plotScaleSpace            package:KCsmart            R Documentation

_P_l_o_t _m_u_l_t_i_p_l_e _s_i_g_n_i_f_i_c_a_n_t _r_e_g_i_o_n_s _i_n _o_n_e _f_i_g_u_r_e

_D_e_s_c_r_i_p_t_i_o_n:

     Plots significant regions in different scale spaces in one figure

_U_s_a_g_e:

     plotScaleSpace(spms, sigLevels, chromosomes=NULL, type='b')

_A_r_g_u_m_e_n_t_s:

    spms: List of sample point matrices 

sigLevels: List of significance levels 

chromosomes: Takes a vector of chromosomes to be plotted. Defaults to
          all chromosomes. 

    type: Determines which data is plotted. 'g' for gains only, 'l' for
          losses only and 'b' for both. When type='b' is used, two
          devices (x11) will be opened.

_D_e_t_a_i_l_s:

     Takes sample point matrices that were calculated using (different)
     kernel widths (sigma), then calculates the significant regions
     given the cutoffs as defined by 'sigLevels' and plots these in one
     figure.

_V_a_l_u_e:

     Depending on the 'type' parameter, produces one or two plots, one
     for the gains and one for the losses. The heatmap color indicates
     the level of the gain or loss.

_A_u_t_h_o_r(_s):

     Jorma de Ronde

_S_e_e _A_l_s_o:

     'plot'

_E_x_a_m_p_l_e_s:

     data(hsSampleData)
     data(hsMirrorLocs)

     spm1mb <- calcSpm(hsSampleData, hsMirrorLocs)
     spm4mb <- calcSpm(hsSampleData, hsMirrorLocs, sigma=4000000)

     siglevel1mb <- findSigLevelTrad(hsSampleData, spm1mb, n=3)
     siglevel4mb <- findSigLevelTrad(hsSampleData, spm4mb, n=3)

     plotScaleSpace(list(spm1mb, spm4mb), list(siglevel1mb, siglevel4mb), type='g')

