OutlierD {OutlierD} | R Documentation |
Outlier dectection using quantile regression on the M-A scatterplots of high-throughput data
Description
This detects outliers using quantile regression on the M-A scatterplots of high-throughput data.
Usage
OutlierD(x1, x2, k=1.5, method="nonlin")
Arguments
x1 |
one n-by-1 vector for data (n= number of peptides, proteins, or genes |
x2 |
the other n-by-1 vector for data (n= number of peptides, proteins, or genes |
k |
parameter in Q1-k*IQR and Q3+k*IQR, IQR=Q3-Q1, k=1.5 (default) |
method |
one of constant, linear, nonlinear, and nonparametric quantile regression |
Value
x |
data and results for outliers |
Author(s)
HyungJun Cho
Examples
data(lcms)
x <- log2(lcms) #log2-tranformation, do normalization if necessary
fit1 <- OutlierD(x1=x[,1], x2=x[,2], method="constant")
fit2 <- OutlierD(x1=x[,1], x2=x[,2], method="linear")
fit3 <- OutlierD(x1=x[,1], x2=x[,2], method="nonlin")
fit4 <- OutlierD(x1=x[,1], x2=x[,2], method="nonpar")
fit3$x[1:10,]
plot(fit3$x$A, fit3$x$M, pch=".", xlab="A", ylab="M")
i <- sort.list(fit3$x$A)
lines(fit3$x$A[i], fit3$x$Q3[i], lty=2); lines(fit3$x$A[i], fit3$x$Q1[i], lty=2)
lines(fit3$x$A[i], fit3$x$LB[i]); lines(fit3$x$A[i], fit3$x$UB[i])
title("Nonlinear")
[Package
OutlierD version 1.6.0
Index]