normalyzerDE | R Documentation |

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.

```
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
)
```

`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 |

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

None

```
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")
```

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