Bscore: B score normalization
In cellHTS2: Analysis of cell-based screens - revised version of cellHTS

Description Usage Arguments Details Value Author(s) References See Also Examples

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, on a per-plate basis, to account for row and column effects.

1	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.

This function is usually not called directly by the user, but from within the 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: In the original B score method, as presented by Malo et al., a further step is performed: for each plate p, each of the obtained residual values r_{ijp}'s are divided by the median absolute deviation of the residuals in plate p (MAD_p), resulting in:

r_{ijp}/MAD_p

The intention of such a further adjustment is to compensate for plate-to-plate variability in dynamic range. In the Bscore function, this step is not automatically 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

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")
xb <- Bscore(KcViabSmall, save.model = TRUE)
## Calling Bscore function from "normalizePlates" and adding the per-plate variance adjustment step:
xopt <- normalizePlates(KcViabSmall, method="Bscore", varianceAdjust="byPlate", save.model = TRUE)
## Access the slots overall.effects and rowcol.effects
ef1 = plateEffects(xb)
ef2 = plateEffects(xopt)


## double-check 
stopifnot(
  all(xb@rowcol.effects==xopt@rowcol.effects, na.rm=TRUE),
  all(xb@overall.effects==xopt@overall.effects, na.rm=TRUE),
  identical(ef1, ef2)
)