smap                  package:SMAP                  R Documentation

_s_m_a_p: _A _S_e_g_m_e_n_t_a_l _M_a_x_i_m_u_m _A _P_o_s_t_e_r_i_o_r_i _A_p_p_r_o_a_c_h _t_o _A_r_r_a_y-_C_G_H _C_o_p_y
_N_u_m_b_e_r _P_r_o_f_i_l_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     This function fits a Hidden Markov Model (HMM) to a set of
     observed microarray intensity ratios and outputs the most
     plausible state sequence in the HMM through segmental a posteriori
     maximization.

     Briefly, given an HMM with initial parameter settings _lambda_ and
     a set of observations _O_, the method alternates maximization of
     the joint posterior probability of the state sequence _Q_ and
     _lambda_ given _O_, 'p(Q,lambda|O)', over _Q_ (using a modified
     Viterbi algorithm) and _lambda_ (using a gradient descent scheme
     with individual learning rate adaptation).

_U_s_a_g_e:

     smap(x, Obs, sd.min=0.05, mean.sd=0.05,
          max.iters=Inf, gd.max.iters=Inf, tau=0.05,
          eta=0.01, e.change=0.5, e.same=1.2,
          e.min=0.0001, e.max=0.5, adaptive=TRUE,
          overlap=TRUE, distance=TRUE, chrom.wise=FALSE,
          verbose=1, L=5000000)

_A_r_g_u_m_e_n_t_s:

       x: An object of class 'SMAPHMM-class'.

     Obs: An object of class 'SMAPObservations-class'.

  sd.min: The minimum allowed standard deviation of state associated
          Gaussian distributions (numeric).

 mean.sd: Prior standard deviation of state associated Gaussian means
          (numeric).

max.iters: Maximum number of iterations in the SMAP algorithm
          (numeric).

gd.max.iters: Maximum number of iterations in the gradient descent
          algorithm per SMAP iteration (numeric).

     tau: Minimum log probability improvement required in the SMAP and
          gradient descent optimization (numeric).

     eta: Initial learning rate in the gradient descent optimization
          (numeric).

e.change: Multiplier for individual learning rate adaptation if the
          sign of partial derivative changes (numeric). Only used if
          'adaptive == TRUE'.

  e.same: Multiplier for individual learning rate adaptation if the
          sign of partial derivative stays the same (numeric). Only
          used if 'adaptive == TRUE'.

   e.min: Minimum allowed learning rate (numeric).

   e.max: Maximum allowed learning rate (numeric).

adaptive: If 'TRUE', individual learning rate adaptation according to
          Algorithm 1 in Bagos et al. (2004) is used in the gradient
          descent optimization. 

 overlap: If 'TRUE', genomic overlap of clones is considered in the
          optimization.

distance: If 'TRUE', genomic distance between clones is considered in
          the optimization, in terms of distance based transition
          probabilities.

chrom.wise: If 'TRUE', the observations are analyzed chromosome-wise
          rather than genome-wise.

 verbose: Specifies the amount of output produced; 0 means no
          information and 3 a lot of information (numeric).

       L: A positive length parameter that controls the convergence of
          distance based transition probabilities towards 1 /
          'noStates(x)' (numeric).

_D_e_t_a_i_l_s:

     'sd.min', 'mean.sd', and 'eta' must all be greater than 0.  'tau'
     must be greater than 0 if 'max.iters' or 'gd.max.iters' are
     infinite, and can be 0 otherwise.  If 'adaptive' is 'TRUE', then
     'e.change' is required to be in the interval (0,1], 'e.same' must
     be greater than or equal to 1, and 'e.max' must be greater than 0.

_V_a_l_u_e:

     The method returns an object of class 'SMAPProfile-class' or
     'SMAPProfiles-class' if 'chrom.wise' is set to 'FALSE' or 'TRUE',
     respectively.

_A_u_t_h_o_r(_s):

     Robin Andersson robin.andersson@lcb.uu.se

_R_e_f_e_r_e_n_c_e_s:

     Andersson, R., Bruder, C. E. G., Piotrowski, A., Menzel, U., Nord,
     H., Sandgren, J., Hvidsten, T. R., Diaz de Stahl, T., Dumanski, J.
     P., Komorowski, J., A Segmental Maximum A Posteriori Approach to
     Array-CGH Copy Number Profiling, submitted

     Bagos P. G., Liakopoulos T. D., Hamodrakas, S. J. (2004) Faster
     Gradient Descent Training of Hidden Markov Models, Using
     Individual Learning Rate Adaptation. In Paliouras, G., Sakakibara,
     Y., editors, _ICGI_, volume 3264 of _Lecture Notes in Computer
     Science_, pages 40-52.

_S_e_e _A_l_s_o:

     'SMAPHMM', 'SMAPObservations'

_E_x_a_m_p_l_e_s:

     ## Load Glioblastoma multiforme data
     data(GBM)
     observations <- SMAPObservations(value=as.numeric(GBM[,2]),
                                      chromosome=as.character(GBM[,3]),
                                      startPosition=as.numeric(GBM[,4]),
                                      endPosition=as.numeric(GBM[,5]),
                                      name="G24460",
                                      reporterId=as.character(GBM[,1]))
     plot(observations, ylim=c(0,2))
     ## Initiate HMM
     init.means <- c(0.4, 0.7, 1, 1.3, 1.6, 3)
     init.sds <- rep(0.1, 6)
     phi <- cbind(init.means, init.sds)
     hmm <- SMAPHMM(6, phi, initTrans=0.02)
     hmm
     ## RUN SMAP:
     profile <- smap(hmm, observations, verbose=2)
     ## genome profile
     plot(profile, ylim=c(0,2))
     ## chromosome 9 profile
     ids <- which(chromosome(observations) == "9")
     plot(profile[ids], ylim=c(0,2), main="chromosome 9")
     ## output results for chromosome 9
     #cbind(reporterId(observations[ids]), Q(profile[ids]))

