progressiveAlignment-class      package:flagme      R Documentation

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

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

     Performs a progressive peak alignment (clustalw style) of multiple
     GCMS peak lists

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

     progressiveAlignment(pD,cA,D=1000,gap=.5,verbose=TRUE,usePeaks=TRUE,df=30,compress=TRUE)

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

      pD: a 'peaksDataset' object

      cA: a 'clusterAlignment' object

       D: retention time penalty

     gap: gap parameter

 verbose: logical, whether to print information

usePeaks: logical, whether to use peaks (if 'TRUE') or the full 2D
          profile alignment (if 'FALSE')

      df: distance from diagonal to calculate similarity

compress: logical, whether to store the similarity matrices in sparse
          form

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

     The progressive peak alignment we implemented here for multiple
     GCMS peak lists is analogous to how 'clustalw' takes a set of
     pairwise sequence alignments and progressively builds a multiple
     alignment.  More details can be found in the reference below.

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

     'progressiveAlignment' 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', 'multipleAlignment'

_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)
     pa<-progressiveAlignment(pd, ca, gap = .6, D=.1,df=30)

