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)