pvcam {bapred} R Documentation

## Proportion of variation induced by class signal estimated by Principal Variance Component Analysis

### Description

Principal Variance Component Analysis (PVCA) (Li et al, 2009) allows the estimation of the contribution of several sources of variability. `pvcam` uses it to estimate the proportion of variance in the data explained by the class signal. See below for a more detailed explanation of what the function does.

### Usage

```pvcam(xba, batch, y, threshold = 0.6)
```

### Arguments

 `xba` matrix. The covariate matrix, raw or after batch effect adjustment. observations in rows, variables in columns. `batch` factor. Batch variable. Currently has to have levels: '1', '2', '3' and so on. `y` factor. Binary target variable. Currently has to have levels '1' and '2'. `threshold` numeric. Minimal proportion of variance explained by the principal components used.

### Details

In PVCA, first principal component analysis is performed on the n x n covariance matrix between the observations. Then, using a random effects model the principal components are regressed on arbitrary factors of variability, such as "batch" and "(phenotype) class". Ultimately, estimated proportions of variance induced by each factor and that of the residual variance are obtained. In `pvcam` the factors included into the model are: "batch", "class" and the interaction of these two into. The metric calculated by `pvcam` is the proportion of variance explained by "class".

`pvcam` uses a slightly altered version of the function `pvcaBatchAssess()` from the Bioconductor package `pvca`. The latter was altered to take the covariate data as a `matrix` instead of as an object of class `ExpressionSet`.

### Value

Value of the metric

### Note

Higher values of this metric indicate a better preservation or exposure, respectively, of the biological signal of interest.

Roman Hornung

### References

Li, J., Bushel, P., Chu, T.-M., Wolfinger, R.D. (2009) Principal variance components analysis: Estimating batch effects in microarray gene expression data. In: Scherer, A. (ed) Batch Effects and Noise in Microarray Experiments: Sources and Solutions, John Wiley & Sons, Chichester, UK.

### Examples

```data(autism)