TS_twosample {ChIPtest} R Documentation

## Three Nonparametric Test Statistics for two sample ChIP-seq data

### Description

It includes three nonparametric test statistics for two sample differential analysis: kernel based nonparametric test, assumption-free nonparametric test with equal variance estimation and unequal variance estimation.

### Usage

TS_twosample(data1, data4, tao, band, quant)


### Arguments

 data1 data matrix (after VST) for condition A data4 data matrix (after VST) for condition B tao the biologically relevant value c in the null hypothesis H0: TS=c, in assumption-free nonparametric test band bandwidth used in kernel smoothing quant threshold used in variance estimation

### Details

kernel-based test statistics is the same as "TS_kernel"

### Value

 TS_kn kernel based test statistics Deql assumption-free nonparametric test statistics with equal variance Dnun assumption-free nonparametric test statistics with unequal variance sigma1 variance estimation for conditon A under equal variance assumption sigma4 variance estimation for condition B under unequal variance assumption Ts_yvec Original statistics, which is calculated as integral of square of kernel estimator Dsum Original statistics, which is calculated for nonparametric test without smoothing Sev variance estimation under equal variance assumption Suv variance estimation under unequal variance assumption Xg estimation of standard deviation for kernel-based test statistics

### References

Qian Wu, Kyoung-Jae Won and Hongzhe Li. (2015) Nonparametric Methods for Identifying Differential Enrichment Regions with ChIP-seq Data. Cancer Informatics,14 (Suppl 1), 11-22

### Examples

data(data1)
data(data4)
Data1=NormTransformation(data1)
Data4=NormTransformation(data4)
tao=est.c(Data1, Data4, max1=5, max4=5)
band=54
TS=TS_twosample(Data1, Data4, tao, band, quant=c(0.9,0.9,0.9))


[Package ChIPtest version 1.0 Index]