OLIN (version 1.50.0)

anovaspatial: One-factorial ANOVA assessing spatial bias

Description

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.

Usage

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

Arguments

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)

Value

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.

Details

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).

See Also

anova, summary.lm, anovaint, marrayRaw, marrayNorm

Examples

# 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