plotVarMean             package:qpcrNorm             R Documentation

_C_o_n_s_t_r_u_c_t_s _s_c_a_t_t_e_r _p_l_o_t _t_o _c_o_m_p_a_r_e _t_h_e _e_f_f_e_c_t_s _o_f _t_w_o _n_o_r_m_a_l_i_z_a_t_i_o_n _a_l_g_o_r_i_t_h_m_s _o_n _a _q_P_C_R _d_a_t_a_s_e_t.

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

     This function makes a scatter plot which serves as a useful
     exploratory tool in evaluating whether one  normalization
     algorithm has been more effective than another on a given qPCR
     dataset.

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

     plotVarMean(qpcrBatch1, qpcrBatch2, normTag1 = "Normalization Type1", normTag2 = "Normalization Type2", ...)

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

qpcrBatch1: A 'qpcrBatch' object. 

qpcrBatch2: A 'qpcrBatch' object. 

normTag1: Character string denoting what normalization algorithm was
          used for this data set. 

normTag2: Character string denoting what normalization algorithm was
          used for this data set. 

     ...: Further arguments can be supplied to the 'plot' function. 

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

     For each gene, the function plots its log-transformed ratio of its
     expression variance in  one normalized dataset versus another
     normalized dataset, i.e. let Gij be the variance of  the
     expression values of gene i that have been normalized with method
     j.  We plot the natural log-transformed ratio of Gij to Gik on the
     y-axis,  and the average expression of gene i on the x-axis for
     all genes. /cr The red curve represents a smoothed lowess curve
     that has been fitted to reflect the overall  trend of the data.
     When the red curve drops below y = 0 (the blue dotted line) we
     know that method j  effects a greater reduction in the variation
     of the data over method k.  Similarly, when the red curve is above
     y = 0, method k is more effective in reducing the variation  in
     the data than method j. If the data from both methods have similar
     variances then the red curve  should remain at y = 0. Bolstad et
     al. (2003) originally used these plots for variance comparisons of
      different normalization methods for high density oligonucleotide
     array data.

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

     A 'plot' object.

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

     Jess Mar jess@jimmy.harvard.edu

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

     Bolstad B et al. A comparison of normalization methods for high
     density oligonucleotide array data based on variance and bias.
     Bioinformatics, 2003.

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

     'plot'

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

       # data(qpcrBatch.object)
       # mynormRI.data <- normQpcrRankInvariant(qpcrBatch.object, 1) 
       # mynormQuant.data <- normQpcrQuantile(qpcrBatch.object)
       # plotVarMean(mynormRI.data, mynormQuant.data, normTag1="Rank-Invariant", normTag2="Quantile", main="Comparing Two Data-driven Methods")  

