ITALICS               package:ITALICS               R Documentation

_A_f_f_y_m_e_t_r_i_x _S_N_P _c_h_i_p _n_o_r_m_a_l_i_z_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Normalize and analyse Affymetrix SNP array 100K and 500K set (see
     the vignette)

_U_s_a_g_e:

     ITALICS(quartetInfo, snpInfo, confidence=0.95, iteration=2, 
         formule="Smoothing+QuartetEffect+FL+I(FL^2)+I(FL^3)+GC+I(GC^2)+I(GC^3)", prc=0.3,
         amplicon=2.1, deletion=-3.5, deltaN=0.15, forceGL=c(-0.2,0.2), param=c(d=2), nbsigma=1, ... )

_A_r_g_u_m_e_n_t_s:

quartetInfo: a data frame containing all the raw quartet intensities
          plus there GC content, fragment length, and Quartet effect

 snpInfo: a data frame containing SNPs position along the genome and
          raw copy number

confidence: The confidence interval. After the last bias estimation
          step, quartets outside this confidence interval are flagged.
          The lower confidence is, the more quartets will be flagged.
          See also the parameter prc.

iteration: The number of iteration you d'like to do

 formule: A symbolic description of the term of the model. The default
          value of formule means that we want correct the observed
          quartetLogRatio using the estimated copy number (Smoothing),
          the Quartet Effect, the quartet Fragment Length (FL) and the
          quartet GC content.

     prc: prc is a frequence (between 0 and 1). After the final
          iteration of ITALICS, badly predicted probes are flagged (see
          also the parameter confidence). Only SNPs having more than
          prc of their probes non-flagged are kept for the final GLAD
          analysis. The higher prc is, the more SNPs are removed before
          the final GLAD analysis.

amplicon: see the amplicon parameter in the daglad function

deletion: see the deletion parameter in the daglad function

  deltaN: see the deltaN parameter in the daglad function

 forceGL: see the forceGL parameter in the daglad function

   param: see the param parameter in the daglad function

 nbsigma: see the nbsigma parameter in the daglad function

     ...: Other daglad parameters.

_D_e_t_a_i_l_s:

     The function 'ITALICS' implements the methodology which is
     described in the article : ITALICS: an algorithm for normalization
     and DNA copy number calling for Affymetrix SNP arrays   (Rigaill
     et al., Bioinformatics Advance Access published on February 5,
     2008).

     The principle of the ITALICS algorithm: ITALICS, is a
     normalization method that estimates both the biological and the
     non-relevant effects in an alternate and iterative way to
     accurately remove the non-relevant effects.

     ITALICS deals with known systematic sources of variation such as
     the GC-content of the quartets, the PCR amplified fragment length
     and the GC-content of the PCR amplified fragment . It also takes
     into account the quartet effect which corresponds to the fact that
     some quartets systematically have a small intensity while others
     tend to have a high intensity.  ITALICS is also able to correct
     spatial artifacts which sometimes arise on Affymetrix SNP arrays
     100K and 500K set.

_V_a_l_u_e:

Return an object of class profileCGH: 

_N_o_t_e:

     People interested in tools dealing with array CGH analysis and DNA
     copy number analysis can visit our web-page <URL:
     http://bioinfo.curie.fr>.

_A_u_t_h_o_r(_s):

     Guillem Rigaill, italics@curie.fr.

_S_o_u_r_c_e:

     Institut Curie, italics@curie.fr.

_E_x_a_m_p_l_e_s:

     ## Not run: 
     ## step to get the path of the HF0844_Hind.CEL file
     ITALICSDataPATH <- attr(as.environment(match("package:ITALICSData",search())),"path")
     filename <- paste(ITALICSDataPATH,"/data/HF0844_Hind.CEL", sep="")
     quartetEffectFile <- paste(ITALICSDataPATH,"/data/Hind.QuartetEffect.csv", sep="")

     ## load quartet effect
     quartetEffect <- read.table(quartetEffectFile, sep=";", header=TRUE)

     ## load annotation using the pd.mapping50k.xba24 or pd.mapping50k.hind240 or  pd.mapping250k.sty or pd.mapping250k.nsp package
     headdetails <- readCelHeader(filename[1])
     pkgname <- cleanPlatformName(headdetails[["chiptype"]])
     snpInfo <- getSnpInfo(pkgname)
     quartet <- getQuartet(pkgname, snpInfo)

     ## read cel files and format data
     tmpExprs <- readCelIntensities(filename, indices=quartet$fid)
     quartet$quartetInfo$quartetLogRatio <- readQuartetCopyNb(tmpExprs)
     quartet$quartetInfo <- addInfo(quartet, quartetEffect)
     snpInfo <- fromQuartetToSnp(cIntensity="quartetLogRatio", quartetInfo=quartet$quartetInfo, snpInfo=snpInfo)

     ## ITALICS normalization
     profilSNPHind <- ITALICS(quartet$quartetInfo, snpInfo,
         formule="Smoothing+QuartetEffect+FL+I(FL^2)+I(FL^3)+GC+I(GC^2)+I(GC^3)")

     ## plot the profile
     plotProfile(profilSNPHind, Smoothing="Smoothing", Bkp=TRUE)
     ## End(Not run)

