probeR                package:ProbeR                R Documentation

_C_a_l_c_u_l_a_t_e _t_h_e _r_e_l_i_a_b_i_l_i_t_y _o_f _o_n_e _g_e_n_e

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

     calculate the reliability of one gene using summary variance and
     probe data variance

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

     probeR(summary.value, probe.value)

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

summary.value: the summary values of a specific gene for all the chips

probe.value: the probe level data of a specific gene. It is a matrix
          that the number of row is the number of probes and the number
          of column is the number of chips

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

summary.var : the variance of the summary values

probe.var : the variance of the probe level data

probe.n : the number of probes

reliability : the reliability 


     ...

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

     Eun-Kyung Lee, Dianne Cook, Heike Hofmann, Maneesha Aluru, and
     Steve Rodermel

_R_e_f_e_r_e_n_c_e_s:

     Using Reliability with Gene Expression Models

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

     'probeR.wholegene', ~~~

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

     library(affy)
     data(affybatch.example)
     eset<-expresso(affybatch.example,bg.correct=FALSE,normalize.method="quantiles",pmcorrect.method="pmonly",summary.method="medianpolish")
     data.summary<-exprs(eset)
     probe.norm<-normalize.AffyBatch.quantiles(affybatch.example,type="pmonly")
     data.probe<-log(probes(probe.norm),base=2)
     summary.value<-data.summary[1,]
     probe.value<-data.probe[1:16,]
     probeR(summary.value,probe.value)
     data.rel<-probeR.wholegene(data.summary,data.probe)

