Bscore {cellHTS2} | R Documentation |
Correction of plate and spatial effects of data stored in slot assayData
of a cellHTS
object using the B score method (without variance adjustment of the residuals).
Using this method, a two-way median polish is fitted in a per-plate basis to account for row and column effects.
NOTE: the obtained residuals within each plate are NOT further divided by their median absolute deviations to standardize for plate-to-plate variability.
Bscore(object, save.model = FALSE)
object |
a cellHTS object that has already been configured. See details. |
save.model |
a logical value specifying whether the per-plate models should be stored in slots rowcol.effects and overal.effects . See details. |
For convenience, this function should be called indireclty from normalizePlates
function.
The normalization is performed in a per-plate fashion using the B score method, for each replicate and channel.
In the B score method, the residual r_{ijp} of the measurement for row i and column j on the p-th plate is obtained by fitting a two-way median polish, in order to account for both row and column effects within the plate:
r_{ijp} = y_{ijp} - yest_{ijp} = y_{ijp} - (mu_p + R_{ip} + C_{jp})
y_{ijp} is the measurement value in row i and column j of plate p
(taken from slot assayData
- only sample wells are considered), and
yest_{ijp} is the corresponding fitted value.
This is defined as the sum between the estimated average
of the plate (mu_p), the estimated systematic offset for row i
(R_{ip}), and the systematic offset for column j (C_{jp}).
NOTE:
Bscore_{ijp} = r_{ijp}/MAD_p
Bscore
function, this step is not performed, but can be done if B score normalization is called using the function normalizePlates
with arguments method="Bscore"
and varianceAdjust="byPlate"
. See the latter function for more details.
If save.model=TRUE
, the models row and column offsets and overall offsets are stored in the slots
rowcol.effects
and overall.effects
of object
.
An object of class cellHTS
with B-score normalized data stored in slot assayData
.
Furthermore, if save.model=TRUE
, the row and column effects and the overall effects are stored in slots rowcol.effects
and overall.effects
, respectively.
The latter slots are arrays with the same dimension as Data(object)
, except the
overall.effects
slot, which has dimensions nr Plates x nr Samples x nr Channels
.
After calling this function, the processing status of the cellHTS
object is updated
in the slot state
to object@state["normalized"]=TRUE
.
Ligia Bras ligia@ebi.ac.uk
Brideau, C., Gunter, B., Pikounis, B. and Liaw, A. (2003) Improved statistical methods for hit selection in high-throughput screening, J. Biomol. Screen 8, 634–647.
Malo, N., Hanley, J.A., Cerquozzi, S., Pelletier, J. and Nadon, R. (2006) Statistical practice in high-throughput screening data analysis, Nature Biotechn 24(2), 167–175.
Boutros, M., Br'as, L.P. and Huber, W. (2006) Analysis of cell-based RNAi screens, Genome Biology 7, R66.
medpolish
,
loess
,
locfit.robust
,
plotSpatialEffects
,
normalizePlates
,
summarizeChannels
plateEffects
data(KcViabSmall) x <- KcViabSmall xb <- Bscore(x, save.model = TRUE) ## Calling Bscore function from "normalizePlates" and adding the per-plate variance adjustment step: xopt <- normalizePlates(x, method="Bscore", varianceAdjust="byPlate", save.model = TRUE) all(xb@rowcol.effects==xopt@rowcol.effects, na.rm=TRUE) all(xb@overall.effects==xopt@overall.effects, na.rm=TRUE) ## Access the slots overall.effects and rowcol.effects ef1 = plateEffects(xb) ef2 = plateEffects(xopt) identical(ef1, ef2)