backgroundCorrect           package:limma           R Documentation

_C_o_r_r_e_c_t _I_n_t_e_n_s_i_t_i_e_s _f_o_r _B_a_c_k_g_r_o_u_n_d

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

     Background correct microarray expression intensities.

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

     backgroundCorrect(RG, method="subtract", offset=0, printer=RG$printer, verbose=TRUE)

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

      RG: an 'RGList' object or a unclassed list containing the same
          components as an 'RGList'

  method: character string specifying correction method.  Possible
          values are '"none"', '"subtract"', '"half"', '"minimum"',
          '"movingmin"', '"edwards"' or 'normexp'.

  offset: numeric value to add to intensities

 printer: a list containing printer layout information, see
          'PrintLayout-class'

 verbose: logical, should progress be reported to standard output

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

     If 'method="none"' then the corrected intensities are equal to the
     foreground intensities, i.e., the background intensities are
     treated as zero. If 'method="subtract"' then this function simply
     subtracts the background intensities from the foreground
     intensities which is the usual background correction method. If
     'method="movingmin"' then the background estimates are replaced
     with the minimums of the backgrounds of the spot and its eight
     neighbors, i.e., the background is replaced by a moving minimum of
     3x3 grids of spots.

     The remaining methods are all designed to produce positive
     corrected intensities. If 'method="half"' then any intensity which
     is less than 0.5 after background subtraction is reset to be equal
     to 0.5. If 'method="minimum"' then any intensity which is zero or
     negative after background subtraction is set equal to half the
     minimum of the positive corrected intensities for that array. If
     'method="edwards"' a log-linear interpolation method is used to
     adjust lower intensities as in Edwards (2003). If
     'method="normexp"' a convolution of normal and exponential
     distributions is fitted to the foreground intensities using the
     background intensities as a covariate, and the expected signal
     given the observed foreground becomes the corrected intensity. See
     'fit.normexp' for more details. This results in a smooth monotonic
     transformation of the background subtracted intensities such that
     all the corrected intensities are positive.

     The 'offset' can be used to add a constant to the intensities
     before log-transforming, so that the log-ratios are shrunk towards
     zero at the lower intensities. This may eliminate or reverse the
     usual 'fanning' of log-ratios at low intensities associated with
     local background subtraction.

     Background correction (background subtraction) is also performed
     by the 'normalizeWithinArrays' method for 'RGList' objects, so it
     is not necessary to call 'backgroundCorrect' directly unless one
     wants to use a method other than simple subtraction. Calling
     'backgroundCorrect' before 'normalizeWithinArrays' will over-ride
     the default background correction.

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

     An 'RGList' object in which components 'R' and 'G' are background
     corrected and components 'Rb' and 'Gb' are removed.

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

     Gordon Smyth

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

     Edwards, D. E. (2003). Non-linear normalization and background
     correction in one-channel cDNA microarray studies _Bioinformatics_
     19, 825-833. 

     Yang, Y. H., Buckley, M. J., Dudoit, S., and Speed, T. P. (2002).
     Comparison of methods for image analysis on cDNA microarray data.
     _Journal of Computational and Graphical Statistics_ 11, 108-136.

     Yang, Y. H., Buckley, M. J., and Speed, T. P. (2001). Analysis of
     microarray images. _Briefings in Bioinformatics_ 2, 341-349.

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

     An overview of normalization and background correction functions
     is given in '4.Normalization'.

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

     RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
     backgroundCorrect(RG)
     backgroundCorrect(RG, method="half")
     backgroundCorrect(RG, method="minimum")
     backgroundCorrect(RG, offset=5)

