assign.mcmc.RdThe 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.
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. |
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| 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 iter x K x J array of the pathway activation level estimated in every iteration of MCMC.
The iter x G matrix of the precision of genes estimated in every iteration of MCMC
The iter x K x J array of probability of pathway being activated estimated in every iteration of 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.)
The iter x G x K array of signature estimated in every iteration of MCMC.
The iter x G x K array of binary indicator of a gene being significant estimated in every iteration of MCMC.
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.
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)#>#>