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

Function that will investigate all possible pairings in a set of probes, calculate the Pearson correlation coefficient and plot them in a meaningful way

1 2 | ```
plotCoexpression(object, gene, probeData=NULL, verbose=TRUE, directions="all", correlationCutoff=0.5,
probeLevelInfo=c("probeid"))
``` |

`object` |
A ProbeLevelSet object or a regular ExpressionSet object (in which case a probeData argument is required). See getLocalProbeIntensities and related functions on how to create a ProbeLevelSet. |

`gene` |
A number of gene sequences as DNAstring, DNAStringSets or character vectors with sequence. |

`probeData` |
Optional if a ProbeLevelSet is submitted as object argument. Otherwise it must be a data frame with rownames corresponding to the featureNames of the ExpressionSet and a column named "sequence" with the probe sequences as character strings |

`verbose` |
TRUE or FALSE |

`directions` |
A character vector of the matching-directions that should be scanned (which combinations of complementary and reverse). Defaults to "all" which is shorthand for all possible directions, but can take anything from: c("matchForwardSense", "matchForwardAntisense", "matchReverseSense", "matchReverseAntisense") |

`correlationCutoff` |
A number between 0 and 1. The limit at which Pearson correlation (in absolute values) should not be plotted below. Defaults to 0.5 |

`probeLevelInfo` |
The information about each probe to include in the plot. Should be a vector of one or more of the following elements: probeid, probesetid, sequence. Default is only probeid. |

This function takes a ProbeLevelSet or an ExpressionSet + probeData and the sequence of a gene. It then calculates pairwise Pearson correlation coefficients between all possible combinations of probes. Then it assigns each probe to a location along the length of the gene and plots a relational graph showing which probes has high correlation coefficients. The correlation coefficients are sorted by absolute values meaning that it will also include the negative correlations.

No value, but plots a hapmap style plot of correlation values between all probes

Lasse Folkersen

1 2 3 | ```
data(exampleProbeLevelSet)
plotCoexpression(exampleProbeLevelSet, mrna, correlationCutoff=0.7, probeLevelInfo=c("probeid","sequence"))
``` |

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