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

Produces principal component plots from either unguided or guided PCA.

1 |

`out` |
object resulting from |

`ug` |
"guided" or "unguided". Do you want the cumulative variance from guided or unguided PCA plotted. |

`type` |
type of plot. Either "1v2" to plot the first two principal components, or "comp" to compare all principal component up to the level of |

`npcs` |
Number of principal compoents to plot when "comp" type is chosen. |

`...` |
any other |

This function plots either the first principal component versus the second principal component (`type="1v2"`

)
from guided or unguided PCA, or compares (`type="comp"`

) all combinations of the principal components up to
the value of `npcs`

.

Sarah Reese [email protected]

Reese, S. E., Archer, K. J., Therneau, T. M., Atkinson, E. J., Vachon, C. M., de Andrade, M., Kocher, J. A., and Eckel-Passow, J. E. A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal components analysis. Bioinformatics, (in review).

`gPCA.batchdetect`

, `gDist`

, `CumulativeVarPlot`

1 2 | ```
# PCplot(out,ug="unguided",type="1v2")
# PCplot(out,ug="unguided",type="comp",npcs=4)
``` |

gPCA documentation built on May 30, 2017, 8:16 a.m.

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