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Table 1 Single-bias-removal location normalization techniques used in this study. These strategies remove spatial- or intensity effect in a single step. The abbreviations are as follows, (for a given microarray), M l : location-normalized log ratio; median(M): median value of non-normalized log ratios; lowess(rloc i , cloc i ): lowess curve fitted as a function of the row location (rloc i ) and column location (cloc i ) of spots in PT group i; median(M w ): median value of non-normalized log ratios within the window size determined by w; lowess(A): lowess curve fitted to an MA plot of spots on a microarray; lowess(A i ): lowess curve fitted to an MA plot of spots in PT group i; spline(A iset ): spline curve fitted to an MA plot of spots in the invariant set, iset; R l : location-normalized R value; qspline(G i ): qspline smoothing using geometric mean of the G channels of all arrays as a target array; G l : location-normalized G value; qspline(R t ): qspline smoothing using geometric mean of the R channels of all arrays as a target array.

From: Evaluation of normalization methods for cDNA microarray data by k-NN classification

Name * Description: Effect/Level Bioconductor R package/function(parameters)
N ONRM No normalization M l = M marray/maNorm(norm="none")
GMEDIAN Global M l = M - median(M) marray/maNorm (norm="median", subset = T)
SL LOESS Spatial/local lowess M l = M - loess(rloc i , cloc i ) marray/maNormMain (f.loc = list(maNorm2D(g="maPrintTip", subset = T, span = 0.4))
SL FILTERW3 Spatial/Local median filter
M l = M - median(M w ), W = 3 × 3
tRMA/SpatiallyNormalise** (M, width = 3, height = 3)
SL FILTERW7 Spatial/Local median filter
M l = M - median(M w ), W = 7 × 7
tRMA/SpatiallyNormalise** (M, width = 7, height = 7)
IG LOESS Intensity/Global lowess M l = M - loess(A) marray/maNorm (norm="loess", subset = TRUE, span = 0.4)
IL LOESS Intensity/Local lowess M l = M - loess(A i ) marray/maNorm (norm="printTipLoess", subset = T, span = 0.4)
IST SPLINE Intensity/Global spline M l = M - spline(A iset ) affy/normalize.invariantset**(prd.td = c(0.003, 0.007))
QSPLINEG Intensity/Global qspline
R l = R - qspline(G t ), G l = G - qspline(G t ), M l = log(R l / G l )
affy/R l ← normalize.qspline(R, 2^rowMeans(log2(G), na.rm = T), na.rm = T, *default*)
G l ← normalize.qspline(G, 2^rowMeans(log2(G), na.rm = T), na.rm = T, *default*)
QS PLINER Intensity/Global qspline
R l = R - qspline(R t ), G l = G - qspline(R t ), M l = log(R l / G l )
affy/ R l ← normalize.qspline(R, 2^rowMeans(log2(R), na.rm = T), na.rm = T, *default*)
G l ← normalize.qspline(G, 2^rowMeans(log2(R), na.rm = T), na.rm = T, *default*)
  1. * We adopted the terminology given in the table to avoid confusion within this work. Elsewhere, these methods are known as: GMEDIAN, global or median [4]; SL LOESS, 2D spatial [12]; SL FILTERW3, spatial normalization using median filter of the block size 3 × 3 [17]; SL FILTERW7, spatial normalization using median filter of the block size 7 × 7 [17]; IG LOESS, global loess [4, 26]; IL LOESS, print-tip loess [4]; IST SPLINE, invariant set normalization [38]; QS PLINER, qspline using geometric mean of the R channels of all arrays as the target array [13]; Q SPLINEG, qspline using geometric mean of the G channels of all arrays as the target array [13].
  2. ** The SpatiallyNormalise function in the tRMA package was modified to remove scale normalization. The normalize.invariantset function in Affy package was modified so that the function could be applied on cDNA microarray data.
  3. *default* The default parameters for Q SPLINEG and QS PLINER are (fit.iters = 5, min.offset = 5, spline.method="natural", smooth = T, spar = 0, p.min = 0, p.max = 1.0, incl.ends = T, converge = F)