LLCT_Long_mRNA: Longitudinal Linear Combination Test for Gene Set Analysis
In its-likeli-jeff/LLCT: Longitudinal Linear Combination Test for Gene set Analysis

Description Usage Arguments Value Examples

View source: R/LLCT_Long_mRNA.R

LLCT is a two-step self-contained gene-set analysis method which is developed to handle multiple longitudinal outcomes. Analysis of within-subject variation in the first step is followed by examining the between-subject variation utilizing Linear Combination Test (LCT) in the second step. This method is also applicable in analysis of time-course microarray data.

LLCT_Long_mRNA(LongEXPR=LongEXPR, GS=GS, PhenData=PhenData, ID="ID",time=c("time","time2"), covariate=NULL, Genes=colnames(LongEXPR)[!(colnames(LongEXPR) %in% c("ID","time","time2","covariate"))],phenotype="phenotype",FIX.formula="~time+time2",nbPermutations=1000)

`LongEXPR`	Gene Expressions — Row names:Subjects (multiple rows per subject); Col names: columns include Subjects' ID, time/visit variables, time dependent covariates and Genes expressions . The rownames of GS must be matched with names of the genes included in LongEXPR. The ID values of PhenData must be matched with unique rownames of LongEXPR.
`GS`	Gene Set Matrix — Row names: list of genes; Col names: list of selected gene sets; Cells: 1 (if gene of the row belongs to the gene set of the column) 0 (otherwise). Note: The rownames of GS must be matched with colnames of EXPR
`PhenData`	Phenotype(s)/Predictor(s) Data. Columns should include Subjects' ID, single or multiple phenotypes. Rows: Subjects (one row per subject). The ID values of PhenData must be matched with the unique rownames of LongEXPR
`ID`	Name of ID variable in PhenData and LongEXPR Defaults to "ID".
`time`	Vector of the names of time variables in LongEXPR
`covariate`	name of covariate(s) in LongEXPR
`Genes`	List of gene names within LongEXPR defaults to colnames(LongEXPR)[-1:-2].
`phenotype`	name of phenotype in PhenData
`FIX.formula`	Formula of Phenotype~time model Defaults to "phenotype~time+covariate"
`RANDOM.formula`	Formula of random effects as lme function requires - required in case of related subjects Defaults to NULL
`nbPermutations`	Number of Permutations Defaults to 1000

`LLCT_Results`	geneset-specific p-values and q-values
`Step1_Coefs`	coefficients calculated in the first step using FIX_formula

data(data_for_LLCT_Long_mRNA)
#LLCT for time-course microarray datawith two time variables (time and time2) defining phenotypic temppral patterns
mrnarun <- LLCT_Long_mRNA(LongEXPR=LongEXPR, GS=GS, PhenData=PhenData, ID="ID",time=c("time","time2"), covariate=NULL, Genes=colnames(LongEXPR)[!(colnames(LongEXPR) %in% c("ID","time","time2","covariate"))],phenotype="phenotype",FIX.formula="~time+time2",nbPermutations=1000)
mrnarun$LLCT_Results
  
#'#LLCT for time-course microarray data with a linear time trend
#'LLCT_Long_mRNA(LongEXPR=LongEXPR, GS=GS, PhenData=PhenData, ID="ID",time=c("time"), covariate=NULL, Genes=colnames(LongEXPR)[!(colnames(LongEXPR) %in% c("ID","time","time2","covariate"))],phenotype="phenotype",FIX.formula="~time+time2",nbPermutations=1000)


#'#LLCT for time-course microarray data with adjustment for time dependent covariate
#'LLCT_Long_mRNA(LongEXPR=LongEXPR, GS=GS, PhenData=PhenData, ID="ID",time=c("time"), covariate="covariate", Genes=colnames(LongEXPR)[!(colnames(LongEXPR) %in% c("ID","time","time2","covariate"))],phenotype="phenotype",FIX.formula="~time+time2",nbPermutations=1000)

#'#LLCT for time-course microarray data with two phenotypes
#'LLCT_Long_mRNA(LongEXPR=LongEXPR, GS=GS, PhenData=PhenData, ID="ID",time=c("time"), covariate=NULL, Genes=colnames(LongEXPR)[!(colnames(LongEXPR) %in% c("ID","time","time2","covariate"))],phenotype=c("phenotype","phenotype2"),FIX.formula="~time+time2",nbPermutations=1000)