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]