`plotPCA`

in Package EDASeq `plotPCA`

produces a Principal Component Analysis (PCA) plot of the counts in `object`

1 2 3 4 |

`object` |
Either a numeric matrix or a |

`k` |
The number of principal components to be plotted. |

`labels` |
Logical. If |

`isLog` |
Logical. Set to |

`...` |
See |

The Principal Component Analysis (PCA) plot is a useful diagnostic plot to highlight differences in the distribution of replicate samples, by projecting the samples into a lower dimensional space.

If there is strong differential expression between two classes, one expects the samples to cluster by class in the first few Principal Components (PCs) (usually 2 or 3 components are enough). This plot also highlights possible batch effects and/or outlying samples.

`signature(x = "matrix")`

`signature(x = "SeqExpressionSet")`

1 2 3 4 5 6 7 8 9 10 11 | ```
library(yeastRNASeq)
data(geneLevelData)
mat <- as.matrix(geneLevelData)
data <- newSeqExpressionSet(mat,
phenoData=AnnotatedDataFrame(
data.frame(conditions=factor(c("mut", "mut", "wt", "wt")),
row.names=colnames(geneLevelData))))
plotPCA(data, col=rep(1:2, each=2))
``` |

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