Welcome to PyIOmica’s documentation!¶
This documentation describes PyIOmica, a Python package that provides bioinformatics utilities for analyzing (dynamic) omics datasets. PyIOmica extends MathIOmica usage to Python and implements new visualizations and computational tools for graph analyses.
- 1. Overview
- 2. Installation
- 3. Functionality
- Global variables
- Categorization functions
- Enrichment analyses functions
internalAnalysisFunction()
OBOGODictionary()
GetGeneDictionary()
GOAnalysisAssigner()
obtainConstantGeneDictionary()
GOAnalysis()
GeneTranslation()
KEGGAnalysisAssigner()
KEGGDictionary()
KEGGAnalysis()
MassMatcher()
MassDictionary()
ExportEnrichmentReport()
BenjaminiHochbergFDR()
ReactomeAnalysis()
ExportReactomeEnrichmentReport()
- Extended DataFrame and data-processing functions
DataFrame
DataFrame.__init__()
DataFrame.filterOutAllZeroSignals()
DataFrame.filterOutFractionZeroSignals()
DataFrame.filterOutFractionMissingSignals()
DataFrame.filterOutReferencePointZeroSignals()
DataFrame.tagValueAsMissing()
DataFrame.tagMissingAsValue()
DataFrame.tagLowValues()
DataFrame.removeConstantSignals()
DataFrame.boxCoxTransform()
DataFrame.modifiedZScore()
DataFrame.normalizeSignalsToUnity()
DataFrame.quantileNormalize()
DataFrame.compareTimeSeriesToPoint()
DataFrame.compareTwoTimeSeries()
DataFrame.imputeMissingWithMedian()
mergeDataframes()
getLombScarglePeriodogramOfDataframe()
getRandomSpikesCutoffs()
getRandomAutocorrelations()
getRandomPeriodograms()
- Clustering functions
- Frequency Based Subject Match
- Visibility graph preparation functions
- Visibility Graph Community Detection
- Visualization functions
- Utility functions
- Data Storage
- 4. Dependencies
- 5. Included data
- 6. Examples