mergeLevels               package:aCGH               R Documentation

_m_e_r_g_e_L_e_v_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     Merging of predicted levels for array CGH data and similar.

_U_s_a_g_e:

     mergeLevels(vecObs,vecPred,pv.thres=0.0001,ansari.sign=0.05,thresMin=0.05,thresMax=0.5,verbose=1,scale=TRUE)

_A_r_g_u_m_e_n_t_s:

  vecObs: Vector of observed values, i.e. observed log2-ratios 

 vecPred: Vector of predicted values, i.e. mean or median of levels
          predicted by segmentation algorithm 

pv.thres: Significance threshold for Wilcoxon test for level merging 

ansari.sign: Significance threshold for Ansari-Bradley test 

thresMin: merge if segment medians are closer than thresMin , defaiult
          is 0.05

thresMax: don't merge if segment medians are further than thresMax
          (unless needs to be merged for a different reason: wilcoxon
          test), default is .5

 verbose: if 1, progress is printed

   scale: whether thresholds are on the log2ratio scale and thus need
          to be converted to the copy number. default is TRUE 

_D_e_t_a_i_l_s:

     mergeLevels takes a vector of observed log2-ratios and predicted
     log2ratios and merges levels that are not significantly distinct.

_V_a_l_u_e:

vecMerged : Vector with merged values. One merged value returned for
          each predicted/observed value 

  mnNow : Merged level medians

     sq : Vector of thresholds, the function has searched through to
          find optimum. Note, these thresholds are based on copy number
          transformed values

 ansari : The p-values for the ansari-bradley tests for each threshold
          in sq

_N_o_t_e:

     vecObs and vecPred must have same length and observed and
     predicted value for a given probe should have same position in
     vecObs and vedPred. The function assumes that log2-ratios are
     supplied

_A_u_t_h_o_r(_s):

     Hanni Willenbrock (Hanni@cbs.dtu.dk) and Jane Fridlyand
     (jfridlyand@cc.ucsf.edu)

_R_e_f_e_r_e_n_c_e_s:

     Willenbrock H, Fridlyand J. (2005). A comparison study: applying
     segmentation to array CGH data for downstream analyses.
     Bioinformatics. 2005 Sep 14; [Epub ahead of print]

_E_x_a_m_p_l_e_s:

     # Example data of observed and predicted log2-ratios
     vecObs <- c(rep(0,40),rep(0.6,15),rep(0,10),rep(-0.4,20),rep(0,15))+rnorm(100,sd=0.2)
     vecPred <- c(rep(median(vecObs[1:40]),40),rep(median(vecObs[41:55]),15),
       rep(median(vecObs[56:65]),10),rep(median(vecObs[66:85]),20),rep(median(vecObs[86:100]),15))

     # Plot observed values (black) and predicted values (red)
     plot(vecObs,pch=20)
     points(vecPred,col="red",pch=20)

     # Run merge function
     merge.obj <- mergeLevels(vecObs,vecPred)

     # Add merged values to plot
     points(merge.obj$vecMerged,col="blue",pch=20)

     # Examine optimum threshold
     merge.obj$sq

