proboscis             package:MAQCsubset             R Documentation

_P_r_o_d_u_c_e _a _p_l_o_t _s_i_m_i_l_a_r _t_o _F_i_g_u_r_e _2 _o_f _t_h_e _S_h_i_p_p_y _M_A_Q_C _p_a_p_e_r (_P_M_I_D _1_6_9_6_4_2_2_6).

_D_e_s_c_r_i_p_t_i_o_n:

     Produce a plot similar to Figure 2 of the Shippy MAQC paper (PMID
     16964226).

_U_s_a_g_e:

     proboscis(es, site=1, ABp=0.001, CDp=0.01, mmrad=100)

_A_r_g_u_m_e_n_t_s:

      es: 'ExpressionSet' instance with MAQC assay results

    site: numeric code - site to be assessed

     ABp: ABp - p-value threshold to declare concentration of gene in
          sample A to be different from ehe concentration in sample B

     CDp: CDp - p-value threshold to declare concentration of gene in
          sample C to be different from the concentration in sample D

   mmrad: numeric radius of the moving mean used to smooth the
          proportions differentially expressed 

_D_e_t_a_i_l_s:

     Figure 2 of the Shippy paper consists of a collection of plots of
     estimated probabilities of self-consistent monotone titration -
     briefly, samples are such that A has 100% USRNA, B has 100% Ambion
     brain, C has 75% USRNA+25% brain, D has 25% USRNA, 75% brain. 
     Self-consistent monotone titration holds for gene g if microarray
     measures for that gene satisfy A > C > D > B or B > C > D > A. 
     The estimated probability functions look like a creature sticking
     its nose over a wall, thus the name of this function.

_V_a_l_u_e:

     an instance of 'proboStruct', for which a plot and lines method
     are available.

_A_u_t_h_o_r(_s):

     Vince Carey <stvjc@channing.harvard.edu>

_R_e_f_e_r_e_n_c_e_s:

     PMID 16964226

_E_x_a_m_p_l_e_s:

     data(afxsubRMAES)
     NN2 = proboscis(afxsubRMAES, site=2)
     plot(NN2)

