backgroundCorrect         package:beadarray         R Documentation

_B_a_c_k_g_r_o_u_n_d _C_o_r_r_e_c_t _a _B_e_a_d_L_e_v_e_l_L_i_s_t _o_b_j_e_c_t

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

     Adapted from 'limma' backgroundCorrect function to correct the
     foreground intensities of a BLData object using the background
     values.

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

     backgroundCorrect(object, method = "subtract", offset = 0, verbose = FALSE)

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

  object: a BeadLevelList object

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

  offset: numeric value to add to intensities

 verbose: logical. Used when method = '"normexp"'.  If TRUE, the
          parameters estimated by the model are 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.

     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"' the method of Edwards (2003) is used. If
     'method="normexp"' or '"rma"', a normal-exponential convolution
     model is fitted to the intensities, using different estimation
     procedures.  See Smyth (2005) for further details on normexp.

     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.

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

     An 'BeadLevelList' object in which the 'R' and 'G' intensities for
     each array are background corrected

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

     Mark Dunning based on the limma function

_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.

     Smyth, G. K. (2005). Limma: linear models for microarray data. In:
     Bioinformatics and Computational Biology Solutions using R and
     Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W.
     Huber (eds.), Springer, New York, pages 397-420.

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

     data(BLData)

     #default is to simply subtract Rb from R
     BLData.bc = backgroundCorrect(BLData)

     #Use 'minimum' method to stop negative values appearing
     BLData.min = backgroundCorrect(BLData, method="minimum")

