# OutlierD: Outlier dectection using quantile regression on the M-A... In OutlierD: Outlier detection 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

 `1` ``` 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

HyungJun Cho

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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") ```

OutlierD documentation built on Nov. 1, 2018, 2:25 a.m.