clusterAlignment           package:flagme           R Documentation

_D_a_t_a _S_t_r_u_c_t_u_r_e _f_o_r _a _c_o_l_l_e_c_t_i_o_n _o_f _a_l_l _p_a_i_r_w_i_s_e _a_l_i_g_n_m_e_n_t_s _o_f _G_C_M_S _r_u_n_s

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

     Store the raw data and optionally, information regarding signal
     peaks for a number of GCMS runs

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

     clusterAlignment(pD,runs=1:length(pD@rawdata),timedf=NULL,usePeaks=TRUE,verbose=TRUE,...)

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

      pD: a 'peaksDataset' object.

    runs: vector of integers giving the samples to calculate set of
          pairwise alignments over.

  timedf: list (length = the number of pairwise alignments) of matrices
          giving the expected time differences expected at each pair of
          peaks (used with 'usePeaks'='TRUE', passed to
          'peaksAlignment'

usePeaks: logical, 'TRUE' uses 'peakdata' list, 'FALSE' uses 'rawdata'
          list for computing similarity.

 verbose: logical, whether to print out info.

     ...: other arguments passed to 'peaksAlignment'

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

     clusterAlignment computes the set of pairwise alignments.

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

     'clusterAlignment' object

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

     Mark Robinson

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

     Mark D Robinson (2008).  Methods for the analysis of gas
     chromatography - mass spectrometry data _PhD dissertation_
     University of Melbourne.

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

     'peaksDataset', 'peaksAlignment'

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

     require(gcspikelite)

     # paths and files
     gcmsPath<-paste(.find.package("gcspikelite"),"data",sep="/")
     cdfFiles<-dir(gcmsPath,"CDF",full=TRUE)
     eluFiles<-dir(gcmsPath,"ELU",full=TRUE)

     # read data, peak detection results
     pd<-peaksDataset(cdfFiles[1:2],mz=seq(50,550),rtrange=c(7.5,8.5))
     pd<-addAMDISPeaks(pd,eluFiles[1:2])

     ca<-clusterAlignment(pd, gap = .5,D=.05,df=30)

