View source: R/scmet_simulate.R

scmet_simulate_diff | R Documentation |

General function for simulating two methylation datasets for
performing differential methylation analysis. Differential analysis can be
either performed in detecting changes in mean or variability of methylation
patterns between the two groups. Similar to `scmet_simulate`

,
the function allows inclusion of covariates X that explain differences in
mean methylation levels. Or also defining the trend for the mean -
overdispersion relationship.

scmet_simulate_diff( N_feat = 100, N_cells = 50, N_cpgs = 15, L = 4, diff_feat_prcg_mu = 0, diff_feat_prcg_gamma = 0.2, OR_change_mu = 3, OR_change_gamma = 3, X = NULL, w_mu = c(-0.5, -1.5), s_mu = 1, w_gamma = NULL, s_gamma = 0.3, rbf_c = 1, cells_range = c(0.4, 0.8), cpgs_range = c(0.4, 0.8) )

`N_feat` |
Total number of features (genomics regions). |

`N_cells` |
Maximum number of cells. |

`N_cpgs` |
Maximum number of CpGs per cell and feature. |

`L` |
Total number of radial basis functions (RBFs) to fit the mean-overdispersion trend. For L = 1, this reduces to a model that does not correct for the mean-overdispersion relationship. |

`diff_feat_prcg_mu` |
Percentage of features (betwen 0 and 1) that show differential mean methylation between the two groups. |

`diff_feat_prcg_gamma` |
Percentage of features (betwen 0 and 1) that show differential variability between the two groups. |

`OR_change_mu` |
Effect size change (in terms of odds ratio) of mean methylation between the two groups. |

`OR_change_gamma` |
Effect size change (in terms of odds ratio) of methylation variability between the two groups. |

`X` |
Covariates which might explain variability in mean (methylation). If X = NULL, a 2-dim matrix will be generated, first column containing intercept term (all values = 1), and second colunn random generated covariates. |

`w_mu` |
Regression coefficients for covariates X. Should match number of columns of X. |

`s_mu` |
Standard deviation for mean parameter |

`w_gamma` |
Regression coefficients of the basis functions. Should match the value of L. If NULL, random coefficients will be generated. |

`s_gamma` |
Standard deviation of dispersion parameter |

`rbf_c` |
Scale parameter for empirically computing the variance of the RBFs. |

`cells_range` |
Range (betwen 0 and 1) to randomly (sub)sample the number of cells per feature. |

`cpgs_range` |
Range (betwen 0 and 1) to randomly (sub)sample the number of CpGs per cell and feature. |

Methylation data from two cell populations/conditions.

sim_diff <- scmet_simulate_diff(N_feat = 150, N_cells = 100, N_cpgs = 15, L = 4)

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