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

Generate 2d or 3d sample relationship plot based on principal component analysis, multidimensional scaling, etc.

1 |

`x` |
A matrix of numeric values |

`subset` |
A numeric value indicating the number of genes that is randomly selected for pca analysis. Default to NULL, which means no subsetting procedure will be done |

`cv.Th` |
If subset = NULL, a numeric value indicating threshold of coeffcient of variation in selecting genes |

`var.Th` |
A numeric value indicating threshold of variation in selecting genes. This is only used when subset and cv.Th are both set to NULL |

`mean.Th` |
Similar to var.Th, a numeric value indicating threshold of mean value in selecting genes. This is only used when subset and cv.Th are both set to NULL |

`standardize` |
Whether to standardize samples so that each sample has mean 0 and variance 1. Default to TRUE |

`method` |
One of "cluster", "mds", "pca". Please refer to details section |

`dimension` |
Numeric vector indicating the number of dimensions you would like to generate the figure |

`color` |
Color for points when 'mds' or 'pca' is chosen as method |

`princurve` |
Logical value indicating whether to generate a principal curve. Please refer to details |

`lwd` |
The line width for principal curve |

`starts` |
Providing the starting point for principal curve. Please refer to details |

`col.curve` |
The color of principal curve |

`text` |
Logical value indicating whether text is added as label to the figure |

`main` |
Main title for the figure |

`psi` |
Integer value indicating point size |

`type` |
For the default method, a single character indicating the type of item to plot. Supported types are: 'p' for points, 's' for spheres, 'l' for lines, 'h' for line segments from z = 0, and 'n' for nothing. |

`...` |
Further arguments will be ignored |

If method = 'cluster', `hclust`

is used; if method = 'mds', `cmdscale`

is used; if method = 'pca', `prcomp`

is used.

If princurve is set to TURE, then fits a principal curve which describes a smooth curve that passes through the middle of the data x in an orthogonal sense. This curve is a nonparametric generalization of a linear principal component. For details of principal curve, please refer to `principal.curve`

. When princurve is set to TRUE, you need to provide a starting point for principal curve as `starts`

argument. `starts`

is basically a logical vector of the same length as number of samples, that tells you which sample will be used as starting point.

If method = 'cluster': a 'hclust' object If method = 'mds' or 'pca', a data.frame containing user specified number of principal components.

Yuanhang Liu

https://github.com/Liuy12/MBDDiff

`cmdscale`

, `hclust`

, `prcomp`

, `principal.curve`

1 2 3 4 5 6 7 8 9 | ```
## Not run:
data(PromoterCount)
Condition <- c(rep('C1', 3), rep('C2', 3))
TestStat <- MBDDiff(Promoter, Background, Condition)
MBD <- TestStat[[1]]
Norm_count <- counts(MBD, normalized = TRUE)
pcaplot(Norm_count, cv.Th = 0.1, method = 'pca', dimension = c(1,2,3), princurve = TRUE, starts = c(1,1,1,0,0,0))
## End(Not run)
``` |

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