plotComplex            package:apComplex            R Documentation

_R_e_n_d_e_r _c_o_m_p_l_e_x _e_s_t_i_m_a_t_e_s

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

     plotComplex renders complex estimates from the apComplex algorithm
     using Rgraphviz.

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

     plotComplex(complexMembers,g,VBs,VPs,geneName=FALSE,baitColor="yellow",preyColor="white",recipLineColor="red",unrecipBBLineColor="blue",unrecipBPLineColor="gray",y="neato")

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

complexMembers: A character vector of proteins composing a complex
          estimate.

       g: An object of class graph, the full bait-prey graph of AP-MS
          data used in analysis. complexMembers must be a subset of the
          node names of g.

     VBs: A vector of viable baits used in the AP-MS experiment.

     VPs: A vector of viable prey used in the AP-MS experiment.

geneName: A logical indicating whether or not nodes should be plotted
          with common gene names as labels rather than systematic
          names.

baitColor: Color of bait nodes.

preyColor: Color of prey nodes.

recipLineColor: Color of edges connecting baits which both detected
          each other as prey

unrecipBBLineColor: Color of edges connecting baits in which one bait
          finds the other as prey but not vice versa.

unrecipBPLineColor: Color of edges extending from baits to proteins
          that were only used as prey, hence reciprocity is not
          possible.

       y: Layout of plot

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

     This is a simple function for plotting complex estimates resulting
     from the apComplex algorithm.  Giving the upcoming changes in
     Rgraphviz, it will likely be changed substantially.

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

     A plotted graph of the complex estimate subgraph.

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

     Denise Scholtens

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

     Scholtens D and Gentleman R.  Making sense of high-throughput
     protein-protein interaction data.  Statistical Applications in
     Genetics and Molecular Biology 3, Article 39 (2004).

     Scholtens D, Vidal M, and Gentleman R.  Local modeling of global
     interactome networks.  Bioinformatics 21, 3548-3557 (2005).

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

     'findComplexes'

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

     data(apEX)
     data(apEXG)
     PCMG2 <- findComplexes(apEX,sensitivity=.7,specificity=.75)
     PCMG2sorted <- sortComplexes(PCMG2,apEX)

     VBs <- rownames(apEX)
     VPs <- setdiff(colnames(apEX),VBs)

     plotComplex(PCMG2sorted$MBME[[1]],g=apEXG,VBs=VBs, VPs=VPs)

