applyStickyError        package:simulatorAPMS        R Documentation

_F_u_n_c_t_i_o_n _t_h_a_t _t_a_k_e_s _s_t_i_c_k_y _p_r_o_t_e_i_n_s _a_n_d _c_a_l_c_u_l_a_t_e_s _F_P'_s

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

     The applyStickyError function simulates FP outputs for those
     proteins are known to attract many prey during the experiment. The
     parameters for the function implicitly define which protein, P, is
     sticky, since the notNoundProt is the set of proteins in the TSN
     which all do not interact with P. The rateStky is the rate at
     which the stickiness causes P to accumulate FP's. The return value
     are those proteins which will become the stickiness FP set for
     protein P in the simulated error model.

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

     applyStickyError(notFoundProt, rateSticky)

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

notFoundProt: A vector consisting of all the proteins that do not
          interact with protein P in the TSN 

rateSticky: The rate at which the sticky protein attracts other
          proteins ands records FP outputs in the experiment.

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

     This is essentially a function for recording more FP interactions
     among baits and non-prey. The function takes in a bait that is
     known to have some high level of finding prey as a result of the
     experimentation.

     In essence, the bait attracts and binds to other proteins with
     which it would normally not interact.

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

     The return value, ProtInErr, is a vector consisting of a subset of
     proteins from the set notFoundProt. This subset will be

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

     Tony Chiang

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

     'applyFPErrors', 'applyDeformationError'

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

     library(simulatorAPMS)
     data(TSNMatrix)
     exampleNotFoundPrey <- rownames(TSNMatrix)[1:50]
     applyStickyError(exampleNotFoundPrey, 0.9)

