ctQc                package:qpcrNorm                R Documentation

_Q_u_a_l_i_t_y _C_o_n_t_r_o_l _F_i_l_t_e_r _f_o_r _R_e_p_l_i_c_a_t_e _C_t _V_a_l_u_e_s

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

     This function applies a quality control filter to triplicate Ct
     values before  combining them into a single summary Ct measure. 

     Current implementation can only handle three replicates.

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

     ctQc(x)

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

       x: Matrix with three columns, corresponding to the triplicate Ct
          values. 

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

     Applying ctQc is an alternative to averaging the triplicate Ct
     values. This filter was originally developed by Yasumasa Kimura 
     1. For each primer, sort Ct values in ascending order so we have
     [ct1, ct2, ct3]. 2. Take two differences  difference1 := ct2 - ct1
     difference2 := ct3 - ct2 If either or both have ct 40, we don't
     calculate the difference and set it to region 4 in the next step.
     3. Take an average according to the differences. Here we have 2
     thresholds : 0.2 and 1.0. With the thresholds, we classify the 2
     differences into the below regions. region1 : difference <= 0.2
     region2 : 0.2 < difference <= 1.0 region3 : 1.0 < difference
     region4 : either or both ct are 40 If the 2 differences are in
     same region, we take an average of 3 ct values. If the 2
     differences are in different regions, we take an average of 2 ct
     values which are in smaller number region.

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

     Numeric vector of Ct values combined over the three replicates
     according to the QC filter.

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

     Yasumasa Kimura 
      Jess Mar jess@jimmy.harvard.edu

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

     'readQpcr', 'readQpcrBatch'

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

             ## myQpcrBatch <- readQpcrBatch(qc=T)   # reads in data from a batch of qPCR experiments, applies ctQc to raw Ct values

