Bscore {cellHTS2}R Documentation

B score normalization

Description

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.

Usage

Bscore(object, save.model = FALSE)

Arguments

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.

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:

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.

Value

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.

Author(s)

Ligia Bras ligia@ebi.ac.uk

References

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.

See Also

medpolish, loess, locfit.robust, plotSpatialEffects, normalizePlates, summarizeChannels plateEffects

Examples

    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)

[Package cellHTS2 version 2.2.5 Index]