SARP.compo (version 0.1.5)

SCH23390: Effect of MDMA and SCH23390 on gene expression

Description

This data set gives the mRNA quantification of several genes involved in the dopamin pathway in four different conditions: control, after addition of MDMA, after addition of SCH23390 and after addition of both.

Usage

data(SCH23390)

Format

A data frame with 8 columns and 48 rows. Each row gives the quantification results, expressed in cycle threshold (CT):

Groupe	factor	The group label
MDMA	factor	MDMA addition indicator (Oui=Yes, Non=No)
SCH23390	factor	SCH23390 addition indicator (as above)
Hprt	numeric	CT for the hprt gene, use as reference gene
Fos,Fosb,Egr1,Egr2	numeric	CT for the four genes of interest

References

N. Benturquia, C. Courtin, F. Noble, and C. Marie-Claire (2008). Involvement of D1 dopamine receptor in MDMA-induced locomotor activity and striatal gene expression in mice. Brain Research, 1211, 1-5

Examples

  data( SCH23390 )
  # Optimal cut-off for five genes and alpha = 0.05
  #  is around 0.13

  # First step, experimental check
  #
  # MDMA should change expression levels of all genes but the reference
  #  1) extract the data for the Ctrl vs MDMA groups comparison
  d.MDMA <- SCH23390[ which( SCH23390$Groupe %in% c( 'Ctrl', 'MDMA' ) ), ]
  M.MDMA <- creer.Mp( d = d.MDMA, noms = names( d.MDMA )[ 4:8 ], log = TRUE,
                      f.p = student.fpc, v.X = 'MDMA' )

  #  2) L'arbre associé
  plot( arbre.Mp( M.MDMA, reference = 'Hprt' ),
        seuil.p = c( 0.137, 0.128, 0.147 ) )

  #  3) Le graphe pour le seuil optimal
  #    => indeed, all genes are modified by MDMA
  #       Fos and Fosb seems to have the same behavior
  plot( grf.Mp( M.MDMA, reference = 'Hprt', p = 0.13 ) )

  
  # Second step, experiment analysis
  # Does SCH23390 modulate the MDMA effect?
  #   => interaction term in a two-ways analysis of variance
  M.I <- creer.Mp( d = SCH23390, noms = names( SCH23390 )[ 4:8 ], log = TRUE,
                   f.p = anva_SC.fpc,
                   frm = R ~ MDMA + SCH23390 + MDMA:SCH23390, SC = 3 )


  #  2) L'arbre associé
  plot( arbre.Mp( M.I, reference = 'Hprt' ),
        seuil.p = c( 0.137, 0.128, 0.147 ) )

  #  3) Le graphe pour le seuil optimal
  #    => no clear detection of interaction
  plot( grf.Mp( M.I, reference = 'Hprt', p = 0.13 ) )