normalyzerDE {NormalyzerDE} | R Documentation

## NormalyzerDE differential expression

### Description

Performs differential expression analysis on a normalization matrix. This command executes a pipeline processing the data and generates an annotated normalization matrix and a report containing p-value histograms for each of the performed comparisons.

### Usage

    normalyzerDE(jobName, comparisons, designPath = NULL, dataPath = NULL,
      experimentObj = NULL, outputDir = ".", logTrans = FALSE,
      type = "limma", sampleCol = "sample", condCol = "group",
      batchCol = NULL, techRepCol = NULL, leastRepCount = 1,
      quiet = FALSE, sigThres = 0.1, sigThresType = "fdr",
      log2FoldThres = 0)

### Arguments

`jobName` | Name of job
`comparisons` | Character vector containing target contrasts. If comparing condA with condB, then the vector would be c("condA-condB")
`designPath` | File path to design matrix
`dataPath` | File path to normalized matrix
`experimentObj` | SummarizedExperiment object, can be provided as input as alternative to 'designPath' and 'dataPath'
`outputDir` | Path to output directory
`logTrans` | Log transform the input (needed if providing non-logged input)
`type` | Type of statistical comparison, "limma" or "welch"
`sampleCol` | Design matrix column header for column containing sample IDs
`condCol` | Design matrix column header for column containing sample conditions
`batchCol` | Provide an optional column for inclusion of possible batch variance in the model
`techRepCol` | Design matrix column header for column containing technical replicates
`leastRepCount` | Minimum required replicate count
`quiet` | Omit status messages printed during run
`sigThres` | Significance threshold use for illustrating significant hits in diagnostic plots
`sigThresType` | Type of significance threshold, "fdr" or "p". "fdr" is strongly recommended (Benjamini-Hochberg corrected p-values)
`log2FoldThres` | Fold-size cutoff for being considered significant in diagnostic plots

### Details

When executed, it performs the following steps:

1: Read the data and the design matrices into dataframes. 2: Generate an instance of the NormalyzerStatistics class representing the data and their statistical comparisons. 3: Optionally reduce technical replicates in both the data matrix and the design matrix 4: Calculate statistical contrats between supplied groups 5: Generate an annotated version of the original dataframe where columns containing statistical key measures have been added 6: Write the table to file 7: Generate a PDF report displaying p-value histograms for each calculated contrast

### Value

None

### Examples

    data_path <- system.file(package="NormalyzerDE", "extdata", "tiny_data.tsv")
    design_path <- system.file(package="NormalyzerDE", "extdata", "tiny_design.tsv")
    out_dir <- tempdir()
    normalyzerDE(
      jobName="my_jobname",
      comparisons=c("4-5"),
      designPath=design_path,
      dataPath=data_path,
      outputDir=out_dir,
      condCol="group")

[Package NormalyzerDE version 1.4.0 Index]