Verify that input data is in correct format, and if so, return a generated NormalyzerDE data object from that input data — getVerifiedNormalyzerObject • NormalyzerDE

This function performs a number of checks on the input data and provides informative error messages if the data isn't fulfilling the required format. Checks include verifying that the design matrix matches to the data matrix, that the data matrix contains valid numbers and that samples have enough values for analysis

Usage

getVerifiedNormalyzerObject(
  jobName,
  summarizedExp,
  threshold = 15,
  omitSamples = FALSE,
  requireReplicates = TRUE,
  quiet = FALSE,
  noLogTransform = FALSE,
  tinyRunThres = 50
)

Arguments

jobName: Name of ongoing run.
summarizedExp: Summarized experiment input object
threshold: Minimum number of features.
omitSamples: Automatically omit invalid samples from analysis.
requireReplicates: Require there to be at least to samples per condition
quiet: Don't print output messages during processing
noLogTransform: Don't log-transform the provided data
tinyRunThres: If less features in run, a limited run is performed

Value

Normalyzer data object representing verified input data.

Examples

data(example_summarized_experiment)
normObj <- getVerifiedNormalyzerObject("job_name", example_summarized_experiment)
#> Input data checked. All fields are valid.
#> Sample check: More than one sample group found
#> Sample replication check: All samples have replicates
#> RT annotation column found (5)