GADL1 {SARP.compo}

Gene expression change in bipolar disorder

Description

This data set gives the mRNA quantification of a few genes compared between bipolar disorder patients and healthy volunteers.

Usage

data(GADL1)

Format

A data frame with 13 columns and 56 rows. Each row gives the quantification results, expressed in amounts:

Groupe	factor	The group label, Ctrl for healthy volunteers and BPD for bipolar disorder patients
Phenotype	factor	the subgroup label, C for healthy volunteers, NR for patients not responding to treatment and R for patients responding to treatment
Sample	factor	the sample unique identifier

All other columns are numeric and give the amount, in arbitrary unit, of mRNA of the corresponding gene. The column name gives the name of the gene and the dilution used for the quantification. hprt and sdha are used as reference genes.

Source

Original data communicated by the authors of the experiment.

Examples

  data( GADL1 )
  # Optimal cut-off for ten genes and alpha = 0.05
  #  is around 0.22

  # First step, is there differences between healthy subjects
  #   and patients ?
  #
  M.m <- creer.Mp( d = GADL1, noms = names( GADL1 )[ -c( 1:3 ) ],
                   f.p = student.fpc, v.X = 'Groupe' )

  #  2) L'arbre associé
  #  [reference gene for plotting purpose]
  n.ref <- grep( 'HPRT|SDHA', names( GADL1 ), value = TRUE )
  plot( arbre.Mp( M.m, reference = n.ref ),
        seuil.p = c( 0.218, 0.207, 0.230 ) )

  #  3) Le graphe pour le seuil optimal
  #    => only IGF1 seems to behave differently
  #       (but it has missing values, so interpretation is difficult)
  plot( grf.Mp( M.m, reference = n.ref, p = 0.22 ) )

  
  # Second step, is there differences between patients
  #   that respond or not respond to treatment?
  d.R <- GADL1[ which( GADL1$Groupe == 'BPD' ), ]
  M.R <- creer.Mp( d = d.R, noms = names( GADL1 )[ -c( 1:3 ) ],
                   f.p = student.fpc, v.X = 'Phenotype' )

  #  2) L'arbre associé
  plot( arbre.Mp( M.R, reference = n.ref ),
        seuil.p = c( 0.218, 0.207, 0.230 ) )

  #  3) Le graphe pour le seuil optimal
  #    => no sign of any difference
  plot( grf.Mp( M.R, reference = n.ref, p = 0.22 ) )