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Easily get EZbakR table of estimates of interest

EZget.Rd

EZget() returns a table of interest from your EZbakRData object. It is meant to make it easier to find and access certain analyses, as a single EZbakRData object may include analyses of different features, kinetic parameters, dynamical systems models, comparisons, etc.

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,
  dynamics_design_factors = NULL,
  scale_factors = NULL,
  cstrat = NULL,
  feature_lengths = 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,
  normalize_by_median = NULL,
  deconvolved = 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".

dynamics_design_factors

design_factors included in final EZDynamics() output. Therefore, only relevant if type == "dynamics".

scale_factors

Sample group scale factors used in EZDynamics(). Therefore, only relevant if type == "dynamics"

cstrat

Strategy used for comparative analyses. Can be:

  • contrast: If two parameters were compared via specifying the reference and experimental levels in CompareParameters() (for type == "averages").

  • single_param: If a single parameter was passed to CompareParameters() via its param_name option.

  • dynamics: If output of EZDynamics() was passed to CompareParameters()

  • function: If function of multiple parameters was passed to CompareParameter() via its param_function option.

feature_lengths

Table of feature lengths used for length normalization.

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().

normalize_by_median

Whether median difference was subtracted from estimated kinetic parameter difference. Thus, only relevant if type == "comparisons".

deconvolved

Only relevant if type == "fractions". Boolean that is TRUE if fractions table is result of performing multi-feature deconvolution with DeconvolveFractions().

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.

Value

Table of interest from the relevant EZbakRdata list (set by the type parameter).

Details

The input to EZget() is 1) the type of table you want to get ("fractions", "kinetics", "averages", "comparisons", or "dynamics") and 2) the metadata necessary to uniquely specify the table of interest. Above, every available piece of metadata that can be specified for this purpose is documented. You only need to specify the minimum information necessary. For example, if you would like to get a "fractions" table from an analysis of exon bins (feature == "exon_bins", and potentially other overarching features like "XF", "GF", or "rname"), and none of your other "fractions" tables includes exon_bins as a feature, then you can get this table with EZget(ezbdo, type = "fractions", features = "exon_bins"), where ezbdo is your EZbakRData object.

As another example, imagine you want to get a "kinetics" table from an analysis of gene-wise kinetic parameters (e.g., features == "XF"). You may have multiple "kinetics" tables, all with "XF" as at least one of their features. If all of the other tables have additional features though, then you can tell EZget() that "XF" is the only feature present in your table of interest by setting exactMatch to TRUE, which tells EZget() that the metadata you specify should exactly match the relevant metadata for the table of interest. So the call in this case would look like EZget(ezbdo, type = "fractions", features = "XF", exactMatch = TRUE).

EZget() is used internally in almost every single EZbakR function to specify the input table for each analysis. Thus, the usage and metadata described here also applies to all functions that require you to specify which table you want to use (e.g., EstimateKinetics(), AverageAndRegularize(), CompareParameters(), etc.).

Examples


# Simulate data to analyze
simdata <- EZSimulate(30)

# Create EZbakR input
ezbdo <- EZbakRData(simdata$cB, simdata$metadf)

# Estimate Fractions
ezbdo <- EstimateFractions(ezbdo)
#> Estimating mutation rates
#> Summarizing data for feature(s) of interest
#> Averaging out the nucleotide counts for improved efficiency
#> Estimating fractions
#> Processing output

# Estimate Kinetics
ezbdo <- EstimateKinetics(ezbdo)

# Average log(kdeg) estimates across replicate
ezbdo <- AverageAndRegularize(ezbdo)
#> Fitting linear model
#> Estimating coverage vs. variance trend
#> Regularizing variance estimates

#' # Average log(ksyn) estimates across replicate
ezbdo <- AverageAndRegularize(ezbdo, parameter = "log_ksyn")
#> Fitting linear model
#> Estimating coverage vs. variance trend
#> Regularizing variance estimates

# Compare log(kdeg) across conditions
ezbdo <- CompareParameters(
ezbdo,
design_factor = "treatment",
reference = "treatment1",
experimental = "treatment2"
)

# Get the one and only fractions object
fxns <- EZget(ezbdo, type = "fractions")

# Get the log(ksyn) averages table
ksyn_avg <- EZget(ezbdo, type = "averages", parameter = "log_ksyn")