timeLinePlot-methods         package:flowViz         R Documentation

_P_l_o_t _c_h_a_n_n_e_l _v_a_l_u_e_s _a_g_a_i_n_s_t _t_i_m_e

_D_e_s_c_r_i_p_t_i_o_n:

     Plots values of one parameter for each flowFrame in a flowSet
     against time.

_D_e_t_a_i_l_s:

     Plotting flow cytometry data against the time domain can help to
     identify problems with the fluidics or drifts in the instrument
     setting during measurement runs.

     This function creates plots for all flowFrames in a flowSet for a
     given parameter against time. A barplot legend indicates the
     deviation from the median for each sample. There is also a
     flowFrame method, which will create a plot for a single flowFrame
     only.

     In addition, the function computes a quality score for each frame,
     which essentially is the sum of the positive distances of each bin
     mean from a frame-specific confidence interval, divided by the
     number of bins. Values larger than zero indicate a problem.

_V_a_l_u_e:

     A numeric vector of quality scores.

_M_e_t_h_o_d_s:

     General usage:

     'timeLinePlot(x, channel, type=c("stacked", "scaled", "native"),
     col, ylab=names(x), binSize, varCut=1, ...)'



     _x An object of class 'flowFrame' or 'flowSet' containing the data
          to be plotted.

     _c_h_a_n_n_e_l The parameter for which the data is to be plotted

     _t_y_p_e One in 'stacked', 'scaled' or 'native'. 'stacked' will plot
          the measurements for the frames on top of each other.
          'scaled' will align the median values around zero and
          'native' will plot the values in the original dimensions of
          the measurement range.

     _c_o_l Optional color parameter.

     _y_l_a_b The axis annotation to add on the y-axis for stacked plots.

     _b_i_n_S_i_z_e The number of events per bin. If not set, a reasonable
          default is computed.

     _v_a_r_C_u_t The cutoff in the adjusted variance to which the quality
          score is computed. Basically, all values that are outside of
          the confidence interval defined by '\[my - signma * varCut,
          my + sigma * varCut\]' will contribute to a positive quality
          score value.

     ... Further arguments that are passed on to the base plotting
          functions.




     _x = "_A_N_Y", _c_h_a_n_n_e_l = "_m_i_s_s_i_n_g" This casts a useful error message
          about missing inputs. 

     _x = "_f_l_o_w_F_r_a_m_e", _c_h_a_n_n_e_l = "_c_h_a_r_a_c_t_e_r" For a 'flowFrame' we only
          plot a single parameter against time. 

     _x = "_f_l_o_w_S_e_t", _c_h_a_n_n_e_l = "_c_h_a_r_a_c_t_e_r" For a 'flowSet', we plot the
          lines for a  single parameter against time for each
          'flowFrame' in the set. 


_A_u_t_h_o_r(_s):

     F. Hahne

_S_e_e _A_l_s_o:

     'flowFrame', 'flowSet'

_E_x_a_m_p_l_e_s:

     data(GvHD)
     opar <- par(ask=TRUE)

     res <- timeLinePlot(GvHD[[1]], "SSC-H")
     res

     res <- timeLinePlot(GvHD, "SSC-H")

     res <- timeLinePlot(GvHD, "SSC-H", type="scaled", varCut=4)

     res <- timeLinePlot(GvHD[1:4], "SSC-H", type="native", binSize=50)

     par(opar)

