Package 'gDRutils'

Description

Set fit parameters for an invalid fit.

Usage

.set_invalid_fit_params(out, norm_values)

Arguments

Value

Modified named list of fit parameters.

Examples

.set_invalid_fit_params(list(), norm_values = rep(0.3, 6))

Description

Modify and object's class attribute.

Usage

addClass(x, newClass)

Arguments

Details

This is a simple convenience function that an item to the class attribute of an object so that it can be dispatched to a proper S3 method. This is purely for code clarity, so that individual methods do not clutter the definitions of higher order functions.

Value

The same object with an added S3 class.

Examples

addClass(data.table::data.table(), "someClass")

Description

Aggregation can only be performed on nested variables.

Usage

aggregate_assay(asy, by, FUN)

Arguments

Value

A BumpyMatrix object aggregated by FUN.

Examples

mae <- get_synthetic_data("finalMAE_small") 
se <- mae[[1]]
assay <- SummarizedExperiment::assay(se)
aggregate_assay(assay, FUN = mean, by = c("Barcode"))

Description

Apply a user-specified function to every element of a bumpy matrix.

Usage

apply_bumpy_function(
  se,
  FUN,
  req_assay_name,
  out_assay_name,
  parallelize = FALSE,
  ...
)

Arguments

Value

The original se object with a new assay, out_assay_name.

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
se <- mae[[1]]
FUN <- function(x) {
  data.table::data.table(Concentration = x$Concentration, CorrectedReadout = x$CorrectedReadout)
} 
apply_bumpy_function(
  se, 
  FUN = FUN, 
  req_assay_name = "RawTreated", 
  out_assay_name = "CorrectedReadout"
)

Description

assert choices

Usage

assert_choices(x, choices, ...)

Arguments

Value

NULL

Examples

assert_choices("x", c("x","y"))

Description

Average biological replicates on the data table side.

Usage

average_biological_replicates_dt(
  dt,
  var,
  prettified = FALSE,
  fixed = TRUE,
  geometric_average_fields = get_header("metric_average_fields")$geometric_mean,
  fit_type_average_fields = get_header("metric_average_fields")$fit_type,
  blacklisted_fields = get_header("metric_average_fields")$blacklisted,
  add_sd = FALSE
)

Arguments

Value

data.table without replicates

Examples

dt <- data.table::data.table(a = c(1:10, 1),
b = c(rep("drugA", 10), rep("drugB", 1)))
average_biological_replicates_dt(dt, var = "a")

Description

This function calculates the standard deviation of a numeric vector. If the vector has a length of 1 and it is numeric, it returns 0.

Usage

calc_sd(x)

Arguments

Value

The standard deviation of the vector if its length is greater than 1 or it is not numeric, otherwise 0.

Examples

calc_sd(c(1, 2, 3, 4, 5)) # Should return the standard deviation
calc_sd(c(1)) # Should return 0
calc_sd(numeric(0)) # Should return NA
calc_sd(c("a", "b", "c")) # Should return NA

Description

Set IC50/GR50 value to Inf or -Inf based on upper and lower limits.

Usage

cap_xc50(xc50, max_conc, min_conc = NA, capping_fold = 5)

Arguments

Details

Note: xc50 and max_conc should share the same units. Ideally, the lower_cap should be based on the lowest tested concentration. However, since we don't record that, it is set 5 orders of magnitude below the highest dose.

Value

Capped IC50/GR50 value.

Examples

cap_xc50(xc50 = 1, max_conc = 2)
cap_xc50(xc50 = 2, max_conc = 5, min_conc = 1)
cap_xc50(xc50 = 26, max_conc = 5, capping_fold = 5)

Description

Convert colData to JSON format for elasticsearch indexing.

Usage

convert_colData_to_json(
  cdata,
  identifiers,
  req_cols = c("cellline", "cellline_name", "cellline_tissue", "cellline_ref_div_time")
)

Arguments

Details

Standardizes the cdata to common schema fields and tidies formatting to be condusive to joining with other JSON responses.

Value

JSON string capturing the cdata.

Examples

cdata <- data.table::data.table(
  mycellline = letters, 
  mycelllinename = letters, 
  mycelllinetissue = letters,
  cellline_ref_div_time = "cellline_ref_div_time")
identifiers <- list(cellline = "mycellline",
                    cellline_name = "mycelllinename",
                    cellline_ref_div_time = "cellline_ref_div_time",
                    cellline_tissue = "mycelllinetissue")
convert_colData_to_json(cdata, identifiers)

Description

convert combo assays from SummarizedExperiments to the list of data.tables

Usage

convert_combo_data_to_dt(
  se,
  c_assays = get_combo_assay_names(),
  normalization_type = c("RV", "GR"),
  prettify = TRUE
)

Arguments

Value

list of data.table(s) with combo data

Author(s)

Arkadiusz Gładki [email protected]

Examples

mae <- get_synthetic_data("finalMAE_combo_matrix_small.qs")
convert_combo_data_to_dt(mae[[1]])

Description

get combo assay names based on the field name

Usage

convert_combo_field_to_assay(field)

Arguments

Value

charvec

Examples

convert_combo_field_to_assay("hsa_score")

Description

Convert an assay within a SummarizedExperiment object in a MultiAssayExperiment to a long data.table.

Usage

convert_mae_assay_to_dt(
  mae,
  assay_name,
  experiment_name = NULL,
  include_metadata = TRUE,
  retain_nested_rownames = FALSE,
  wide_structure = FALSE
)

Arguments

Details

NOTE: to extract information about 'Control' data, simply call the function with the name of the assay holding data on controls.

Value

data.table representation of the data in assay_name.

Author(s)

Bartosz Czech [email protected]

See Also

flatten convert_se_assay_to_dt

Examples

mae <- get_synthetic_data("finalMAE_small")
convert_mae_assay_to_dt(mae, "Metrics")

Description

Convert a MultiAssayExperiment object to a JSON document.

Usage

convert_mae_to_json(mae, with_experiments = TRUE)

Arguments

Value

String representation of a JSON document.

Examples

mae <- get_synthetic_data("finalMAE_small")
convert_mae_to_json(mae)
convert_mae_to_json(mae, with_experiments = FALSE)

Description

Convert experiment metadata to JSON format for elasticsearch indexing.

Usage

convert_metadata_to_json(se)

Arguments

Value

JSON string capturing experiment metadata.

Examples

md <- list(title = "my awesome experiment",
  description = "description of experiment",
  sources = list(list(name = "GeneData_Screener", id = "QCS-12345")))
se <- SummarizedExperiment::SummarizedExperiment(metadata = md)
convert_metadata_to_json(se)

Description

Convert rowData to JSON format for elasticsearch indexing.

Usage

convert_rowData_to_json(
  rdata,
  identifiers,
  req_cols = c("drug", "drug_name", "drug_moa", "duration")
)

Arguments

Details

Standardizes the rdata to common schema fields and tidies formatting to be condusive to joining with other JSON responses.

Value

JSON string capturing the rdata.

Examples

rdata <- data.table::data.table(
  mydrug = letters, 
  mydrugname = letters, 
  mydrugmoa = letters, 
  Duration = 1)
identifiers <- list(drug = "mydrug", drug_name = "mydrugname", drug_moa = "mydrugmoa",
duration = "Duration")
convert_rowData_to_json(rdata, identifiers)

Description

Convert an assay within a SummarizedExperiment object to a long data.table. Then condcut some post processing steps.

Usage

convert_se_assay_to_custom_dt(se, assay_name, output_table = NULL)

Arguments

Details

Current strategy is per-assay specific.

1. combo assays: conversion to data.table only (with wide_structure = FALSE)

2. 'Metrics' assay can be converted to three types of outputs:

• Metrics_initial (conversion to data.table only, with wide_structure = FALSE)

• Metrics_raw: same as Metrics_initial followed by:

  • fix for 'EC50' and 'Metrics_rownames'

  • flatten

  • prettifying and dropping excess variables

• Metrics (same as Metrics_raw + capVals)

1. 'Normalization' and 'Averaged' assay:

• conversion to data.table (with wide_structure = TRUE)

• prettifying and dropping excess variables

NOTE: to extract information about 'Control' data, simply call the function with the name of the assay holding data on controls. To extract the reference data in to same format as 'Averaged' use convert_se_ref_assay_to_dt.

Value

data.table representation of the data in assay_name with added information from colData.

See Also

convert_se_assay_to_dt

Examples

mae <- get_synthetic_data("finalMAE_small")
se <- mae[[1]]
convert_se_assay_to_custom_dt(se, "Metrics")
convert_se_assay_to_custom_dt(se, "Metrics", output_table = "Metrics_raw")
convert_se_assay_to_custom_dt(se, "Metrics", output_table = "Metrics_initial")
convert_se_assay_to_custom_dt(se, "Averaged")

Description

Convert an assay within a SummarizedExperiment object to a long data.table.

Usage

convert_se_assay_to_dt(
  se,
  assay_name,
  include_metadata = TRUE,
  retain_nested_rownames = FALSE,
  wide_structure = FALSE
)

Arguments

Details

NOTE: to extract information about 'Control' data, simply call the function with the name of the assay holding data on controls. To extract the reference data in to same format as 'Averaged' use convert_se_ref_assay_to_dt.

Value

data.table representation of the data in assay_name.

See Also

flatten

Examples

mae <- get_synthetic_data("finalMAE_small")
se <- mae[[1]]
convert_se_assay_to_dt(se, "Metrics")

Description

Convert a SummarizedExperiment object to a JSON document.

Usage

convert_se_to_json(se)

Arguments

Value

String representation of a JSON document.

Examples

md <- list(title = "my awesome experiment",
  description = "description of experiment",
  source = list(name = "GeneData_Screener", id = "QCS-12345"))
rdata <- data.table::data.table(
 mydrug = letters, 
  mydrugname = letters, 
  mydrugmoa = letters, 
  Duration = 1)
cdata <- data.table::data.table(mycellline = letters, mycelllinename = letters,
 mycelllinetissue = letters, cellline_ref_div_time = letters)
identifiers <- list(cellline = "mycellline",
                    cellline_name = "mycelllinename",
                    cellline_tissue = "mycelllinetissue",
                    cellline_ref_div_time = "cellline_ref_div_time",
                    drug = "mydrug",
                    drug_name = "mydrugname",
                    drug_moa = "mydrugmoa",
                    duration = "Duration")
se <- SummarizedExperiment::SummarizedExperiment(rowData = rdata,
                                                 colData = cdata)
se <- set_SE_experiment_metadata(se, md)
se <- set_SE_identifiers(se, identifiers)
convert_se_to_json(se)

Description

Define matrix grid positions

Usage

define_matrix_grid_positions(conc1, conc2)

Arguments

Details

drug_1 is diluted along the rows as the y-axis and drug_2 is diluted along the columns and will be the x-axis.

Value

list with axis grid positions

Examples

cl_name <- "cellline_BC"
drug1_name <- "drug_001"
drug2_name <- "drug_026"

se <- get_synthetic_data("combo_matrix_small")[["combination"]]
dt_average <- convert_se_assay_to_dt(se, "Averaged")[normalization_type == "GR"]

ls_axes <- define_matrix_grid_positions(
   dt_average[["Concentration"]], dt_average[["Concentration_2"]])

Description

Demote a metadata field in the rowData or colData of a SummarizedExperiment object to a nested field of a BumpyMatrix assay.

Usage

demote_fields(se, fields)

Arguments

Details

Revert this operation using promote_fields.

Value

A SummarizedExperiment object with new dimensions resulting from demoting given fields to nested columns.

See Also

promote_fields

Examples

mae <- get_synthetic_data("finalMAE_small")
se <- mae[[1]]
se <- promote_fields(se, "ReadoutValue", 2)
demote_fields(se, "ReadoutValue")

Description

Convert data.table with dose-response data into a BumpyMatrix assay.

Usage

df_to_bm_assay(data, discard_keys = NULL)

Arguments

Details

The 'assay' is simply a BumpyMatrix object with rownames being the treatment ids, colnames being the ids of the cell lines and values with dose-response data for given cell lines under given conditions.

Value

BumpyMatrix object

Examples

df_to_bm_assay(data.table::data.table(Gnumber = 2, clid = "A"))

Description

extend abbreviated normalization type

Usage

extend_normalization_type_name(x)

Arguments

Value

string

Examples

extend_normalization_type_name("GR")

Description

Fit GR and RV curves from a data.table.

Usage

fit_curves(
  df_,
  series_identifiers,
  e_0 = 1,
  GR_0 = 1,
  n_point_cutoff = 4,
  range_conc = c(0.005, 5),
  force_fit = FALSE,
  pcutoff = 0.05,
  cap = 0.1,
  normalization_type = c("GR", "RV")
)

Arguments

Details

The df_ expects the following columns:

• RelativeViability normalized relative viability values (if normalization_type includes "RV")

• GRvalue normalized GR values (if normalization_type includes "GR")

The range_conc is used to calculate the x_AOC_range statistic. The purpose of this statistic is to enable comparison across different experiments with slightly different concentration ranges.

Value

data.table of fit parameters as specified by the normalization_type.

Examples

df_ <- data.table::data.table(Concentration = c(0.001, 0.00316227766016838, 
0.01, 0.0316227766016838),
x_std = c(0.1, 0.1, 0.1, 0.1), normalization_types = c("RV", "RV", "RV", "RV"),
x = c(0.9999964000144, 0.999964001439942, 0.999640143942423, 0.996414342629482))

fit_curves(df_, "Concentration", normalization_type = "RV")

Description

Flatten a stacked table into a wide format.

Usage

flatten(tbl, groups, wide_cols, sep = "_")

Arguments

Details

flattened columns will be named with original column names prefixed by wide_cols columns, concatenated together and separated by sep.

A common use case for this function is when a flattened version of the "Metrics" assay is desired.

Value

table of flattened data as defined by wide_cols.

See Also

convert_se_assay_to_dt

Examples

n <- 4
m <- 5
grid <- expand.grid(normalization_type = c("GR", "RV"),
  source = c("GDS", "GDR"))
repgrid <- data.table::rbindlist(rep(list(grid), m))
repgrid$wide <- seq(m * n)
repgrid$id <- rep(LETTERS[1:m], each = n)

groups <- colnames(grid)
wide_cols <- c("wide")

flatten(repgrid, groups = groups, wide_cols = wide_cols)

Description

Function for generating local synthetic data used for unit tests in modules

Usage

gen_synthetic_data(m = 1, n = 5)

Arguments

Value

list with drugs, cell_lines, raw_data and assay_data

Examples

gen_synthetic_data()

Description

Identify and return additional variables in list of dt

Usage

get_additional_variables(dt_list, unique = FALSE, prettified = FALSE)

Arguments

Value

vector of variable names with additional variables

Examples

dt <- data.table::data.table(
  Gnumber = seq_len(10), 
  Concentration = runif(10), 
  Ligand = c(rep(0.5, 5), rep(0, 5))
)
get_additional_variables(dt)

Description

Helper function to find duplicated rows in assay data

Usage

get_assay_dt_duplicated_rows(dt, output = "index")

Arguments

Value

integer vector or data.table with duplicated rows

Examples

sdata <- get_synthetic_data("finalMAE_small")
smetrics_data <- convert_se_assay_to_dt(sdata[[1]], "Metrics")
get_assay_dt_duplicated_rows(smetrics_data, output = "data")
get_assay_dt_duplicated_rows(smetrics_data)

Description

get assay names of the given se/dataset fetch the data from the se if provided as metadata use predefined values from get_env_assay_names otherwise

Usage

get_assay_names(se = NULL, ...)

Arguments

Value

charvec

Author(s)

Arkadiusz Gładki [email protected]

Examples

get_assay_names()

Description

get columns in the assay data required to have unique (non-duplicated) data

Usage

get_assay_req_uniq_cols(dt)

Arguments

Value

charvec with columns required to have unique data

Examples

sdata <- get_synthetic_data("finalMAE_small")
smetrics_data <- convert_se_assay_to_dt(sdata[[1]], "Metrics")
get_assay_req_uniq_cols(smetrics_data)

Description

get names of combo assays

Usage

get_combo_assay_names(se = NULL, ...)

Arguments

Value

charvec of combo assay names.

Author(s)

Arkadiusz Gładki [email protected]

Examples

get_combo_assay_names()

Description

get names of combo base assays

Usage

get_combo_base_assay_names(se = NULL, ...)

Arguments

Value

charvec

Author(s)

Arkadiusz Gładki [email protected]

Examples

get_combo_base_assay_names()

Description

get names of combo excess fields

Usage

get_combo_excess_field_names()

Value

charvec

Examples

get_combo_excess_field_names()

Description

get names of combo score assays

Usage

get_combo_score_assay_names(se = NULL, ...)

Arguments

Value

charvec

Author(s)

Arkadiusz Gładki [email protected]

Examples

get_combo_score_assay_names()

Description

get names of combo score fields

Usage

get_combo_score_field_names()

Value

charvec

Examples

get_combo_score_assay_names()

Description

Get gDR default identifiers required for downstream analysis.

Usage

get_default_identifiers()

Value

charvec

Examples

get_default_identifiers()

Description

Helper function to find duplicated rows

Usage

get_duplicated_rows(x, col_names = NULL, output = "index")

Arguments

Value

integer vector or data.table with duplicated rows

Examples

dt <- data.table::data.table(a = c(1, 2, 3), b = c(3, 2, 2))
get_duplicated_rows(dt, "b")
get_duplicated_rows(dt, "b", output = "data")

Description

get default assay names for the specified filters, i.e. set of assay types, assay groups and assay data types

Usage

get_env_assay_names(
  type = NULL,
  group = NULL,
  data_type = NULL,
  prettify = FALSE,
  simplify = TRUE
)

Arguments

Value

charvec

Author(s)

Arkadiusz Gładki [email protected]

Examples

get_env_assay_names()

Description

So far the helper is needed to handle env vars containing : for which the backslash is automatically added in some contexts and R could not get the original value for these env vars.

Usage

get_env_var(x, ...)

Arguments

Value

sanitized value of the env variable

Examples

get_env_var("HOME")

Description

Get identifiers that expect only one value for each identifier.

Usage

get_expect_one_identifiers()

Value

charvec

Examples

get_expect_one_identifiers()

Description

get experiment groups

Usage

get_experiment_groups(type = NULL)

Arguments

Value

list with experiment groups or string (if type not NULL)

Author(s)

Arkadiusz Gladki [email protected]

Examples

get_experiment_groups()

Description

Get descriptions for identifiers

Usage

get_identifiers_dt(k = NULL, get_description = FALSE, get_example = FALSE)

Arguments

Value

named list

Examples

get_identifiers_dt()

Description

Get gDR synonyms for the identifiers

Usage

get_idfs_synonyms()

Value

charvec

Examples

get_idfs_synonyms()

Description

Get isobologram column names

Usage

get_isobologram_columns(k = NULL, prettify = TRUE)

Arguments

Value

character vector of isobologram column names for combination data

Examples

get_isobologram_columns()
get_isobologram_columns("iso_level", prettify = TRUE)

Description

get the identifiers of all SE's in the MAE

Usage

get_MAE_identifiers(mae)

Arguments

Value

named list with identifiers for each SE

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
get_MAE_identifiers(mae)

Description

get non empty assays

Usage

get_non_empty_assays(mae)

Arguments

Value

charvec with non-empty experiments

Author(s)

Arkadiusz Gladki [email protected]

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
get_non_empty_assays(mae)

Description

get optional colData fields

Usage

get_optional_coldata_fields(se)

Arguments

Value

a charvec containing the names of the optional identifiers in the SE colData

Description

get optional rowData fields

Usage

get_optional_rowdata_fields(se)

Arguments

Value

a charvec containing the names of the optional identifiers in the SE rowData

Description

Get identifiers required for downstream analysis.

Usage

get_required_identifiers()

Value

charvec

Examples

get_required_identifiers()

Description

Get settings from JSON file In most common scenario the settings are stored in JSON file to avoid hardcoding

Usage

get_settings_from_json(
  s = NULL,
  json_path = system.file(package = "gDRutils", "cache.json")
)

Arguments

Value

value/values for entry/entries from JSON file

Examples

if (!nchar(system.file(package="gDRutils"))) {
   get_settings_from_json()
}

Description

get supported experiments

Usage

get_supported_experiments(type = NULL)

Arguments

Value

charvec with supported experiment name(s)

Author(s)

Arkadiusz Gladki [email protected]

Examples

get_supported_experiments()

Description

Get synthetic data from gDRtestData package

Usage

get_synthetic_data(qs)

Arguments

Value

loaded data

Examples

get_synthetic_data("finalMAE_small.qs")

Description

Function to obtain data from gDRtestData and prepare for unit tests

Usage

get_testdata()

Value

list with drugs, cell_lines, raw_data and assay_data

Examples

get_testdata()

Description

Function to obtain data from gDRtestData and prepare for unit tests

Usage

get_testdata_codilution()

Value

list with drugs, cell_lines, raw_data and assay_data

Examples

get_testdata_codilution()

Description

Function to obtain data from gDRtestData and prepare for unit tests

Usage

get_testdata_combo()

Value

list with drugs, cell_lines, raw_data and assay_data

Examples

get_testdata_combo()

Description

An auxiliary function that checks for duplicates in the assay data

Usage

has_assay_dt_duplicated_rows(dt)

Arguments

Value

logical flag indicating if a dt contains duplicated rows or not

Examples

sdata <- get_synthetic_data("finalMAE_small")
smetrics_data <- convert_se_assay_to_dt(sdata[[1]], "Metrics")
has_assay_dt_duplicated_rows(smetrics_data)

Description

An auxiliary function that checks for duplicates in the data.table (or its subset)

Usage

has_dt_duplicated_rows(dt, col_names = NULL)

Arguments

Value

logical flag indicating if a dt contains duplicated rows or not

Examples

dt <- data.table::data.table(a = c(1, 2, 3), b = c(3, 2, 2))
has_dt_duplicated_rows(dt, "b")

Description

Has Single Codrug Data

Usage

has_single_codrug_data(
  cols,
  prettify_identifiers = TRUE,
  codrug_identifiers = c("drug_name2", "concentration2")
)

Arguments

Value

logical flag

Examples

has_single_codrug_data("Drug Name")
has_single_codrug_data(c("Drug Name", "Cell Lines"))
has_single_codrug_data(c("Drug Name 2", "Concentration 2"))
has_single_codrug_data(
  get_prettified_identifiers(
    c("concentration2", "drug_name2"), 
    simplify = FALSE
  )
)

Description

Has Valid Codrug Data

Usage

has_valid_codrug_data(
  data,
  prettify_identifiers = TRUE,
  codrug_name_identifier = "drug_name2",
  codrug_conc_identifier = "concentration2"
)

Arguments

Value

logical flag

Examples

dt <-
  data.table::data.table(
    "Drug Name" = letters[seq_len(3)],
    "Concentration" = seq_len(3),
    "Drug Name 2" = letters[4:6],
    "Concentration 2" = 4:6
  )
has_valid_codrug_data(dt)

dt$`Concentration 2` <- NULL
has_valid_codrug_data(dt)

Description

Get the expected header(s) for one field or reset all header fields

Usage

get_header(k = NULL)

Arguments

Details

If get_header is called with no values, the entire available header list is returned.

Value

For get_header a character vector of headers for field k.

Examples

get_header(k = NULL)
get_header("manifest")

Description

Get, set, or reset the expected identifier(s) for one or all identifier field(s). Identifiers are used by the gDR processing functions to identify which columns in a data.table correspond to certain expected fields. Functions of the family *et_identifier will look for identifiers from the environment while functions of the family *et_SE_identifiers will look for identifiers in the metadata slot of a SummarizedExperiment object. See details for expected identifiers and their definitions.

Usage

get_env_identifiers(k = NULL, simplify = TRUE)

get_prettified_identifiers(k = NULL, simplify = TRUE)

set_env_identifier(k, v)

reset_env_identifiers()

Arguments

Details

Identifiers supported by the gDR suite include:

• "barcode": String of column name containing barcode metadata

• "cellline": String of column name containing unique, machine-readable cell line identifiers

• "cellline_name": String of column name containing human-friendly cell line names

• "cellline_tissue": String of column name containing metadata on cell line tissue type

• "cellline_ref_div_time": String of column name containing reference division time for cell lines

• "cellline_parental_identifier": String of column name containing unique, machine-readable parental cell line identifiers. Used in the case of derived or engineered cell lines.

• "drug": String of column name containing unique, machine-readable drug identifiers

• "drug_name": String of column name containing human-friendly drug names

• "drug_moa": String of column name containing metadata for drug mode of action

• "duration": String of column name containing metadata on duration that cells were treated (in hours)

• "template": String of column name containing template metadata

• "untreated_tag": Character vector of entries that identify control, untreated wells

• "well_position": Character vector of column names containing metadata on well positions on a plate

Value

For any setting or resetting functionality, a NULL invisibly. For get_env_identifiers a character vector of identifiers for field k. For functions called with no arguments, the entire available identifier list is returned.

list or charvec depends on unify param

list or charvec depends on unify param

NULL

NULL

Examples

get_env_identifiers("duration") # "Duration"

Description

Identify unique metadata fields from a list of SummarizedExperiments

Usage

identify_unique_se_metadata_fields(SElist)

Arguments

Value

character vector of unique names of metadata

Examples

mae <- get_synthetic_data("finalMAE_small")
se <- mae[[1]]
SElist <- list(
  se, 
  se
)
identify_unique_se_metadata_fields(SElist)

Description

check if any experiment is empty

Usage

is_any_exp_empty(mae)

Arguments

Value

logical

Author(s)

Arkadiusz Gladki [email protected]

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
is_any_exp_empty(mae)

Description

Checks if se is combo dataset.

Usage

is_combo_data(se)

Arguments

Value

logical

Examples

se <- get_synthetic_data("combo_matrix")[[1]]
is_combo_data(se)
se <- get_synthetic_data("combo_matrix")[[2]]
is_combo_data(se)
se <- get_synthetic_data("small")[[1]]
is_combo_data(se)

Description

check if experiment (SE) is empty

Usage

is_exp_empty(exp)

Arguments

Value

logical

Author(s)

Arkadiusz Gladki [email protected]

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
se <- mae[[1]]
is_exp_empty(se)

Description

check if all mae experiments are empty

Usage

is_mae_empty(mae)

Arguments

Value

logical

Author(s)

Arkadiusz Gladki [email protected]

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
is_mae_empty(mae)

Description

Fit a logistic curve to drug response data.

Usage

logisticFit(
  concs,
  norm_values,
  std_norm_values = NA,
  x_0 = 1,
  priors = NULL,
  lower = NULL,
  range_conc = c(0.005, 5),
  force_fit = FALSE,
  pcutoff = 0.05,
  cap = 0.1,
  n_point_cutoff = 4,
  capping_fold = 5
)

Arguments

Details

Implementation of the genedata approach for curve fit: https://screener.genedata.com/documentation/display/DOC21/Business-Rules-for-Dose-Response-Curve-Fitting,-Model-Selection,-and-Fit-Validity.html #nolint

The output parameter names correspond to the following definitions:

x_mean

The mean of a given dose-response metric

x_AOC_range

The range of the area over the curve

x_AOC

The area over the GR curve or, respectively, under the relative cell count curve, averaged over the range of concentration values

xc50

The concentration at which the effect reaches a value of 0.5 based on interpolation of the fitted curve

x_max

The maximum effect of the drug

ec50

The drug concentration at half-maximal effect

x_inf

The asymptotic value of the sigmoidal fit to the dose-response data as concentration goes to infinity

x_0

The asymptotic metric value corresponding to a concentration of 0 for the primary drug

h

The hill coefficient of the fitted curve, which reflects how steep the dose-response curve is

r2

The goodness of the fit

x_sd_avg

The standard deviation of GR/IC

fit_type

This will be given by one of the following:

• "DRC4pHillFitModel" Successfully fit with a 4-parameter model

• "DRC3pHillFitModelFixS0" Successfully fit with a 3-parameter model

• "DRCConstantFitResult" Successfully fit with a constant fit

• "DRCTooFewPointsToFit" Not enough points to run a fit

• "DRCInvalidFitResult" Fit was attempted but failed

maxlog10Concentration

The highest log10 concentration

N_conc

Number of unique concentrations

Value

data.table with metrics and fit parameters.

Examples

logisticFit(
c(0.001, 0.00316227766016838, 0.01, 0.0316227766016838),
c(0.9999964000144, 0.999964001439942, 0.999640143942423, 0.996414342629482),
rep(0.1, 4),
priors = c(2, 0.4, 1, 0.00658113883008419)
)

Description

Lapply or bplapply.

Usage

loop(x, FUN, parallelize = TRUE, ...)

Arguments

Value

List containing output of FUN applied to every element in x.

Examples

loop(list(c(1,2), c(2,3)), sum, parallelize = FALSE)

Description

Lapply through all the experiments in MultiAssayExperiment object

Usage

MAEpply(mae, FUN, unify = FALSE, ...)

Arguments

Value

list or vector depends on unify param

Author(s)

Bartosz Czech [email protected]

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
MAEpply(mae, SummarizedExperiment::assayNames)

Description

get colData of all experiments

Usage

mcolData(mae)

Arguments

Value

data.table with all-experiments colData

Author(s)

Arkadiusz Gladki [email protected]

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
mcolData(mae)

Description

Merge assay data

Usage

merge_assay(
  SElist,
  assay_name,
  additional_col_name = "data_source",
  discard_keys = NULL
)

Arguments

Value

BumpyMatrix or list with data.table + BumpyMatrix

Examples

mae <- get_synthetic_data("finalMAE_combo_2dose_nonoise")

listSE <- list(
  combo1 = mae[[1]], 
  sa = mae[[2]]
)
merge_assay(listSE, "Normalized")

Description

Merge metadata

Usage

merge_metadata(SElist, metadata_fields)

Arguments

Value

list of merged metadata

Examples

mae <- get_synthetic_data("finalMAE_small")
se <- mae[[1]]
listSE <- list(
  se, 
  se
)
metadata_fields <- identify_unique_se_metadata_fields(listSE)
merge_metadata(listSE, metadata_fields)

Description

Merge multiple Summarized Experiments

Usage

merge_SE(
  SElist,
  additional_col_name = "data_source",
  discard_keys = c("normalization_type", "fit_source", "record_id", "swap_sa",
    "control_type")
)

Arguments

Value

merged SummarizedExperiment object

Examples

se1 <- get_synthetic_data("finalMAE_small")[[1]]
merge_SE(list(se1 = se1, se2 = se1))

Description

Reduce dimensionality of an assay by dropping extra data or combining variables.

Usage

modifyData(x, ...)

## S3 method for class 'drug_name2'
modifyData(x, option, keep, ...)

## S3 method for class 'data_source'
modifyData(x, option, keep, ...)

## Default S3 method:
modifyData(x, option, keep, ...)

Arguments

Details

If an essay extracted from a SummarizedExperiment contains additional information, i.e. factors beyond DrugName and CellLineName, that information will be treated in one of three ways, depending on the value of option:

• drop: Some information will be discarded and only one value of the additional variable (chosen by the user) will be kept.

• toDrug: The information is pasted together with the primary drug name. All observations are kept.

• toCellLine: As above, but pasting is done with cell line name.

Depending on the type of additional information, the exact details will differ. This is handled in the app by adding special classes to the data tables and dispatching to S3 methods.

Following modification, the additional columns are discarded.

Value

modified object

Methods (by class)

• modifyData(drug_name2): includes the name and concentration of the second drug

• modifyData(data_source): includes the data source

• modifyData(default): includes the name of other additional variables

Examples

dt <- data.table::data.table(a = as.character(1:10), b = "data")
dt <- addClass(dt, "a")
modifyData(dt, "average", keep = "b")

Description

get rowData of all experiments

Usage

mrowData(mae)

Arguments

Value

data.table with all-experiments rowData

Author(s)

Arkadiusz Gladki [email protected]

Examples

mae <- get_synthetic_data("finalMAE_small.qs") 
mrowData(mae)

Description

Predict a concentration for a given efficacy with fit parameters.

Usage

predict_conc_from_efficacy(efficacy, x_inf, x_0, ec50, h)

Arguments

Details

The inverse of this function is predict_efficacy_from_conc.

Value

Numeric vector representing predicted concentrations from given efficacies and fit parameters.

See Also

predict_efficacy_from_conc .calculate_x50

Examples

predict_conc_from_efficacy(efficacy = c(1, 1.5), x_inf = 0.1, x_0 = 1, ec50 = 0.5, h = 2)

Description

Predict efficacy values given fit parameters and a concentration.

Usage

predict_efficacy_from_conc(c, x_inf, x_0, ec50, h)

Arguments

Details

The inverse of this function is predict_conc_from_efficacy.

Value

Numeric vector representing predicted efficacies from given concentrations and fit parameters.

See Also

predict_conc_from_efficacy

Examples

predict_efficacy_from_conc(c = 1, x_inf = 0.1, x_0 = 1, ec50 = 0.5, h = 2)

Description

Map existing column names of a flattened 'Metrics' assay to prettified names.

Usage

prettify_flat_metrics(
  x,
  human_readable = FALSE,
  normalization_type = c("GR", "RV")
)

Arguments

Details

A common use case for this function is to prettify column names from a flattened version of the "Metrics" assay. Mode "human_readable" = TRUE is often used for prettification in the context of front-end applications, whereas "human_readable" = FALSE is often used for prettification in the context of the command line.

Value

character vector of prettified names.

Examples

x <- c("CellLineName", "Tissue", "Primary Tissue", "GR_gDR_x_mean", "GR_gDR_xc50", "RV_GDS_x_mean")
prettify_flat_metrics(x, human_readable = FALSE)

Description

Promote a nested field to be represented as a metadata field of the SummarizedExperiment as either the rowData or colData.

Usage

promote_fields(se, fields, MARGIN = c(1, 2))

Arguments

Details

Revert this operation using demote_fields.

Value

A SummarizedExperiment object with new dimensions resulting from promoting given fields.

See Also

demote_fields

Examples

mae <- get_synthetic_data("finalMAE_small")
se <- mae[[1]]
se <- promote_fields(se, "ReadoutValue", 2)

Description

current improvements done on the colData as a standardization step:

• set default value for optional colData fields

Usage

refine_coldata(cd, se, default_v = "Undefined")

Arguments

Value

refined colData

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
refine_coldata(SummarizedExperiment::colData(mae[[1]]), mae[[1]])

Description

current improvements done on the rowData as a standardization step:

• set default value for optional rowData fields

Usage

refine_rowdata(rd, se, default_v = "Undefined")

Arguments

Value

refined rowData

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
refine_rowdata(SummarizedExperiment::colData(mae[[1]]), mae[[1]])

Description

Remove Codrug Data

Usage

remove_codrug_data(
  data,
  prettify_identifiers = TRUE,
  codrug_identifiers = c("drug_name2", "concentration2")
)

Arguments

Value

data.table without combination columns

Examples

dt <-
  data.table::data.table(
    "Drug Name" = letters[seq_len(3)],
    "Concentration" = seq_len(3),
    "Drug Name 2" = letters[4:6],
    "Concentration 2" = 4:6
  )
dt
remove_codrug_data(dt)

Description

Rename BumpyMatrix

Usage

rename_bumpy(bumpy, mapping_vector)

Arguments

Value

a renamed BumpyMatrix object

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
se <- mae[[1]]
assay <- SummarizedExperiment::assay(se)
rename_bumpy(assay, c("Concentration" = "conc"))

Description

Rename DFrame

Usage

rename_DFrame(df, mapping_vector)

Arguments

Value

a renamed DFrame object

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
rename_DFrame(SummarizedExperiment::rowData(mae[[1]]), c("Gnumber" = "Gnumber1"))

Description

Round concentration to ndigit significant digits

Usage

round_concentration(x, ndigit = 3)

Arguments

Value

rounded x

Examples

round_concentration(x = c(0.00175,0.00324,0.0091), ndigit = 1)

Description

Set metadata for the fitting parameters that define the Metrics assay in SummarizedExperiment object metadata.

Usage

set_SE_fit_parameters(se, value)

set_SE_processing_metadata(se, value)

set_SE_keys(se, value)

set_SE_experiment_metadata(se, value)

set_SE_experiment_raw_data(se, value)

get_SE_fit_parameters(se)

get_SE_processing_metadata(se)

get_SE_experiment_raw_data(se)

get_SE_experiment_metadata(se)

get_SE_keys(se, key_type = NULL)

get_SE_identifiers(se, id_type = NULL, simplify = TRUE)

set_SE_identifiers(se, value)

Arguments

Details

For *et_SE_processing_metadata, get/set metadata for the processing info that defines the date_processed and packages versions in SummarizedExperiment object metadata. For *et_SE_fit_parameters, get/set metadata for fit parameters used to construct the Metrics assay in a SummarizedExperiment object.

Value

se with added metadata.

Examples

mae <- get_synthetic_data("finalMAE_small.qs") 
se <- mae[[1]]
get_SE_fit_parameters(se)

mae <- get_synthetic_data("finalMAE_small.qs") 
se <- mae[[1]]
meta <- get_SE_processing_metadata(se)

mae <- get_synthetic_data("finalMAE_small.qs") 
se <- mae[[1]]
get_SE_experiment_raw_data(se)

mae <- get_synthetic_data("finalMAE_small.qs") 
se <- mae[[1]]
get_SE_experiment_metadata(se)

mae <- get_synthetic_data("finalMAE_small.qs") 
se <- mae[[1]]
get_SE_identifiers(se)

Description

Replace values for flat fits: ec50 = 0, h = 0.0001 and xc50 = +/- Inf

Usage

set_constant_fit_params(out, mean_norm_value)

Arguments

Value

Modified named list of fit parameters.

Examples

na <- list(x_0 = NA)
set_constant_fit_params(na, mean_norm_value = 0.6)

Description

This function sets the CellLineName field in colData to be unique by appending the clid in parentheses for duplicates.

Usage

set_unique_cl_names(se)

Arguments

Value

A SummarizedExperiment object with unique CellLineName in colData.

Examples

se <- SummarizedExperiment::SummarizedExperiment(
  assays = list(counts = matrix(1:4, ncol = 2)),
  colData = S4Vectors::DataFrame(CellLineName = c("ID1", "ID1"), clid = c("C1", "C2"))
)
se <- set_unique_cl_names(se)

Description

This function sets the primary cell line field in data.frame-like object to be unique by appending the secondary cell line field in parentheses for duplicates.

Usage

set_unique_cl_names_dt(
  dt,
  primary_name = get_env_identifiers("cellline_name"),
  secondary_name = get_env_identifiers("cellline"),
  sep = " "
)

Arguments

Value

fixed input table with unique primary cell line field in dt

Examples

col_data <- S4Vectors::DataFrame(CellLineName = c("ID1", "ID1"), clid = c("C1", "C2"))
col_data <- set_unique_cl_names_dt(col_data)

Description

This function sets the DrugName, DrugName_2, and DrugName_3 fields in rowData to be unique by appending the corresponding Gnumber, Gnumber_2, and Gnumber_3 in parentheses for duplicates.

Usage

set_unique_drug_names(se)

Arguments

Value

A SummarizedExperiment object with unique DrugName fields in rowData.

Examples

se <- SummarizedExperiment::SummarizedExperiment(
  assays = list(counts = matrix(1:9, ncol = 3)),
  rowData = S4Vectors::DataFrame(DrugName = c("DrugA", "DrugA", "DrugB"),
  Gnumber = c("G1", "G2", "G5"),
  DrugName_2 = c("DrugC", "DrugC", "DrugD"),
  Gnumber_2 = c("G3", "G4", "G5")
))
se <- set_unique_drug_names(se)

Description

This function sets the primary drug field(s) in data.frame-like object to be unique by appending the secondary drug field(s) in parentheses for duplicates. By default DrugName, DrugName_2, and DrugName_3 are primary drug fields, while Gnumber, Gnumber_2, and Gnumber_3 are their respective secondary drug fields.

Usage

set_unique_drug_names_dt(
  dt,
  primary_names = unlist(get_env_identifiers()[(c("drug_name", "drug_name2",
    "drug_name3"))]),
  secondary_names = unlist(get_env_identifiers()[(c("drug", "drug2", "drug3"))]),
  sep = " "
)

Arguments

Value

fixed input table with unique primary drug field in dt

Examples

row_data <- S4Vectors::DataFrame(
  DrugName = c("DrugA", "DrugA", "DrugB"),
  Gnumber = c("G1", "G2", "G5"),
  DrugName_2 = c("DrugC", "DrugC", "DrugD"),
  Gnumber_2 = c("G3", "G4", "G5")
)
row_data <- set_unique_drug_names_dt(row_data)

Description

This function sets the CellLineName in colData and DrugName fields in rowData to be unique for each SummarizedExperiment in a MultiAssayExperiment.

Usage

set_unique_identifiers(mae)

Arguments

Value

A MultiAssayExperiment object with unique identifiers.

Examples

se1 <- SummarizedExperiment::SummarizedExperiment(
  assays = list(counts = matrix(1:4, ncol = 2)),
  colData = S4Vectors::DataFrame(parental_identifier = c("ID1", "ID1"), clid = c("C1", "C2")),
  rowData = S4Vectors::DataFrame(DrugName = c("DrugA", "DrugA"), Gnumber = c("G1", "G2"))
)
rownames(SummarizedExperiment::colData(se1)) <- c("cl1", "cl2")
rownames(SummarizedExperiment::rowData(se1)) <- c("g1", "g")
se2 <- SummarizedExperiment::SummarizedExperiment(
  assays = list(counts = matrix(5:8, ncol = 2)),
  colData = S4Vectors::DataFrame(parental_identifier = c("ID2", "ID2"), clid = c("C3", "C4")),
  rowData = S4Vectors::DataFrame(DrugName = c("DrugB", "DrugB"), Gnumber = c("G3", "G4"))
)
rownames(SummarizedExperiment::colData(se2)) <- c("cl3", "cl4")
rownames(SummarizedExperiment::rowData(se2)) <- c("g3", "g4")
mae <- MultiAssayExperiment::MultiAssayExperiment(experiments = list(se1 = se1, se2 = se2))
mae <- set_unique_identifiers(mae)

Description

This function sets the primary field in the data.frame-like objects to be unique by appending the secondary field in parentheses for duplicates.

Usage

set_unique_names_dt(dt, primary_name, secondary_name, sep = " ")

Arguments

Value

fixed input table with unique primary field in the table

Examples

col_data <- S4Vectors::DataFrame(CellLineName = c("ID1", "ID1"), clid = c("C1", "C2"))
col_data <- set_unique_names_dt(col_data, primary_name = "CellLineName", secondary_name = "clid")

Description

shorten normalization type

Usage

shorten_normalization_type_name(x)

Arguments

Value

shortened string representing the normalization type

Examples

shorten_normalization_type_name("GRvalue")

Description

Divide the columns of an input data.table into treatment metadata, condition metadata, experiment metadata, and assay data for further analysis. This will most commonly be used to identify the different components of a SummarizedExperiment object.

Usage

split_SE_components(df_, nested_keys = NULL, combine_on = 1L)

Arguments

Details

Named list containing the following elements:

"treatment_md":

treatment metadata

"condition_md":

condition metadata

"data_fields":

all data.table column names corresponding to fields nested within a BumpyMatrix cell

"experiment_md":

metadata that is constant for all entries of the data.table

"identifiers_md":

key identifier mappings

The nested_keys provides the user the opportunity to specify that they would not like to use that metadata field as a differentiator of the treatments, and instead, incorporate it into a nested DataFrame in the BumpyMatrix matrix object.

In the event that if any of the nested_keys are constant throughout the whole data.table, they will still be included in the DataFrame of the BumpyMatrix and not in the experiment_metadata.

Columns within the df_ will be identified through the following logic: First, the known data fields and any specified nested_keys are extracted. Following that, known cell and drug metadata fields are detected, and any remaining columns that represent constant metadata fields across all rows are extracted. Next, any cell line metadata will be heuristically extracted. Finally, all remaining columns will be combined on either the rows or columns as specified by combine_on.

Value

named list containing different elements of a SummarizedExperiment; see details.

Examples

split_SE_components(data.table::data.table(clid = "CL1", Gnumber = "DrugA"))

Description

Standardize MAE by switching from custom identifiers into gDR-default

Usage

standardize_mae(mae, use_default = TRUE)

Arguments

Value

mae a MultiAssayExperiment with default gDR identifiers

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
S4Vectors::metadata(mae[[1]])$identifiers$drug <- "druug"
standardize_mae(mae)

Description

Standardize SE by switching from custom identifiers into gDR-default

Usage

standardize_se(se, use_default = TRUE)

Arguments

Value

se a SummarizedExperiment with default gDR identifiers

Examples

mae <- get_synthetic_data("finalMAE_small.qs")
se <- mae[[1]]
S4Vectors::metadata(se)$identifiers$drug <- "druug"
standardize_se(se)

Description

String first and last characters of a string.

Usage

strip_first_and_last_char(jstring)

Arguments

Details

This is most often used to remove the JSON brackets '{' and '}'.

Value

String with first and last characters stripped.

Description

An auxiliary function that checks for duplicated rows in assay data.table, In case of duplicates it throws a message. The messsage function is by default stop() The message function can be customized with msg_f parameter

Usage

throw_msg_if_duplicates(
  dt,
  assay_name = "unknown",
  msg_f = stop,
  preview_max_numb = 4
)

Arguments

Examples

sdata <- get_synthetic_data("finalMAE_small")
smetrics_data <- convert_se_assay_to_dt(sdata[[1]], "Metrics")
throw_msg_if_duplicates(smetrics_data, assay_name = "Metrics", msg_f = futile.logger::flog.info)

Description

Update environment identifiers from MAE object identifiers

Usage

update_env_idfs_from_mae(mae_idfs)

Arguments

Value

NULL

Examples

update_env_idfs_from_mae(list(get_env_identifiers()))

Description

Update gDR synonyms for the identifier

Usage

update_idfs_synonyms(data, dict = get_idfs_synonyms())

Arguments

Value

list

Examples

mdict <- list(duration = "time")
iv <- c("Time", "Duration", "time")
update_idfs_synonyms(iv, dict = mdict)

Description

Assure that dimnames of two objects are the same

Usage

validate_dimnames(obj, obj2, skip_empty = TRUE)

Arguments

Value

NULL

Description

Check that specified identifier values exist in the data and error otherwise.

Usage

validate_identifiers(
  df,
  identifiers = NULL,
  req_ids = NULL,
  exp_one_ids = NULL
)

Arguments

Details

Note that this does NOT set the identifiers anywhere (i.e. environment or SummarizedExperiment object). If identifiers do not validate, will throw error as side effect.

Value

Named list of identifiers modified to pass validation against the input data. Errors with explanatory message if validation cannot pass with the given identifiers and data.

Examples

validate_identifiers(
  S4Vectors::DataFrame("Barcode" = NA, "Duration" = NA, "Template" = NA, "clid" = NA), 
  req_ids = "barcode"
)

Description

Validate JSON describing an object against a schema.

Usage

validate_json(json, schema_path)

Arguments

Details

This is most often used to validate JSON before passing it in as a document to an ElasticSearch index.

Value

Boolean of whether or not JSON successfully validated.

Examples

json <- '{}'

Description

Function validates correctness of SE included in MAE by checking multiple cases:

• detection of duplicated rowData/colData,

• incompatibility of rownames/colnames,

• occurrence of necessary assays,

• detection of mismatch of CLIDs inside colData and colnames (different order),

• correctness of metadata names.

Usage

validate_MAE(mae)

Arguments

Value

NULL invisibly if the MultiAssayExperiment is valid. Throws an error if the MultiAssayExperiment is not valid.

Author(s)

Bartosz Czech [email protected]

Examples

mae <- get_synthetic_data("finalMAE_small") 
validate_MAE(mae)

Description

Validate MAE object against a schema. Currently only SEs are validated TODO: add mae.json schema and validate full MAE object

Usage

validate_mae_with_schema(
  mae,
  schema_package = Sys.getenv("SCHEMA_PACKAGE", "gDRutils"),
  schema_dir_path = Sys.getenv("SCHEMA_DIR_PATH", "schemas"),
  schema = c(se = "se.json", mae = "mae.json")
)

Arguments

Value

Boolean of whether or not mae is valid

Examples

mae <- get_synthetic_data("finalMAE_small") 
validate_mae_with_schema(mae)

Description

Function validates correctness of SE by checking multiple cases:

• detection of duplicated rowData/colData,

• incompatibility of rownames/colnames,

• occurrence of necessary assays,

• detection of mismatch of CLIDs inside colData and colnames (different order),

• correctness of metadata names.

Usage

validate_SE(se, expect_single_agent = FALSE)

Arguments

Value

NULL invisibly if the SummarizedExperiment is valid. Throws an error if the SummarizedExperiment is not valid.

Examples

mae <- get_synthetic_data("finalMAE_small")
se <- mae[[1]]
validate_SE(se)

Description

Check for the presence of an assay in a SummarizedExperiment object.

Usage

validate_se_assay_name(se, name)

Arguments

Value

NULL invisibly if the assay name is valid. Throws an error if the assay is not valid.

Examples

mae <- get_synthetic_data("finalMAE_small") 
se <- mae[[1]]
validate_se_assay_name(se, "RawTreated")