firstpass                package:SAGx                R Documentation

_F_i_r_s_t _p_a_s_s _d_e_s_c_r_i_p_t_i_o_n _o_f _G_e_n_e_C_h_i_p _d_a_t_a

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

     Does a first-pass analysis for a comparative experiment. This
     includes the calculation of means and confidence intervals for the
     groups, and finally a Kruskal-Wallis p-value for the null
     hypothesis of no difference

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

     firstpass(data = D, probes = probes , g, log = FALSE, present = NULL, labels = NULL, output.data = FALSE)

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

    data: A data frame with one array in each column

  probes: 

       g: A vector with the groups for the arrays, eg. TREATMENT and
          CONTROL

 present: A dataframe with the Present calls, 3 = P, 2 = M, 1 = A.

     log: if TRUE then data are log transformed through t(x) = log(1+x)
          and geometric means are calculated

  labels: a vector of labels given the group means

output.data: if T the raw data are included in the output

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

     A speed-up for Wilcoxon based on Kronecker products was put in
     place with SAGx v.1.4.5. Ties are currently not taken into account
     in Wilcoxon.

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

     A dataframe with the coumns PROBES, followed by group means and
     sd's, lower confidence intervals and then, upper  confidence
     interval (confidence level 95%), and followed a Kruskal-Wallis
     p-value, and finally  the input data,. If present names a
     dataframe holding the present calls the proportion present is
     calculated. Furthermore, if there are two groups the difference in
     group means is added.

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

     ## Not run: 
     # not run
      g <- c(rep(1,4),rep(2,4)); labs <- c("Mean Diet","Mean Control"); probes <- paste("Probe",1:1000)
      firstpass(data = utmat[1:2,], probes = probes[1:2], g, log = FALSE, labels = labs)
     #  Probesets         Mean Diet      Mean Control             LCL.1             LCL.2             UCL.1             UCL.2               pval
     #1   Probe 1 -12.3444460036497 -11.7495704973055 -12.9047961446666 -12.2832657957485 -11.7840958626327 -11.2158751988625 0.0433081428107922
     #2   Probe 2 -7.99773926405627 -8.02799133391929 -8.47704512876227 -8.19487551919835 -7.51843339935028 -7.86110714864023  0.772829992684449
     #          Difference  Subject 1  Subject 2  Subject 3  Subject 4  Subject 5  Subject 6  Subject 7  Subject 8
     #1 -0.594875506344176 -12.345150 -11.805071 -12.776232 -12.451332 -11.595748 -12.320430 -11.482349 -11.599755
     #2 0.0302520698630131  -7.660097  -8.157944  -8.404433  -7.768484  -7.979951  -8.017327  -8.197361  -7.917326
     ## End(Not run)

