curveFit          package:diffGeneAnalysis          R Documentation

_C_u_r_v_e_F_i_t _d_a_t_a _t_o _a _G_a_u_s_s_i_a_n _d_i_s_t_r_i_b_u_t_i_o_n

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

     CurveFit takes a vector of chipdata from microarray slides and
     fits the data to a Gaussian distribution through a non-linear
     least-squares optimization algorithm.The results are graphically
     depicted in a series of histograms. Each histogram represents a
     different initial seed (left to right: 2 bins, 3 bins, 4 bins, 4.5
     bins, 5 bins, and 5.5 bins) that is passed to the curve fitting
     algorithm. The resulting fit for each histogram is superimposed
     with a solid blue line.The user is then able to visually select
     the 'best' fit.

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

     curveFit(chipdata, plot)

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

chipdata: a vector of chipdata from microarray slides.

    plot: plot can take values of 1 or 0. If plot is 1 then  the
          histogram with the curve fit will be shown graphically.

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

     an object res which is a list containing the following components.
     res1]: mean of the computed background. res[2]: standard deviation
     of the computed background.

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

     Choudary L Jagarlamudi

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

     Dozmorov I,Centola,M. An associative analysis of gene expression
     array data. Bioinformatics.2003 Jan22;19(2):204-11

     Knowlton N,Dozmorov I, Centola M. Microarray data Analysis Tool
     box(MDAT): for normalization,adjustment and analysis of gene
     expression data. Bioonformatics.2004 Dec 12;20(18):3687-90

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

     #see normalize for details.

