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]