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
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")
#> You are running version 1.23.5 of NormalyzerDE
#> [1] "Setting up statistics object"
#> [1] "Calculating statistical contrasts..."
#> [1] "Contrast calculations done!"
#> [1] "Writing 100 annotated rows to /tmp/Rtmp1Vj4ki/my_jobname/my_jobname_stats.tsv"
#> [1] "Writing statistics report"
#> [1] "All done! Results are stored in: /tmp/Rtmp1Vj4ki/my_jobname, processing time was 0 minutes"