meanSdPlot                package:vsn                R Documentation

_P_l_o_t _r_o_w _s_t_a_n_d_a_r_d _d_e_v_i_a_t_i_o_n_s _v_e_r_s_u_s _r_o_w _m_e_a_n_s

_D_e_s_c_r_i_p_t_i_o_n:

     Plot row standard deviations versus row means

_U_s_a_g_e:

     meanSdPlot(x, 
                ranks = TRUE,
                xlab  = ifelse(ranks, "rank(mean)", "mean"),
                ylab  = "sd",
                pch   = ".",
                col, ...)

_A_r_g_u_m_e_n_t_s:

       x: An object of class 'matrix' or 'exprSet'

   ranks: Logical, indicating whether the x-axis (means) should be
          plotted on the original scale (FALSE) or on the rank scale
          (TRUE). The latter distributes the data more evenly along the
          x-axis and allows a better visual assessment of the standard
          deviation as a function of  the mean.

    xlab: Character, label for the x-axis.

    ylab: Character, label for the y-axis.

     pch: Plot symbol.

     col: Color of plotted points. See details.

     ...: Further arguments that get passed to plot.default.

_D_e_t_a_i_l_s:

     Standard deviation and mean are calculated row-wise from the
     matrix 'exprs(x)'. The scatterplot of these versus each other
     allows to visually verify whether there is a dependence of the
     standard deviation (or variance) on the mean. The red dots depict
     the running median estimator (window-width 10%). If there is no
     variance-mean dependence, then the line formed by the red dots
     should be approximately horizontal.

     If the 'preprocessing' slot of the 'description' slot of 'x' is a
     'list' and contains an element named 'vsnTrimSelection', then the
     coloring of the points reflects the trimming that was used in the
     least trimmed sum of squares (LTS) estimation (see 'vsn'). If the
     condition does not apply, and 'col' is 'NULL', the points are
     drawn in black. If 'col' is not 'NULL', its value is used for the
     coloring of the points.

_V_a_l_u_e:

     The function is called for its side effect, creating a plot on the
     active graphics device.

_A_u_t_h_o_r(_s):

     Wolfgang Huber <URL: http://www.ebi.ac.uk/huber>

_S_e_e _A_l_s_o:

     'vsn'

_E_x_a_m_p_l_e_s:

       data(kidney)
       log.na = function(x) log(ifelse(x>0, x, NA))

       exprs(kidney) = log.na(exprs(kidney))
       meanSdPlot(kidney)

       ## ...try this out with non-logged data, the lymphoma data, your data...

