testDATs {EDDA} | R Documentation

## Run differential abundance testings

### Description

Perform differential abundance testing on simulated count data.

### Usage

    testDATs(data, numCores=10, minCountsThreshold=0,
    	DE.methods=c("Cuffdiff","DESeq","baySeq","edgeR","MetaStats","NOISeq"),
    	nor.methods=c("default","Mode","UQN","NDE"),method.list=NULL)

### Arguments

`data` | Data object from generateData() function or predifined data object similar to the output of generateData().
`numCores` | Number of cores for parallelization. Default is 10.
`minCountsThreshold` | Minimum counts threshold for filtering. Default is 0.
`DE.methods` | Method list for differential expression tests. Methods currently available include "Cuffdiff","DESeq","baySeq","edgeR","MetaStats","NOISeq".
`nor.methods` | Normalization method list. Methods currently available include "default" (default normalization for each DE method),"Mode"(Mode normalization), "UQN"(Upper quartile normalization), "NDE"(non-differential expression normalization).
`method.list` | The method list for the combination of DE.methods and nor.methods. Default is NULL.

### Value

`data` | Data object from generateData() function.
`filterCounts` | filtered count data.
`Cuffdiff` | Result form Cuffdiff with default normalization.
`Cuffdiff_uqn` | Result form Cuffdiff with Upper quartile normalization normalization.
`Cuffdiff_Mode` | Result form Cuffdiff with Mode normalization.
`Cuffdiff_nde` | Result form Cuffdiff with non-differential expression normalization.
`DESeq` | Result form DESeq with default normalization.
`DESeq_uqn` | Result form DESeq with Upper quartile normalization normalization.
`DESeq_Mode` | Result form DESeq with Mode normalization.
`DESeq_nde` | Result form DESeq with non-differential expression normalization.
`baySeq` | Result form baySeq with default normalization.
`baySeq_uqn` | Result form baySeq with Upper quartile normalization normalization.
`baySeq_Mode` | Result form baySeq with Mode normalization.
`baySeq_nde` | Result form baySeq with non-differential expression normalization.
`edgeR` | Result form edgeR with default normalization.
`edgeR_uqn` | Result form edgeR with Upper quartile normalization normalization.
`edgeR_Mode` | Result form edgeR with Mode normalization.
`edgeR_nde` | Result form edgeR with non-differential expression normalization.
`MetaStats` | Result form MetaStats with default normalization.
`MetaStats_uqn` | Result form MetaStats with Upper quartile normalization normalization.
`MetaStats_Mode` | Result form MetaStats with Mode normalization.
`MetaStats_nde` | Result form MetaStats with non-differential expression normalization.
`NOISeq` | Result form NOISeq with default normalization.
`NOISeq_uqn` | Result form NOISeq with Upper quartile normalization normalization.
`NOISeq_Mode` | Result form NOISeq with Mode normalization.
`NOISeq_nde` | Result form NOISeq with non-differential expression normalization.

### Author(s)

Li Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan

### References

Luo Huaien, Li Juntao,Chia Kuan Hui Burton, Shyam Prabhakar, Paul Robson, Niranjan Nagarajan, The importance of study design for detecting differentially abundant features in high-throughput experiments, under review.

### Examples

    data <- generateData(EntityCount=100)
    test.obj <- testDATs(data,nor.methods="default")
    test.obj <- testDATs(data,DE.methods="DESeq")

    # test data with input count.
    x <- matrix(rnbinom(1000*15,size=1,mu=10), nrow=1000, ncol=15);
    x[1:50,11:15] <- x[1:50,11:15]*10
    x.name=paste("g",1:1000,sep="");
    write.table(cbind(x.name,x),"count.txt",row.names =FALSE, sep ='\t')

    x <- read.table("count.txt",head=TRUE,sep='\t')
    x.count <- x[,2:16]
    x.lable=c(rep(0,10),rep(1,5))
    row.names(x.count) <- x[,1]
    data <- list(count=x.count,dataLabel=x.lable)
    test.obj <- testDATs(data,DE.methods=c("DESeq","edgeR"),nor.methods="default")

[Package EDDA version 1.18.0 Index]