Description Usage Arguments Details Value Author(s) Examples

The assign.mcmc function uses a Bayesian sparse factor analysis model to estimate the adaptive baseline/background, adaptive pathway signature, and pathway activation status of individual test (disease) samples.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
assign.mcmc(
Y,
Bg,
X,
Delta_prior_p,
iter = 2000,
adaptive_B = TRUE,
adaptive_S = FALSE,
mixture_beta = TRUE,
sigma_sZero = 0.01,
sigma_sNonZero = 1,
p_beta = 0.01,
sigma_bZero = 0.01,
sigma_bNonZero = 1,
alpha_tau = 1,
beta_tau = 0.01,
Bg_zeroPrior = TRUE,
S_zeroPrior = FALSE,
ECM = FALSE,
progress_bar = TRUE
)
``` |

`Y` |
The G x J matrix of genomic measures (i.g., gene expression) of test samples. Y is the testData_sub variable returned from the data.process function. Genes/probes present in at least one pathway signature are retained. |

`Bg` |
The G x 1 vector of genomic measures of the baseline/background (B). Bg is the B_vector variable returned from the data.process function. Bg is the starting value of baseline/background level in the MCMC chain. |

`X` |
The G x K matrix of genomic measures of the signature. X is the S_matrix variable returned from the data.process function. X is the starting value of pathway signatures in the MCMC chain. |

`Delta_prior_p` |
The G x K matrix of prior probability of a gene being "significant" in its associated pathway. Delta_prior_p is the Pi_matrix variable returned from the data.process function. |

`iter` |
The number of iterations in the MCMC. The default is 2000. |

`adaptive_B` |
Logicals. If TRUE, the model adapts the baseline/background (B) of genomic measures for the test samples. The default is TRUE. |

`adaptive_S` |
Logicals. If TRUE, the model adapts the signatures (S) of genomic measures for the test samples. The default is FALSE. |

`mixture_beta` |
Logicals. If TRUE, elements of the pathway activation matrix are modeled by a spike-and-slab mixture distribution. The default is TRUE. |

`sigma_sZero` |
Each element of the signature matrix (S) is modeled by a spike-and-slab mixture distribution. Sigma_sZero is the variance of the spike normal distribution. The default is 0.01. |

`sigma_sNonZero` |
Each element of the signature matrix (S) is modeled by a spike-and-slab mixture distribution. Sigma_sNonZero is the variance of the slab normal distribution. The default is 1. |

`p_beta` |
p_beta is the prior probability of a pathway being activated in individual test samples. The default is 0.01. |

`sigma_bZero` |
Each element of the pathway activation matrix (A) is modeled by a spike-and-slab mixture distribution. sigma_bZero is the variance of the spike normal distribution. The default is 0.01. |

`sigma_bNonZero` |
Each element of the pathway activation matrix (A) is modeled by a spike-and-slab mixture distribution. sigma_bNonZero is the variance of the slab normal distribution. The default is 1. |

`alpha_tau` |
The shape parameter of the precision (inverse of the variance) of a gene. The default is 1. |

`beta_tau` |
The rate parameter of the precision (inverse of the variance) of a gene. The default is 0.01. |

`Bg_zeroPrior` |
Logicals. If TRUE, the prior distribution of baseline/background level follows a normal distribution with mean zero. The default is TRUE. |

`S_zeroPrior` |
Logicals. If TRUE, the prior distribution of signature follows a normal distribution with mean zero. The default is TRUE. |

`ECM` |
Logicals. If TRUE, ECM algorithm, rather than Gibbs sampling, is applied to approximate the model parameters. The default is FALSE. |

`progress_bar` |
Display a progress bar for MCMC. Default is TRUE. |

The assign.mcmc function can be set as following major modes. The combination of logical values of adaptive_B, adaptive_S and mixture_beta can form different modes.

Mode A: adaptive_B = FALSE, adaptive_S = FALSE, mixture_beta = FALSE. This is a regression mode without adaptation of baseline/background, signature, and no shrinkage of the pathway activation level.

Mode B: adaptive_B = TRUE, adaptive_S = FALSE, mixture_beta = FALSE. This is a regression mode with adaptation of baseline/background, but without signature, and with no shrinkage of the pathway activation level.

Mode C: adaptive_B = TRUE, adaptive_S = FALSE, mixture_beta = TRUE. This is a regression mode with adaptation of baseline/background, but without signature, and with shrinkage of the pathway activation level when it is not significantly activated.

Mode D: adaptive_B = TRUE, adaptive_S = TRUE, mixture_beta = TRUE. This is a Bayesian factor analysis mode with adaptation of baseline/background, adaptation signature, and with shrinkage of the pathway activation level.

`beta_mcmc` |
The iter x K x J array of the pathway activation level estimated in every iteration of MCMC. |

`tau2_mcmc` |
The iter x G matrix of the precision of genes estimated in every iteration of MCMC |

`gamma_mcmc` |
The iter x K x J array of probability of pathway being activated estimated in every iteration of MCMC. |

`kappa_mcmc` |
The iter x K x J array of pathway activation level (adjusted beta scaling between 0 and 1) estimated in every iteration of MCMC.) |

`S_mcmc` |
The iter x G x K array of signature estimated in every iteration of MCMC. |

`Delta_mcmc` |
The iter x G x K array of binary indicator of a gene being significant estimated in every iteration of MCMC. |

Ying Shen

1 2 3 4 5 6 | ```
mcmc.chain <- assign.mcmc(Y=processed.data$testData_sub,
Bg = processed.data$B_vector,
X=processed.data$S_matrix,
Delta_prior_p = processed.data$Pi_matrix,
iter = 20, adaptive_B=TRUE, adaptive_S=FALSE,
mixture_beta=TRUE)
``` |

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