flag.arrayCGH             package:MANOR             R Documentation

_A_p_p_l_y _a _f_l_a_g _t_o _a_n _a_r_r_a_y_C_G_H

_D_e_s_c_r_i_p_t_i_o_n:

     Function 'flag$FUN' is applied to a 'flag' object for
     normalization

_U_s_a_g_e:

     flag.arrayCGH(flag, arrayCGH)

_A_r_g_u_m_e_n_t_s:

    flag: an object of type 'flag'

arrayCGH: an object of type 'arrayCGH'

_D_e_t_a_i_l_s:

     Optional arguments in 'flag$args' are passed to 'flag$FUN'

_V_a_l_u_e:

     An object of class 'arrayCGH', which corresponds to the return
     value of 'flag$FUN' if 'flag$char' is null, and to the input
     'arrayCGH' object with spots given by 'flag$FUN' flagged with
     'flag$char'

_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.

_S_e_e _A_l_s_o:

     'to.flag', 'norm.arrayCGH'

_E_x_a_m_p_l_e_s:

     data(spatial)
     data(flags)

     gradient$arrayValues$LogRatioNorm <- gradient$arrayValues$LogRatio
     ## flag spots with no available position on the genome
     gradient <- flag.arrayCGH(position.flag, gradient)

     ## flag spots corresponding to low poor quality clones
     gradient <- flag.arrayCGH(val.mark.flag, gradient)

     ## flag spots excluded by Genepix pro
     gradient <- flag.arrayCGH(spot.flag, gradient)

     ## flag local spatial bias zones
     ## Not run: gradient <- flag.arrayCGH(local.spatial.flag, gradient)

     ## correct global spatial bias
     gradient <- flag.arrayCGH(global.spatial.flag, gradient)

     ## flag spots with low signal to noise
     gradient <- flag.arrayCGH(SNR.flag, gradient)

     ## flag spots with extremely high log-ratios
     gradient <- flag.arrayCGH(amplicon.flag, gradient)

     ## flag spots with poor within replicate consistency
     gradient <- flag.arrayCGH(replicate.flag, gradient)

     ## flag spots corresponding to clones for which all other spot
     ## replicates have already been flagged  
     gradient <- flag.arrayCGH(unique.flag, gradient)

     summary.factor(gradient$arrayValues$Flag)

