tecVarEstim {BALLI} | R Documentation |

## Technical Variance Estimation

### Description

Estimate technical variance by using voom-trend. The code is derived from voom function in limma package

### Usage

```
tecVarEstim(counts, design = NULL, lib.size = NULL, span = 0.5, ...)
```

### Arguments

`counts` |
a DGEList object |

`design` |
design matrix with samples in row and coefficient(s) to be estimated in column |

`lib.size` |
numeric vector containing total library sizes for each sample |

`span` |
width of the lowess smoothing window as a proportion |

`...` |
other arguments are passed to lmFit. |

### Value

an TecVarList object with the following components:

`targets` |
matrix containing covariables, library sizes and normalization foctors of each sample |

`design` |
design matrix with samples in row and covariable(s) to be estimated in column |

`logcpm` |
logcpm values of each gene and each sample |

`tecVar` |
estimated techical variance of each gene and each sample |

### Examples

```
expr <- data.frame(t(sapply(1:1000,function(x)rnbinom(20,mu=500,size=50))))
group <- c(rep("A",10),rep("B",10))
design <- model.matrix(~group, data = expr)
dge <- DGEList(counts=expr, group=group)
dge <- calcNormFactors(dge)
tecVarEstim(dge,design)
```

[Package

*BALLI*version 0.2.0 Index]