assign.wrapper: ASSIGN All-in-one function
In ASSIGN: Adaptive Signature Selection and InteGratioN (ASSIGN)

Description Usage Arguments Details Value Author(s) Examples

The assign.wrapper function integrates the assign.preprocess, assign.mcmc, assign.summary, assign.output, assign.cv.output functions into one wrapper function.

assign.wrapper(
  trainingData = NULL,
  testData,
  trainingLabel,
  testLabel = NULL,
  geneList = NULL,
  anchorGenes = NULL,
  excludeGenes = NULL,
  n_sigGene = NA,
  adaptive_B = TRUE,
  adaptive_S = FALSE,
  mixture_beta = TRUE,
  outputDir,
  p_beta = 0.01,
  theta0 = 0.05,
  theta1 = 0.9,
  iter = 2000,
  burn_in = 1000,
  sigma_sZero = 0.01,
  sigma_sNonZero = 1,
  S_zeroPrior = FALSE,
  pctUp = 0.5,
  geneselect_iter = 500,
  geneselect_burn_in = 100,
  outputSignature_convergence = FALSE,
  ECM = FALSE,
  progress_bar = TRUE,
  override_S_matrix = NULL
)

`trainingData`	The genomic measure matrix of training samples (i.g., gene expression matrix). The dimension of this matrix is probe number x sample number. The default is NULL.
`testData`	The genomic measure matrix of test samples (i.g., gene expression matrix). The dimension of this matrix is probe number x sample number.
`trainingLabel`	The list linking the index of each training sample to a specific group it belongs to. See examples for more information.
`testLabel`	The vector of the phenotypes/labels of the test samples. The default is NULL.
`geneList`	The list that collects the signature genes of one/multiple pathways. Every component of this list contains the signature genes associated with one pathway. The default is NULL.
`anchorGenes`	A list of genes that will be included in the signature even if they are not chosen during gene selection.
`excludeGenes`	A list of genes that will be excluded from the signature even if they are chosen during gene selection.
`n_sigGene`	The vector of the signature genes to be identified for one pathway. n_sigGene needs to be specified when geneList is set NULL. The default is NA. See examples for more information.
`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.
`outputDir`	The path to the directory to save the output files. The path needs to be quoted in double quotation marks.
`p_beta`	p_beta is the prior probability of a pathway being activated in individual test samples. The default is 0.01.
`theta0`	The prior probability for a gene to be significant, given that the gene is NOT defined as "significant" in the signature gene lists provided by the user. The default is 0.05.
`theta1`	The prior probability for a gene to be significant, given that the gene is defined as "significant" in the signature gene lists provided by the user. The default is 0.9.
`iter`	The number of iterations in the MCMC. The default is 2000.
`burn_in`	The number of burn-in iterations. These iterations are discarded when computing the posterior means of the model parameters. The default is 1000.
`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.
`S_zeroPrior`	Logicals. If TRUE, the prior distribution of signature follows a normal distribution with mean zero. The default is TRUE.
`pctUp`	By default, ASSIGN bayesian gene selection chooses the signature genes with an equal fraction of genes that increase with pathway activity and genes that decrease with pathway activity. Use the pctUp parameter to modify this fraction. Set pctUP to NULL to select the most significant genes, regardless of direction. The default is 0.5
`geneselect_iter`	The number of iterations for bayesian gene selection. The default is 500.
`geneselect_burn_in`	The number of burn-in iterations for bayesian gene selection. The default is 100
`outputSignature_convergence`	Create a pdf of the MCMC chain. The default is FALSE.
`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 and gene selection. Default is TRUE.
`override_S_matrix`	Replace the S_matrix created by assign.preprocess with the matrix provided in override_S_matrix. This can be used to indicate the expected directions of genes in a signature if training data is not provided.

The assign.wrapper function is an all-in-one function which outputs the necessary results for basic users. For users who need more intermediate results for model diagnosis, it is better to run the assign.preprocess, assign.mcmc, assign.convergence, assign.summary functions separately and extract the output values from the returned list objects of those functions.

The assign.wrapper returns one/multiple pathway activity for each individual training sample and test sample, scatter plots of pathway activity for each individual pathway in the training and test data, heatmap plots for gene expression signatures for each individual pathway, heatmap plots for the gene expression of the prior and posterior signatures (if adaptive_S equals TRUE) of each individual pathway in the test data

Ying Shen and W. Evan Johnson

data(trainingData1)
data(testData1)
data(geneList1)

trainingLabel1 <- list(control = list(bcat=1:10, e2f3=1:10, myc=1:10,
                                      ras=1:10, src=1:10),
                       bcat = 11:19, e2f3 = 20:28, myc= 29:38, ras = 39:48,
                       src = 49:55)
testLabel1 <- rep(c("subtypeA","subtypeB"), c(53,58))

assign.wrapper(trainingData=trainingData1, testData=testData1,
               trainingLabel=trainingLabel1, testLabel=testLabel1,
               geneList=geneList1, adaptive_B=TRUE, adaptive_S=FALSE,
               mixture_beta=TRUE, outputDir=tempdir, p_beta=0.01,
               theta0=0.05, theta1=0.9, iter=20, burn_in=10)