balli {BALLI} | R Documentation |

DEG analysis using BALLI algorithm

balli(object, intV = 2, logcpm = NULL, tecVar = NULL, design = NULL, numCores = NULL, threshold = 1e-06, maxiter = 200)

`object` |
a TecVarList object |

`intV` |
numeric vector designating interest variable(s) which is(are) column number(s) of design matrix |

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

`tecVar` |
estimated technical variance values for each gene and each sample |

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

`numCores` |
number of cores to be used for multithreding. If NULL, a single core is used |

`threshold` |
threshold for convergence |

`maxiter` |
maximum number of iteration to converge of estimated biological variance. If not, biological variance is estimated by using Brent method |

an Balli object including Result and topGenes list. Following components are shown by Result (same order of genes with input data) and topGenes (ordered by pBALLI in Result) :

`log2FC` |
log2 fold changes of interest variable(s) |

`lLLI` |
log-likelihoods estimated by LLI |

`lBALLI` |
log-likelihoods estimated by BALLI |

`pLLI` |
p-values estimated by LLI |

`pBALLI` |
p-values estimated by BALLI |

`BCF` |
Bartlett's correction factor |

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) tV <- tecVarEstim(dge,design) balli(tV,intV=2)

[Package *BALLI* version 0.2.0 Index]