norm              package:MANOR              R Documentation(latin1)

_N_o_r_m_a_l_i_z_e _a_n _o_b_j_e_c_t _o_f _t_y_p_e _a_r_r_a_y_C_G_H

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

     Normalize an object of type 'arrayCGH' using a list of criteria
     specified as (temporary or permanent) flags. If a replicate
     identifier (clone name) is provided, a single target value is
     computed for all the replicates.

     The normalization coefficient is computed as a function, and is
     applied to all good quality spots of the array.

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

       ## S3 method for class 'arrayCGH':
       norm(arrayCGH, flag.list=NULL, var="LogRatio", printTime=FALSE, FUN=median, ...)

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

arrayCGH: an object of type 'arrayCGH'

flag.list: a list of objects of type flag

     var: a variable name (from 'arrayCGH$arrayValues') from which
          normalization coefficient has to be computed; default is
          "LogRatio"

printTime: boolean value; if 'TRUE', the time taken by each step of the
          normalization process is displayed

     FUN: an aggregation function (e.g. mean, median) to compute a
          normalization coefficient; default is median

     ...: further arguments to be passed to FUN

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

     The two flag types are treated differently :   - permanent flags
     detect poor quality spots, which are removed from further analysis
     - temporary flags detect good quality spots that would bias the
     normalization coefficient if they were not excluded from its
     computation, e.g. amplicons or sexual chromosomes. Thus they are
     not taken into account for the computation of the coefficient, but
     at the end of the analysis they are normalized as any good quality
     spots of the array. 

     The normalization coefficient is computed as a function (e.g. mean
     or median) of the target value (e.g. log-ratios); it is then
     applied to all good quality spots (including temporary flags),
     i.e. substracted from each target value.

     If clone level information is available (i.e. if
     'arrayCGH$cloneValues' is not null), a normalized mean target
     value and basic statistics (such as the number of replicates per
     clone) are calculated for each clone.

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

     an object of type 'arrayCGH'

_N_o_t_e:

     People interested in tools for array-CGH analysis can visit our
     web-page: <URL: http://bioinfo.curie.fr>.

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

     Pierre Neuvial, manor@curie.fr.

_R_e_f_e_r_e_n_c_e_s:

     P. Neuvial, P. Hup, I. Brito, S. Liva, E. Mani, C. Brennetot, A.
     Aurias, F. Radvanyi, and E. Barillot. _Spatial normalization of
     array-CGH data_. BMC Bioinformatics, 7(1):264. May 2006.

_S_e_e _A_l_s_o:

     'flag'

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

     data(spatial)
     data(flags)

     ### 'edge': local spatial bias
     ## define a list of flags to be applied
     flag.list1 <- list(spatial=local.spatial.flag, spot=spot.corr.flag,
     ref.snr=ref.snr.flag, dapi.snr=dapi.snr.flag, rep=rep.flag,
     unique=unique.flag) 
     flag.list1$spatial$args <- alist(var="ScaledLogRatio", by.var=NULL,
     nk=5, prop=0.25, thr=0.15, beta=1, family="symmetric") 
     flag.list1$spot$args <- alist(var="SpotFlag")
     flag.list1$spot$char <- "O"
     flag.list1$spot$label <- "Image analysis"

     ## normalize arrayCGH
     ## Not run: 
     edge.norm <- norm(edge, flag.list=flag.list1,
     var="LogRatio", FUN=median, na.rm=TRUE)
     ## End(Not run) 
     print(edge.norm$flags) ## spot-level flag summary (computed by flag.summary)

     ## aggregate arrayCGH without normalization
     edge.nonorm <- norm(edge, flag.list=NULL, var="LogRatio",
     FUN=median, na.rm=TRUE)  

     ## sort genomic informations 
     edge.norm <- sort(edge.norm, position.var="PosOrder")
     edge.nonorm <- sort(edge.nonorm, position.var="PosOrder")

     ## plot genomic profiles
     layout(matrix(c(1,2,4,5,3,3,6,6), 4,2),width=c(1, 4), height=c(6,1,6,1))
     report.plot(edge.nonorm, chrLim="LimitChr", layout=FALSE,
     main="Pangenomic representation (before normalization)", zlim=c(-1,1),
     ylim=c(-3,1))  
     report.plot(edge.norm, chrLim="LimitChr", layout=FALSE,
     main="Pangenomic representation (after normalization)", zlim=c(-1,1),
     ylim=c(-3,1)) 

     ### 'gradient': global array Trend
     ## define a list of flags to be applied
     flag.list2 <- list(
       spot=spot.flag, global.spatial=global.spatial.flag, SNR=SNR.flag,
       val.mark=val.mark.flag, position=position.flag, unique=unique.flag,
       amplicon=amplicon.flag, replicate=replicate.flag,
       chromosome=chromosome.flag)

     ## normalize arrayCGH
     ## Not run: gradient.norm <- norm(gradient, flag.list=flag.list2, var="LogRatio", FUN=median, na.rm=TRUE) 
     ## aggregate arrayCGH without normalization
     gradient.nonorm <- norm(gradient, flag.list=NULL, var="LogRatio", FUN=median, na.rm=TRUE) 

     ## sort genomic informations 
     gradient.norm <- sort(gradient.norm)
     gradient.nonorm <- sort(gradient.nonorm)

     ## plot genomic profiles
     layout(matrix(c(1,2,4,5,3,3,6,6), 4,2),width=c(1, 4), height=c(6,1,6,1))
     report.plot(gradient.nonorm, chrLim="LimitChr", layout=FALSE,
     main="Pangenomic representation (before normalization)", zlim=c(-2,2),
     ylim=c(-3,2)) 
     report.plot(gradient.norm, chrLim="LimitChr", layout=FALSE,
     main="Pangenomic representation (after normalization)", zlim=c(-2,2),
     ylim=c(-3,2)) 

