pmartRseq_edgeR: edgeR analysis of omicsData objects
In pmartR/pmartRseq: Analysis and Visualization of 16S Data

Description Usage Arguments Details Value Author(s) References Examples

Differential abundance analysis of count data using edgeR

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

`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.

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.

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.

Allison Thompson

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.

## 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)