anovaspatial              package:OLIN              R Documentation

_O_n_e-_f_a_c_t_o_r_i_a_l _A_N_O_V_A _a_s_s_e_s_s_i_n_g _s_p_a_t_i_a_l _b_i_a_s

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

     This function performs an one-factorial analysis of variance to
     test for spatial bias  for a single array. The predictor variable
     is the average logged intensity of both channels and the response
     variable is the logged fold-change.

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

     anovaspatial(obj,index,xN=5,yN=5,visu=FALSE)

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

     obj: object of class  marrayRaw or marrayNorm

   index: index of array (within 'obj') to be tested 

      xN: number of intervals in x-direction

      yN: number of intervals in y-direction

    visu: If visu=TRUE, results are visualised (see below)

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

     The function 'anovaspatial' performs a one-factorial ANOVA for
     objects of class marrayRaw or  marrayNorm. The predictor 
     variable is the average logged intensity of both channels
     ('A=0.5*(log2(Ch1)+log2(Ch2))'). 'Ch1,Ch2' are the fluorescence
     intensities of channel 1 and channel 2, respectively. The response
     variable is the logged fold-change   ('M=(log2(Ch2)-log2(Ch1))').
     The spot locations  on the array is divided into 'xN' intervals in
     x-direction and 'yN' intervals in y-direction. This division
     defines  ('xN x yN')  rectangular spatial blocks on  the array,
     and thus, ('xN x yN') levels (or treatments) for 'A'.  Note that 
     values chosen for 'xN' and 'yN'  should  divide the array columns
     and rows approx. equally. The null hypothesis is the equality of
     mean('M') of the different levels.  The model formula used by
     'anovaspatial' is M ~ (A - 1)  (without an intercept term).

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

     The return value is a list of summary statistics of the fitted 
     model as produced by 'summary.lm'.   For example, the squared
     multiple correlation coefficient R-square equals the proportion 
     of the variation of 'M' that can be related to the spot location
     (based on the chosen ANOVA.)  Optionally, the distribution of
     p-values (as derived by t-test and stated in the summary
     statistics)  can be  visualised.

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

     Matthias E. Futschik (<URL:
     http://itb.biologie.hu-berlin.de/~futschik>)

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

     'anova', 'summary.lm', 'anovaint', 'marrayRaw', 'marrayNorm'

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

     # CHECK RAW DATA FOR SPATIAL BIAS
     data(sw)
     print(anovaspatial(sw,index=1,xN=8,yN=8,visu=TRUE))

     # CHECK  DATA NORMALISED BY OLIN FOR SPATIAL BIAS
     data(sw.olin)
     print(anovaspatial(sw.olin,index=1,xN=8,yN=8,visu=TRUE)) 
     # note the different scale of the colour bar

