cellHTS (version 1.42.0)

rsa: Perform RSA ranking on the screening results.

Description

The RSA method ranks the resulting hit list of a screening experiment, taking into account the design of the screening library (i.e., multiple probes targeting the same effector molecule).

Usage

rsa(x, geneColumn="GeneID", lowerBound=0, upperBound=1, reverse=FALSE, drop=FALSE)

Arguments

x

Object derived from class cellHTS.

geneColumn

The name of the well annotation column to be used for the grouping of effector molecules and probes.

lowerBound

The lower boundary parameter for the RSA algorithm.

upperBound

The upper boundary parameter for the RSA algorithm.

reverse

Boolean. Reverse the ranking.

drop

Boolean. Drop all probes from the analysis for which no effector molecule is defined.

Value

• A data.frame with the following columns:
• Value of argument geneColumn:
  the target molecule identifier.
• Plate:
  the plate identifier.
• Well:
  the well identifier.
• Score:
  the probe score in the screen.
• RSARank:
  the computed RSA rank.
• ScoreRank:
  the rank based on a simple cutoff scheme.
• PValue:
  the computed RSA $p$-value.
• RSAHit:
  the RSA hit flag.
• #HitWell:
  the number of probes counted as positive RSA hits for a given target molecule.
• #TotalWell:
  the total number of probes for a given target molecule.
• %HitWell:
  the percentage of postive hits for a given molecule.

Details

The input argument x has to be a cellHTS2 object which has been scored, summarized and annotated. For details on the RSA algorithm please see the publication referenced below.

References

Renate Koenig, Chih-yuan Chiang, Buu P Tu, S Frank Yan, Paul D DeJesus, Angelica Romero, Tobias Bergauer, Anthony Orth, Ute Krueger, Yingyao Zhou & Sumit K Chanda: A probability-based approach for the analysis of large-scale RNAi screens

NATURE METHODS | VOL.4 NO.10 | OCTOBER 2007 | 847

See Also

cellHTS

Examples

data(KcViabSmall)
KcViabSmall <- scoreReplicates(KcViabSmall, sign="-", method="zscore")
KcViabSmall <- summarizeReplicates(KcViabSmall, summary="mean")
ranking <- rsa(KcViabSmall)
head(ranking)