normexp.fit              package:limma              R Documentation

_F_i_t _N_o_r_m_a_l+_E_x_p _C_o_n_v_o_l_u_t_i_o_n _M_o_d_e_l _t_o _O_b_s_e_r_v_e_d _I_n_t_e_n_s_i_t_i_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Fit the normal+exponential convolution model to a vector of
     observed intensities. The normal part represents the background
     and the exponential part represents the signal intensities. This
     function is called by 'backgroundCorrect' and is not normally
     called directly by users.

_U_s_a_g_e:

     normexp.fit(x, method="saddle", n.pts=NULL, trace=FALSE)

_A_r_g_u_m_e_n_t_s:

       x: numeric vector of (background corrected) intensities

  method: method used to estimate the three parameters. Choices for
          'normexp.fit' are '"mle"', '"saddle"', '"rma"' and '"rma75"'.

   n.pts: number of quantiles of 'x' to use for the fit. If 'NULL' then
          all values of 'x' will be used.

   trace: logical, if 'TRUE', tracing information on the progress of
          the optimization is given.

_D_e_t_a_i_l_s:

     The Normal+Exp (normexp) convolution model is a mathematical model
     representing microarray intensity data for the purposes of
     background correction. It was proposed originally as part of the
     RMA algorithm for Affymetrix microarray data. For two-color
     microarry data, the normexp background correction method was
     introduced and compared with other methods by Ritchie et al
     (2007).

     This function uses maximum likelihood estimation to fit the
     normexp model to background-corrected intensities. The model
     assumes that the observed intensities are the sum of background
     and signal components, the background being normal and the signal
     being exponential distributed.

     The likelihood may be computed exactly ('method="mle"') or
     approximated using a saddle-point approximation
     ('method="saddle"'). The saddle-point approximation was proposed
     by Ritchie et al (2007). Silver et al (2008) added some
     computational refinements to the saddle-point approximation,
     making it more reliable in practice, and developed the exact
     likelihood maximization algorithm. The '"mle"' method uses the
     best performing algorithm from Silver et al (2008), which calls
     the optimization function 'nlminb' with analytic first and second
     derivatives. Derivatives are computed with respect to the
     normal-mean, the log-normal-variance and the log-exponential-mean.

     Two ad-hoc estimators are also available which do not require
     iterative estimation. '"rma"' results in a call to the
     'bg.parameters' function of the affy package. This provides the
     kernel estimation method that is part of the RMA algorithm for
     Affymetrix data. '"rma75"' uses the similar but less biased RMA-75
     method from McGee and Chen (2006).

     If the length 'x' is very large, it may be worth saving
     computation time by setting 'n.pts' to a value less than the total
     number of probes, for example 'n.pts=2^14'.

_V_a_l_u_e:

     A list containing the components 

     par: numeric vector giving estimated values of the mean and
          log-standard-deviation of the background-normal part and the
          log-mean of the signal-exponential part.

m2loglik: numeric scalar giving minus twice the maximized
          log-likelihood

convergence: integer code indicating successful convergence or
          otherwise of the optimization.

_A_u_t_h_o_r(_s):

     Gordon Smyth and Jeremy Silver

_R_e_f_e_r_e_n_c_e_s:

     McGee, M., and Chen, Z. (2006). Parameter estimation for the
     exponential-normal convolution model for background correction of
     Affymetrix GeneChip data. _Stat Appl Genet Mol Biol_, 5(1),
     Article 24.

     Ritchie, M. E., Silver, J., Oshlack, A., Silver, J., Holmes, M.,
     Diyagama, D., Holloway, A., and Smyth, G. K. (2007). A comparison
     of background correction methods for two-colour microarrays.
     _Bioinformatics_ <URL:
     http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btm412>

     Silver, J., Ritchie, M. E., and Smyth, G. K. (2008). Microarray
     background correction: maximum likelihood estimation for the
     normal-exponential convolution model. _Biostatistics_. To appear.
     <URL: http://www.statsci.org/smyth/pubs/normexp_28_Jul_2008.pdf>

_S_e_e _A_l_s_o:

     'normexp.signal, \code{bg.parameters}'

     An overview of background correction functions is given in
     '04.Background'.

_E_x_a_m_p_l_e_s:

     x <- c(2,3,1,10,3,20,5,6)
     out <- normexp.fit(x)
     normexp.signal(out$par, x=x)

