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*) |