spkGNN               package:spkTools               R Documentation

_G_e_n_e_s _N_e_e_d_e_d _t_o _D_e_t_e_c_t _N _T_r_u_e _P_o_s_i_t_i_v_e_s

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

     Computes the number of genes one would need to consider to obtain
     a given number of truly positive genes if one considered genes in
     order of decreasing observed fold change.

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

     spkGNN(n, n.expr, n.unexpr, AccuracySlope, AccuracySD, nullfc)

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

       n: the desired number of true positives

  n.expr: the actual number of truly expressed genes

n.unexpr: the actual number of truly unexpressed genes

AccuracySlope: the signal detect slope from the spkSlope function

AccuracySD: the standard deviation of the signal detect slope from the
          spkAccSD function

  nullfc: a vector of null fold changes from the spkBox function

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

     This function returns the expected number of genes one would have
     to consider to obtain N true positives under the given conditions.

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

     Matthew N. McCall

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

     data(affy)
     spkSlopeOut <- spkSlope(affy)
     spkBoxOut <- spkBox(affy, spkSlopeOut, fc=2)
     AccuracySlope <- round(spkSlopeOut$slope[-1], digits=2)
     AccuracySD <- round(spkAccSD(affy, spkSlopeOut), digits=2)
     spkGNN(n=25, n.expr=100, n.unexpr=10000, AccuracySlope[2],
     AccuracySD[2], spkBoxOut[[2]])

