plot.ProbBin.FCS          package:rflowcyt          R Documentation

_P_l_o_t_s _a _P_r_o_b_B_i_n._F_C_S _o_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     A ProbBin.FCS object plot results in two histograms-one for the 
     stimulated sample and one for the unstimulated sample.

_U_s_a_g_e:

       plot.ProbBin.FCS(x, xlab=x$varname,
                    xlim=c(min(c(round(range(x$st.hist$breaks),1) + 1, 
                       round(range(x$unst.hist$breaks),1) + 1)), 
                       max(c(round(range(x$st.hist$breaks),1) + 1, 
                       round(range(x$unst.hist$breaks),1) + 1))),
                 
                   main="", 
                   labels=FALSE, 
                   freq=FALSE, plots.made=c("both", "stimulated", "unstimulated"), ...)

_A_r_g_u_m_e_n_t_s:

       x: ProbBin.FCS object

    xlab: Character string of the x-axis;  default is the variable name

    xlim: vector of length 2 denoting the minimum and the maximum value
          of the  breakpoint values, x-axis; default is the minimum and
          the maximum of the breakpoints for both stimulated and
          unstimulated samples

    main: character string of the title of the file (ie, individual id
          number)

  labels: Boolean; if TRUE, then the number/precentage in each bin is
          printed on the histogram, otherwise it is not; default is
          FALS

    freq: Boolean; if TRUE, then the histogram is in terms of counts;
          if FALSE, then the histogram is in terms of relative
          frequencies/precentages; if TRUE and the areas in plot are
          wrong is output as a warning.

plots.made: character string denoting which histogram plot should be 
          displayed; default is "both"

     ...: plotting options such as 'ylab' and 'ylim' to pass to hist 

_V_a_l_u_e:

     Two histograms (one of the stimulated sample, and the other of the
     unstimulated sample) are displayed or only one histogram plot 
     specified by the user will be displayed.

_A_u_t_h_o_r(_s):

     A.J. Rossini & J.Y. Wan

_R_e_f_e_r_e_n_c_e_s:

     Mario Roederer, et al. "Probability Binning Comparison: A Metric
     for Quantitating Univariate Distribution Differences" Cytometry
     45:37-46 (2001).

_S_e_e _A_l_s_o:

     'hist', 'ProbBin.FCS'

_E_x_a_m_p_l_e_s:

     if (require(rfcdmin)){

     if (!( is.element("st.1829", objects()) & is.element("unst.1829",
     objects()) )){
     ## obtaining the FCS objects from VRC data
     data(VRCmin)
     }

     ## This only serves as an example.
     ## Gating/subsetting should precede this analysis
     IFN.gamma.1<-unst.1829@data[1:2000,4]
     IFN.gamma.2<-st.1829@data[1:2000,4]

     #Probability binning using the control dataset to determine the breaks
     PB1<-ProbBin.FCS(IFN.gamma.1, IFN.gamma.2, 200,
     varname=colnames(unst.1829@data)[4], PBspec="by.control",MY.DEBUG=FALSE)

     ## Probability Binning using the combined dataset (control and stimulated)
     ## to determing the breaks
     PB2<-ProbBin.FCS(IFN.gamma.1, IFN.gamma.2, 200,
     varname=colnames(unst.1829@data)[4], PBspec="combined",MY.DEBUG=FALSE)

     if (interactive()){
     par(mfrow=c(2,2))
     ## plots both plots
     plot(PB1, ylim=c(0,500),main="Prob Binning using the Control dataset")

     ## plots only the unstimulated
     plot(PB2, main="Prob Binning using the Combined Dataset", plots.made="unstimulated")

     ## plots only the stimulated
     plot(PB2, main="Prob Binning using the Combined Dataset", plots.made="stimulated")
     }

     }

