assign.output {ASSIGN} | R Documentation

## Prediction/validation output for test data

### Description

The assign.output function outputs the summary results and plots for prediction/validation for the test dataset.

### Usage

    assign.output(
      processed.data,
      mcmc.pos.mean.testData,
      trainingData,
      testData,
      trainingLabel,
      testLabel,
      geneList,
      adaptive_B = TRUE,
      adaptive_S = FALSE,
      mixture_beta = TRUE,
      outputDir
    )

### Arguments

`processed.data` | The list object returned from the assign.preprocess function.
`mcmc.pos.mean.testData` | The list object returned from the assign.mcmc function. Notice that for prediction/validation in the test dataset, the Y argument in the assign.mcmc function should be set as the test dataset.
`trainingData` | The genomic measure matrix of training samples (i.g., gene expression matrix). The dimension of this matrix is probe number x sample number.
`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.
`testLabel` | The vector of the phenotypes/labels of the test samples.
`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.
`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.

### Details

The assign.output function is suggested to run after the assign.preprocess, assign.mcmc and assign.summary functions. For the prediction/validation in the test dataset, The Y argument in the assign.mcmc function is the output value "testData_sub" from the assign.preprocess function.

### Value

The assign.output returns one .csv file containing one/multiple pathway activity for each individual test samples, scatter plots of pathway activity for each individual pathway in all the test samples, and heatmap plots for the gene expression of the prior signature and posterior signatures (if adaptive_S equals TRUE) of each individual pathway in the test samples.

### Author(s)

Ying Shen

### Examples

    assign.output(processed.data = processed.data,
                  mcmc.pos.mean.testData = mcmc.pos.mean,
                  trainingData = trainingData1, testData = testData1,
                  trainingLabel = trainingLabel1, testLabel = testLabel1,
                  geneList = NULL, adaptive_B = TRUE, adaptive_S = FALSE,
                  mixture_beta = TRUE, outputDir = tempdir)

[Package ASSIGN version 1.30.0 Index]