pmartRseq_edgeR: edgeR analysis of omicsData objects

Description Usage Arguments Details Value Author(s) References Examples

Description

Differential abundance analysis of count data using edgeR

Usage

1
2
pmartRseq_edgeR(omicsData, norm_factors = NULL, test = "qcml", pairs, adj,
  thresh)

Arguments

omicsData

an object of the class 'seqData' created by as.seqData.

norm_factors

Named vector of normalization parameters to put into DESeq2. If NULL, will use DESeq2's inbuilt normalization. Default is NULL.

test

name of which differential expression test to use, options are "qcml", "lrt", "qlftest", or "paired". Default is "qcml". See details for further explanation.

pairs

a matrix dictating which pairwise comparisons to make

adj

multiple comparison adjustment method to use.

thresh

p-value threshold for significance.

Details

Perform pairwise differential abundance analysis of count data using edgeR. For test parameter: "qcml" refers to quantile-adjusted conditional maximum likelihood method, which is the most reliable in terms of bias on a wide range of conditions and specifically performs best in the situation of many small samples with a common dispersion. "lrt" refers to generalized linear model likelihood ratio test, best used for cases where there a multiple treatment groups and provides inferences with GLMs. "qlftest" refers to the QL F-test, preferred as it reflects the uncertainty in estimating the dispersion for each gene and provides a more robust and reliable error rate control when the number of replicates is small. "paired" refers to a paired test and performs the generalized liner model likelihood ratio test for paired data (this should also be used for experiments with a block or batch effect). In the "paired" test, 'group1' should be the main effect of interest and 'group2' should be the covariate/block/batch. Currently, this can be used only if there are 2 main effects of interest.

Value

A matrix containing the log2 fold change (logFC), log-average concentration/abundance (logCPM), likelihood ratio (LR), exact p-value for differential expression using the negative binomial model (PValue), and the p-value adjusted for multiple testing (FDR) for every pairwise comparison and every feature.

Author(s)

Allison Thompson

References

Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, pp. -1. McCarthy, J. D, Chen, Yunshun, Smyth and K. G (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research, 40(10), pp. -9. Robinson MD and Smyth GK (2007). Moderated statistical tests for assessing differences in tag abundance. Bioinformatics, 23, pp. -6. Robinson MD and Smyth GK (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9, pp. -11. Zhou X, Lindsay H and Robinson MD (2014). Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Research, 42, pp. e91.

Examples

1
2
3
4
5
6
7
8
9
## Not run: 
library(mintJansson)
data(cDNA_hiseq_data)
mycdnadata <- group_designation(omicsData = cDNA_hiseq_data, main_effects = c("treatment"), time_course=NULL)
mycdnadata_norm <- normalize_data(omicsData = mycdnadata, norm_fn = "percentile")
mycdnadata_edgeR <- pmartRseq_edgeR(omicsData = mycdnadata_norm, test="qcml", pairs = cbind(list("Neg","Plus")), adj = "BH", thresh = 0.05)
mycdnadata_edgeR <- mint_edgeR(omicsData = mycdnadata_norm, test="qcml", pairs = cbind(list("Neg","Plus")), adj = "BH", thresh = 0.05)

## End(Not run)

pmartR/pmartRseq documentation built on May 25, 2019, 9:20 a.m.