Easily get EZbakR table of estimates of interest

Usage

EZget(
  obj,
  type = c("fractions", "kinetics", "readcounts", "averages", "comparisons", "dynamics"),
  features = NULL,
  populations = NULL,
  fraction_design = NULL,
  isoforms = NULL,
  kstrat = NULL,
  parameter = NULL,
  counttype = NULL,
  design_factor = NULL,
  experimental = NULL,
  reference = NULL,
  param_name = NULL,
  param_function = NULL,
  formula_mean = NULL,
  sd_grouping_factors = NULL,
  fit_params = NULL,
  repeatID = NULL,
  sub_features = NULL,
  grouping_features = NULL,
  sample_feature = NULL,
  modeled_to_measured = NULL,
  graph = NULL,
  returnNameOnly = FALSE,
  exactMatch = FALSE,
  alwaysCheck = FALSE
)

Arguments

obj: EZbakRData object
type: The class of EZbakR outputs would you like to search through. Equivalent to the name of the list in the EZbakRData object that contains the tables of interest.
features: Features that must be present in the table of interest. If exactMatch is TRUE, then these features must also be the only features present in the table.
populations: Only relevant if type == "fractions". Mutational populations that must have been analyzed to generate the table of interest.
fraction_design: Only relevant if type == "fractions". Fraction design table used to generate the table of interest.
isoforms: If the relevant table is the result of isoform deconvolution
kstrat: Only relevant if type == "kinetics". Short for "kinetics strategy"; the strategy used to infer kinetic parameters.
parameter: Only relevant if type == "averages" or "comparisons". Which parameter was being averaged or compared?
counttype: String denoting what type of read count information you are looking for. Current options are "TMM_normalized", "transcript", and "matrix". TO-DO: Not sure this is being used in any way currently...
design_factor: design_factor specified in relevant run of CompareParameters(). Therefore, only relevant if type == "comparisons".
experimental: Experimental condition specified in relevant run of CompareParameters(). Therefore, only relevant if type == "comparisons".
reference: Reference condition specified in relevant run of CompareParameters(). Therefore, only relevant if type == "comparisons".
param_name: Parameter name specified in relevant run of CompareParameters(). Therefore, only relevant if type == "comparisons"
param_function: Function of parameters specified in relevant run of CompareParameters(). Therefore, only relevant if type == "comparisons".
formula_mean: An R formula object specifying how the parameter of interest depends on the sample characteristics specified in obj's metadf. Therefore, only relevant if type == "averages".
sd_grouping_factors: What metadf columns should data be grouped by when estimating standard deviations across replicates? Therefore, only relevant if type == "averages".
fit_params: Character vector of names of parameters in linear model fit. Therefore, only relevant if type == "averages".
repeatID: Numerical ID for duplicate objects with same metadata.
sub_features: Only relevant if type == "dynamics". Feature columns that distinguished between the different measured species when running EZDynamics().
grouping_features: Only relevant if type == "dynamics. Features that were the overarching feature assignments by which sub_features were grouped when running EZDynamics().
sample_feature: Only relevant if type == "dynamics". Name of the metadf column that distinguished the different classes of samples when running EZDynamics().
modeled_to_measured: Only relevant if type == "dynamics". Specifies the relationship between sub_features, sample_feature (if specified), and the species in graph.
graph: Only relevant if type == "dynamics". NxN adjacency matrix, where N represents the number of species modeled when running EZDynamics().
returnNameOnly: If TRUE, then only the names of tables that passed your search criteria will be returned. Else, the single table passing your search criteria will be returned. If there is more than one table that passes your search criteria and returnNameOnly == FALSE, an error will be thrown.
exactMatch: If TRUE, then for features and populations, which can be vectors, ensure that provided vectors of features and populations exactly match the relevant metadata vectors.
alwaysCheck: If TRUE, then even if there is only a single table for the type of interest, still run all checks against queries.