plotFeature               package:HELP               R Documentation

_P_l_o_t _f_e_a_t_u_r_e _v_e_r_s_u_s _t_w_o-_c_o_l_o_r _i_n_t_e_n_s_i_t_y

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

     Graphical display of featureData (ex: fragment size) versus
     two-color signal intensity data

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

     plotFeature(x, y, ...)

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

       x: matrix of numerical data to be plotted, with two columns (one
          for each signal channel). 'x' can also be of class
          '"ExpressionSet"'. 

       y: an additional vector of numerical data to be used for
          feature. If 'y' is missing, the function will attempt to fill
          a value from 'featureData' in 'x'. 

     ...: Arguments to be passed to methods (see
          'plotFeature-methods'):

     '_e_l_e_m_e_n_t._x' which element of 'AssayData' to use (for signal
          channel 1) for a given 'ExpressionSet' input (default is
          '"exprs"') 

     '_e_l_e_m_e_n_t._y' which element of 'AssayData' to use (for signal
          channel 2) for a given 'ExpressionSet' input (default is
          '"exprs2"') 

     '_s_a_m_p_l_e' which element of 'sampleNames' to use as data (default is
          1). Can be a character matching a sample name or simply an
          integer indicating which sample to choose. 

     '_f_e_a_t_u_r_e' which element of 'featureData' to use as plotting
          feature (default is 1). Can be a character matching
          'varLabel' or simply an integer indicating which feature to
          choose. 

     '_f_e_a_t_u_r_e._r_a_n_d_o_m' which element of 'featureData' to use to identify
          random probes (default is '"TYPE"'). Can be a character
          matching 'varLabel' or simply an integer indicating which
          feature to choose. 

     '_w_h_i_c_h._r_a_n_d_o_m' an integer vector specifying which rows of data
          correspond to random probes. if 'NULL' (default), the
          function will attempt to identify random probes using
          'featureData'. 

     '_r_a_n_d_o_m._f_l_a_g' a character specifying the label for random probes
          in 'feature.random' from 'featureData'. Default is '"RAND"'. 

     '_n_a._r_m' logical; if 'TRUE' (default), missing values are removed
          from 'x'. If 'FALSE' any missing values cause an error.

     '_l_i_m_i_t' numerical input specifying the maximum number of points to
          plot (default is 10,000). if 'NULL', all points will be used.

     '_c_u_t_o_f_f' a numerical input specifying the value below which signal
          intensities from channel 1 can be considered "failed" probes.
          If 'NULL' (default), the function will attempt to calculate a
          cutoff from random probe information.

     '_c_u_t_o_f_f_2' a numerical input specifying the value below which
          signal intensities from channel 2 can be considered "failed"
          probes. If 'NULL' (default), the function will attempt to
          calculate a cutoff from random probe information.

     '_m_a_i_n' an overall title for the plot: see 'title'. 

     '_x_l_a_b' a title for the x axis (default is '"Fragment size (bp)"'):
          see 'title'. 

     '_y_l_a_b' a title for the y axis for signal channel 1 (default is
          '"log(MspI)"'): see 'title'. 

     '_y_l_a_b_2' a title for the y axis for signal channel 2 (default is
          '"log(HpaII)"'): see 'title'. 

     '_c_e_x' numerical value (default is 0.2) giving the amount by which
          plotting text and symbols should be scaled relative to the
          default.  

     '...' other arguments to be passed to 'plot'. See 'plot'.   

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

     Reid F. Thompson (rthompso@aecom.yu.edu)

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

     'plotFeature-methods'

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

     #demo(pipeline,package="HELP")

     msp1 <- sample(8000:16000/1000, size=1000)
     msp1 <- msp1[order(msp1)]
     hpa2 <- sample(8000:16000/1000, size=1000)
     hpa2 <- hpa2[order(hpa2)]
     size <- sample((1:1000)*1.8+200, size=1000)
     rand <- which.min(abs(msp1-quantile(msp1, 0.25)))
     plotFeature(cbind(msp1, hpa2), size, which.random=(rand-20):(rand+20), main="Random")

     #rm(msp1, hpa2, size, rand)

