module.

Usage

lassoInput(id)

Arguments

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)

Arguments

Value

A tomic object amended based on the lasso selection.

Organize Input

Description

UI components for the organize input module.

Usage

organizeInput(id)

Arguments

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)

Arguments

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
)

Arguments

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")

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
)

Arguments

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"
)

Univariate Plot

Description

Create a histogram from a tomic dataset.

Usage

plot_univariate(tomic_table, x_var, color_var = NULL)

Arguments

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)

Plot Saver Input

Description

UI components for the plot saver module.

Usage

plotsaverInput(id, ui_format = "tall")

Arguments

Value

a shiny UI

Plot Saver Server

Description

Server components for the plot saver module.

Usage

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

Arguments

Value

None

Prepare Example Datasets

Description

Format example datasets and add them to the package.

Usage

prepare_example_datasets(seed = 1234)

Arguments

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)

Arguments

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
)

Arguments

Value

A tomic object where missing values have been accounted for.

Examples

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")

Arguments

Value

A shiny app

Examples

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")

Arguments

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"
  )
}

Shiny ggplot Test

Description

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

Usage

shiny_ggplot_test(tomic)

Arguments

Value

A shiny app

Examples

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")

Arguments

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")
}

Shiny Lasso Test

Description

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

Usage

shiny_lasso_test(tomic, tomic_table)

Arguments

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)
}

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)

Arguments

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)
}

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)

Arguments

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"
  )
}

Shiny Plot Saver Test

Description

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

Usage

shiny_plotsaver_test()

Value

a shiny app

Examples

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)

Arguments

Value

a shiny app

Examples

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

Sort Input

Description

UI components for the sort module.

Usage

sortInput(id, sort_table)

Arguments

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)

Arguments

Value

A sorted tomic object.

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
)

Arguments

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"
  )

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)

Arguments

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)

Arguments

Value

A triple_omic with clustered features or samples.

Tidy omic to triple omic

Description

Convert a tidy_omic object into a triple_omic object.

Usage

tidy_to_triple(tidy_omic)

Arguments

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)

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)

Arguments

Value

length 1 character string indicating whether the tomic is sorted.

Examples

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)

Arguments

Value

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

Examples

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)

Arguments

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)

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(...)

Arguments

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())

Arguments

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")

Update T* Omic

Description

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

Usage

update_tomic(tomic, tomic_table)

Arguments

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)

Var Partial Match

Description

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

Usage

var_partial_match(x, df)

Arguments

Value

a single variable from df