odm: Outlier dectection for (multiple) replicated high-throughput...

In sooheang/OutlierDM: Outlier Detection for Multiplicative High-throughput Data

Description

This function provides some routines for detecting outlying observations (peptides) for multiple replicated high-throughput data, especially in mass-spectrometry by using quantile regression-based boxplot algorithms.

Usage

  odm(x, k=1.5, method= c("linear", "nonlin", "constant", "nonpar"), 
  type = c("proj", "diff", "pair", "grubbs", "dixon", "iqr",  "siqr", "Zscore"), ...)

Arguments

x	data vectors or matrices. These can be given as named arguments. If the number of predictors is 2, x1 describes one n-by-1 vector for data and x2 describes the other n-by-1 vector for data (n= number of peptides, proteins, or genes)
k	non-negative tuning parameter for the outlier detection algorithm. For the sake of IQR-based algorithms such as 'iqr', 'proj', 'diff', and 'pair', it works in the formula of Q1-k*IQR and Q3+k*IQR, IQR=Q3-Q1. For 'Zscore', it works for the 'k' in |Z| > k. A default value is 1.5
method	type of quantile regression methods used in an outlier detection algorithm. Use one of 'constant', 'linear', 'nonlin', and 'nonpar' which mean constant, linear, non-linear, and non-parametric quantile regression in order. For more details, see the quantreg package.
type	type of criterion for detecting outlying observations. You can select one of 'Zscore', 'iqr', 'dixon', 'grubbs', 'pair', 'diff', and 'proj' algorithms. Zscore: Z-score based criterion iqr: Interquartile range (IQR) criterion siqr: Semi-interquartile range (IQR) criterion dixon: Dixon's Q-test (Dixon, 1950; 1951) grubbs: Grubbs test (Grubbs, 1950; 1969) pair: Pariwise OutlierD algorithm (Cho et al., 2008; Eo et al., 2012) diff: Difference approach for multiplicative high-throughput data (Eo et al., 2012) proj: Boxplot-based MA plot with projections (Eo et al., 2012)
...	minor tuning parameters used in odm.control(). See odm.control.

Value

call:	evaluated function call
raw.data:	data to be used in the fitted model
res:	a data.frame including the information about the fitted model. It consists of several columns including outlier, M, A, Q3, Q1, UB and LB.
x.pair:	Object of class "list"
k:	a scalar parameter for constructing boxplot used in the fitted models
n.outliers:	a scalar value that denotes the number of outliers to be detected by the fitted model.
method:	the type of method used in the fitted model
type:	the type of algorithm used in the fitted model
contrl.para:	a list including information about tuning parameters

References

Cho H, Lee JW, Kim Y-J, et al. (2008) OutlierD: an R package for outlier detection using quantile regression on mass spectrometry data. Bioinformatics 24:882<e2><80><93>884.

Dixon WJ (1950) Analysis of extreme values. Ann Math Statistics 21:488<e2><80><93>506.

Dixon WJ (1951) Ratios involving extreme values. Ann Math Statistics 22:68<e2><80><93>78.

Grubbs FE (1950) Sample criteria for testing outlying observations. Ann Math Statistics 21:27<e2><80><93>58.

Grubbs FE (1969) Procedures for detecting outlying observations in samples. Technometrics 11:1<e2><80><93>21.

1. Eo S-H, Pak D, Choi J, Cho H (2012) Outlier detection using projection quantile regression for mass spectrometry data with low replication. BMC Res Notes. doi: 10.1186/1756-0500-5-236

See Also

OutlierDM-package to provide the general information about the OutlierDC package
OutlierDM-class to provide the information about the "OutlierDM" class
odm.control to control tuning parameters

Examples

  ## Not run: 

    ## Load datasets
    data(lcms3)
    
    ## Load 
    ## Fit projection approaches
    fit.proj.const <- odm(lcms3, method="constant")
    fit.proj.linear <- odm(lcms3, method="linear")
    fit.proj.nonlin <- odm(lcms3, method="nonlin")
    fit.proj.nonpara <- odm(lcms3, method="nonpar", lbda = 1)
    
    par(mfrow = c(2,2))
    plot(fit.proj.const, main = "Constant")
    plot(fit.proj.linear, main = "Linear")
    plot(fit.proj.nonlin, main = "NonLinear")
    plot(fit.proj.nonpara, main = "Nonparametric")

    ## Fit pairwise OutlierD algorithm
    fit0 <- odm(lcms3, type = "pair")
    plot(fit0)

  
## End(Not run)

sooheang/OutlierDM documentation built on May 30, 2019, 6:31 a.m.