identifyDifferentiallyExpressedGenes: A benchmark type function to test a complete process of FBN...

In clsdavid/FBNNet2_public: Fundamental Boolean Model and Networks

A benchmark type function to test a complete process of FBN model All sub time series must contain the same number of timepoints Step 3, identify significantly expressed genes, which are strongly related with the samples / study purposes

Description

A benchmark type function to test a complete process of FBN model All sub time series must contain the same number of timepoints Step 3, identify significantly expressed genes, which are strongly related with the samples / study purposes

Usage

identifyDifferentiallyExpressedGenes(
  orderSampleTimeSeries,
  cutOffInduction = 1,
  cutOffRepression = 1,
  majority = 7,
  needLog2scale = FALSE,
  probesetGeneNameMappings = NULL,
  nameTab = "RMA"
)

Arguments

orderSampleTimeSeries	A sorted time series data, which is the output of the method reorderSampleTimeSeries
cutOffInduction	a threshold that identify genes as folds. If the cutOffInduction is 2, the differential genes are identified based on 2 folds
cutOffRepression	a threshold that identify genes as folds. If the cutOffRepression is 2, the differential genes are identified based on 2 folds
majority	A criteria that make a gene as differential
needLog2scale	If it is true, then all gene values will be processed using log2
probesetGeneNameMappings	gene mapping file

clsdavid/FBNNet2_public documentation built on April 20, 2023, 4:36 p.m.