The same method is adopted from DESeq for testing differential expression

1 |

`XB` |
A XBSeqDataSet object |

`condA` |
Factor level specified for condition A |

`condB` |
Factor level specified for condition B |

`pvals_only` |
Logical;whether or not only extract p values |

`method` |
method to use for estimation of distribution parameters, 'NP' or 'MLE'. See details section for details |

`big_count` |
An integer specify a count number above which where be considerred as 'big' and beta approximation will be used instead for testing differential expression |

Differential expression analysis based on statistical methods proposed for DESeq. Details about the method can be found in DESeq manual page. Two methods can be choosen from for `method`

. 'NP' stands for non-parametric method. 'MLE' stands for maximum liklihood estimation method. 'NP' is generally recommended for experiments with replicates smaller than 5.

A data.frame with following columns:

`id` |
rownames of XBSeqDataSet |

`baseMean` |
The basemean for all genes |

`baseMeanA` |
The basemean for condition 'A' |

`baseMeanB` |
The basemean for condition 'B' |

`foldChange` |
The fold change compare condition 'B' to 'A' |

`log2FoldChange` |
The log2 fold change |

`pval` |
The p value for all genes |

`padj` |
The adjusted p value for all genes |

Yuanhang Liu

H. I. Chen, Y. Liu, Y. Zou, Z. Lai, D. Sarkar, Y. Huang, et al., "Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads," BMC Genomics, vol. 16 Suppl 7, p. S14, Jun 11 2015.

`XBSeq`

, `estimateSCV`

1 2 3 4 5 6 7 8 9 | ```
data(ExampleData)
conditions <- factor(c(rep('C1', 3), rep('C2', 3)))
XB <- XBSeqDataSet(Observed, Background, conditions)
XB <- estimateRealCount(XB)
XB <- estimateSizeFactors(XB)
XB <- estimateSCV(XB)
Teststas <- XBSeqTest(XB, levels(conditions)[1L], levels(conditions)[2L])
str(Teststas)
``` |

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