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).

1 |

`x` |
Object derived from class |

`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. |

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.

A data.frame with the following columns:

`Value of argument ` |
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. |

Florian Hahne florian.hahne@novartis.com

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*

1 2 3 4 5 | ```
data(KcViabSmall)
KcViabSmall <- scoreReplicates(KcViabSmall, sign="-", method="zscore")
KcViabSmall <- summarizeReplicates(KcViabSmall, summary="mean")
ranking <- rsa(KcViabSmall)
head(ranking)
``` |

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