TS_twosample {ChIPtest} | R Documentation |
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.
TS_twosample(data1, data4, tao, band, quant)
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 |
kernel-based test statistics is the same as "TS_kernel"
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 |
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
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))