normalizeScale             package:limma             R Documentation

_N_o_r_m_a_l_i_z_e _C_o_l_u_m_n_s _o_f _a _M_a_t_r_i_x _t_o _h_a_v_e _t_h_e _S_a_m_e _S_c_a_l_e

_D_e_s_c_r_i_p_t_i_o_n:

     Performs scale normalization of an M-value matrix or an A-value
     matrix across a series of arrays. Users do not normally need to
     call these functions directly - use 'normalizeBetweenArrays'
     instead.

_U_s_a_g_e:

     normalizeMedianDeviations(x)
     normalizeMedians(x)

_A_r_g_u_m_e_n_t_s:

       x: numeric matrix

_D_e_t_a_i_l_s:

     If 'x' is a matrix of log-ratios of expression (M-values) then
     'normalizeMedianDeviations' is very similar to scaling to equalize
     the median absolute deviation (MAD) as in Yang et al (2001, 2002).
     Here the median-absolute value is used for preference to as to not
     re-center the M-values.

     'normalizeMedians' is used for A-values of overall expression.

_V_a_l_u_e:

     'normalizeMedianDeviations' produces a numeric matrix of the same
     size as that input which has been scaled so that each column as
     the same median-absolute value.

     'normalizeMedians' produces a numeric matrix which has been scaled
     so that each column has the same median-value.

_A_u_t_h_o_r(_s):

     Gordon Smyth

_S_e_e _A_l_s_o:

     An overview of LIMMA functions for normalization is given in
     4.Normalization.

_E_x_a_m_p_l_e_s:

     M <- cbind(Array1=rnorm(10),Array2=2*rnorm(10))
     normalizeMedianDeviations(M)

     A <- cbind(Array1=rlnorm(10),Array2=2*rlnorm(10))
     normalizeMedians(A)

