normalyzerDE: NormalyzerDE differential expression

View source: R/NormalyzerDE.R

normalyzerDER 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,
  writeReportAsPngs = FALSE
)

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", "limma_intensity" or "welch", where "limma_intensity" allows the prior to be fit according to intensity rather than using a flat prior

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

writeReportAsPngs

Output report as separate PNG files instead of a single PDF

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")

ComputationalProteomics/NormalyzerDE documentation built on Oct. 18, 2024, 9:57 p.m.