| spatialNormalization {cellHTS2} | R Documentation |
Correction of spatial plate effects of data stored in slot assayData of a cellHTS object by fitting a polynomial surface within each plate using local regression (loess or robust local fit). Uses a second degree polynomial (local quadratic fit). Only wells containing "sample" are considered to fit the models.
spatialNormalization(object, model="locfit", smoothPar=0.6, save.model=FALSE)
object |
a cellHTS object that has already been configured. |
model |
character indicated whether the polynomial surface should be fitted using robust "locfit" or "loess". The default is "locfit". |
smoothPar |
numeric. The default is smoothPar=0.6. The parameter which controls the degree of smoothing
(corresponds to 'span' argument of loess,
or to the parameter 'nn' of lp of locfit). |
save.model |
a logical value specifying whether the per-plate spatial effects should be stored in slot
rowcol.effects of object. See details. |
For convenience, this function should be called indireclty from normalizePlates function.
The normalization is performed separately for each replicate and channel, in a per-plate fashion using the chosen local regression model. The polynomical surfaces are fitted using the wells containing "sample".
If save.model=TRUE, the models row and column offsets are stored in the slot
rowcol.effects of object.
An object of class cellHTS with normalized data stored in slot assayData.
Furthermore, if save.model=TRUE, the row and column estimated offsets are stored in the slot rowcol.effects.
This slot is a 3D array with the same dimension as Data(object).
Moreover, the processing status of the cellHTS object is updated
in the slot state to object@state[["normalized"]]=TRUE.
Ligia Bras ligia@ebi.ac.uk
medpolish,
loess,
locfit,
plotSpatialEffects,
normalizePlates,
summarizeChannels,
plateEffects
data(KcViabSmall)
x <- KcViabSmall
xs <- spatialNormalization(x, model="loess", save.model = TRUE)
## Calling spatialNormalization function from "normalizePlates":
xopt <- normalizePlates(x, method="loess", varianceAdjust="none", save.model = TRUE)
all(xs@rowcol.effects == xopt@rowcol.effects, na.rm=TRUE)