XBSeq test for differential expression

Description

The same method is adopted from DESeq for testing differential expression

Usage

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XBSeqTest(XB, condA, condB, pvals_only = FALSE, method = c("NP", "MLE"), big_count = 900)

Arguments

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

Details

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.

Value

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

Author(s)

Yuanhang Liu

References

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.

See Also

XBSeq, estimateSCV

Examples

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   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)