KCsmart-package           package:KCsmart           R Documentation

_K_C_s_m_a_r_t

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

     Multiple sample aCGH analysis using kernel convolution

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


       Package:  KCsmart
       Type:     Package
       Version:  2.3.2
       Date:     2009-07-28
       License:  GPL

     Use the wrapper function 'calcSpm' to calculate the sample point
     matrix. Use 'findSigLevelTrad' to find a significance threshold
     using permutation based testing. Use 'plot' to plot the sample
     point matrix or 'plotScaleSpace' to plot the significant regions
     over multiple scales (sigmas). Use 'getSigSegments' to retrieve
     the significantly gained and lost regions using specific cutoffs.
     To use the comparative version of KCsmart, use the
     'calcSpmCollection', 'compareSpmCollection' and
     'getSigRegionsCompKC' functions. See the documentation of those
     function for details on how to use these.

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

     Jorma de Ronde, Christiaan Klijn

     Maintainer: Jorma de Ronde <j.d.ronde@nki.nl>

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

     Identification of cancer genes using a statistical framework for
     multiexperiment analysis of nondiscretized array CGH data. Nucleic
     Acids Res. 2008 Feb;36(2):e13.

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

     'calcSpm',  'findSigLevelTrad', 'findSigLevelFdr', 'plot',
     'plotScaleSpace', 'getSigSegments'

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

     data(hsSampleData)
     data(hsMirrorLocs)

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

     plot(spm1mb)
     plot(spm1mb, chromosomes=c(1,5,6,'X'))

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

     plot(spm1mb, sig.level=siglevel1mb)

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

     sigSegments1mb <- getSigSegments(spm1mb, siglevel1mb)

     spmc1mb <- calcSpmCollection(hsSampleData, hsMirrorLocs, cl=c(rep(0,10),rep(1,10)))
     spmcc1mb <- compareSpmCollection(spmc1mb, nperms=3)
     spmcc1mbSigRegions <- getSigRegionsCompKC(spmcc1mb)

     plot(spmcc1mb, sigRegions=spmcc1mbSigRegions)

