gene: Gene expression

In missMDA: Handling Missing Values with Multivariate Data Analysis

Gene expression

Description

A data frame with 53 brain tumors of 4 different types defined by the standard World Health Organization (WHO) classification (O, oligodendrogliomas; A, astrocytomas; OA, mixed oligo-astrocytomas and GBM, glioblastomas) are described by information at the transcriptome level with expression data (356 continuous variables for microarrays) and at the genome level (76 continuous variables for CGH data) as illustrated. 10 rows are missing for the expression data.

Format

A data-frame with 53 rows and 432 continuous variables

Source

de Tayrac M, Le S, Aubry M, Mosser J, Husson F. (2009). Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach. BMC Genomics, 10.

Examples

## Not run: 
data(gene)
res.impute <- imputeMFA(gene[,-1], group = c(76,356), 
    type = rep("s",2), ncp = 2) 
res.mfa <- MFA(cbind.data.frame(gene[,1], res.impute$completeObs), 
      group = c(1,76,356), type=c("n",rep("s",2)), 
	  name.group = c("WHO","CGH","expr"), num.group.sup = 1)
plot(res.mfa, habillage = 1, lab.ind = FALSE)
plot(res.mfa, habillage = "group", invisible = "ind", partial = "all")
plot(res.mfa, habillage = "group", lab.ind = FALSE, partial = "all")
plot(res.mfa, choix = "var", habillage = "group", lab.var = FALSE)
plot(res.mfa, choix = "group", habillage = "group")

## End(Not run)

missMDA documentation built on Nov. 17, 2023, 5:07 p.m.