odm: Outlier Dectection for Multi-replicated data
In OutlierDM: Outlier Detection for Multi-replicated High-throughput Data
Description
Usage
Arguments
Value
References
See Also
Examples
Description
This function provides some routines for detecting outlying observations (peptides) for multi-replicated high-throughput data, especially in LC/MS experiments.
Usage
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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 IQR-based algorithms such as 'iqr', 'siqr', 'proj', 'diff', and 'pair', it works in the formula of Q1-k*IQR and Q3+k*IQR, where IQR=Q3-Q1. For 'Zscore', it works for the 'k' in |Z| > k. A default value is 3.
quantreg
type of quantile regression models used for the outlier detection method. You can use one of the 'constant', 'linear', 'nonlin', and 'nonpar' which mean the constant, linear, non-linear, and non-parametric quantile regression in order. For more details, see the quantreg package.
method
type of outlier detection methods. You can select one of the 'Zscore', 'iqr', 'dixon', 'grubbs', 'pair', 'diff', and 'proj' algorithms as follows.
Zscore: Z-score based criterion (Cho and Eo, 2015)
iqr: Interquartile range (IQR) criterion (Cho and Eo, 2015)
siqr: Semi-interquartile range (IQR) criterion (Cho and Eo, 2015)
dixon: Dixon's test (Dixon, 1950; 1951)
grubbs: Grubbs test (Grubbs, 1950; 1969)
pair: Pariwise OutlierD algorithm (Cho et al., 2008; Eo et al., 2012)
proj: Projection-based OutlierD algortihm (Eo et al., 2012)
diff: Difference-based OutlierD algorithm (Eo and Cho, 2015)
...
minor tuning parameters used in odm.control(). See odm.control.
Value
call:
evaluated function call
raw.data:
raw dataset used in the model fitting
res:
result matrix of the model fitting. It consists of used data set with some transformation and outlying statistic.
x.pair:
Object of class "list"
k:
threshold parameter for constructing outlier detection methods
outlier:
matrix including the status of each outlying peptide and sample
n.outliers:
the number of outlying parameters (peptides) to be detected by the model fitting.
quantreg:
type of quantile regression used for the model fitting
method:
type of outlier detection method used for the model fitting
contrl.para:
a list of minor parameters
References
Eo, S-H and Cho, H (2015) OutlierDM: More robust outlier detection algorithms for multi-replicated high-throughput data.
Cho, H and Eo, S-H. (2015) Outlier detection for mass-spectrometry data.
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.
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 24:882–884.
Grubbs FE (1969) Procedures for detecting outlying observations in samples. Technometrics 11:1–21.
Dixon WJ (1951) Ratios involving extreme values. Ann Math Statistics 22:68–78.
Dixon WJ (1950) Analysis of extreme values. Ann Math Statistics 21:488–506.
Grubbs FE (1950) Sample criteria for testing outlying observations. Ann Math Statistics 21:27–58.
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
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## Not run:
##############################################################
#
# Outlier Detection for Mass Spectrometry Data
# Section 3. Illustration
# by HyungJun Cho and Soo-Heang Eo,
# Dept of Statistics, Korea University, Seoul, Korea
#
##############################################################
#####
# Load a package OutlierDM
# If an OutlierDM package is not installed on your system, type
#install.package('OutlierDM', dependency = TRUE)
library(OutlierDM)
#####
# Sec 3.1 When the number of replicates is large enough
## Load toy dataset
data(toy)
head(toy)
pairs(log2(toy), pch = 20, cex = .7)
#####
# Fit 1. Z-score based criterion
fit1 = odm(x = toy, method = "Zscore", k = 3)
fit1
summary(fit1)
head(input(fit1))
head(output(fit1))
print(outliers(fit1), digits = 3)
plot(fit1)
rect(1, -4, 10, 4, col = heat.colors(20,alpha = 0.3), border = heat.colors(20,alpha = 0.5))
oneplot(object = fit1, i = 4)
title("Outlier Detection by the Z-score criterion")
# Add a peptide name on a dot-plot
#oneplot(fit1, 191,1)
#title("Outlier Detection by the Z-score criterion")
#####
# Fit 2. Grubbs test criteria
fit2 = odm(x = toy, method ="grubbs", alpha = 0.01)
fit2
summary(fit2)
head(output(fit2))
print(outliers(fit2), digits = 3)
oneplot(object = fit2, i = 1)
title("Outlier Detection by the Grubbs criterion")
# Add text
#oneplot(fit2, 191,1)
#title("Outlier Detection by the Grubbs criterion")
#####
# Fit 3. IQR criteria
fit3 = odm(x = toy, method = "iqr", k = 3)
fit3
summary(fit3)
print(outliers(fit3), digits = 3)
plot(fit3)
rect(1, -4, 10, 40, col = heat.colors(20,alpha = 0.3), border = heat.colors(20,alpha = 0.5))
oneplot(fit3, 1)
title("Outlier Detection by the IQR criterion")
# Add a peptide name on a dot-plot
#oneplot(fit3, 1, 1)
#title("Outlier Detection by the IQR criterion")
#####
# Fit 4. SIQR criteria
fit4 = odm(x = toy, method = "siqr", k = 3)
fit4
summary(fit4)
print(outliers(fit4), digits = 3)
plot(fit4)
rect(1, -4, 10, 4, col = heat.colors(20,alpha = 0.3), border = heat.colors(20,alpha = 0.5))
oneplot(fit4, 1)
title("Outlier Detection by the SIQR criterion")
#####################
## Real data example
#####################
data(lcms3)
head(lcms3)
pairs(log2(lcms3), pch = 20, cex = .7)
#####
# Fit 5. OutlierD
fit5 = odm(lcms3[,1:2], method = "pair", k = 3)
fit5
summary(fit5)
head(output(fit5))
print(outliers(fit5), digits = 3)
plot(fit5)
title("Outlier Detection by the OutlierD algorithm")
#####
# Fit 6. OutlierDM
fit6 = odm(lcms3, method = "proj", k = 3, center = TRUE)
fit6
summary(fit6)
print(outliers(fit6), digits = 3)
plot(fit6)
title("Outlier Detection by the OutlierDM algorithm")
oneplot(fit6, 18)
#oneplot(fit6, 18, 1)
title("The dotplot for the 18th samples of the lcms3 data")
### End of the illustration
#####
# Other OutlierDM algorithms
data(lcms3)
## Load
## Fit projection approaches
fit.proj.const <- odm(lcms3, quantreg="constant")
fit.proj.linear <- odm(lcms3, quantreg="linear")
fit.proj.nonlin <- odm(lcms3, quantreg="nonlin")
fit.proj.nonpara <- odm(lcms3, quantreg="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")
## End(Not run)
Example output
Package OutlierDM (1.1.1) loaded.
Y1 Y2 Y3 Y4 Y5 Y6
[1,] 26783800 25999425 12261435 15899444 2917266265 41721220
[2,] 23590082416 26217449538 24966545767 25356948796 563871754 27198324106
[3,] 104073761 145261911 160333545 190778246 122346655 157835864
[4,] 1264867022 1136510555 949419238 1908955569 995395157 1944121226
[5,] 854562021 836104879 1494102552 757282589 551074573 31969323311
[6,] 463327911 573439414 463991305 563645631 630575491 596544140
Y7 Y8 Y9 Y10 Y11 Y12
[1,] 28995062 19159370 23991286 14909836 22517745 28098385
[2,] 25123907000 30762477540 21573786718 32314611785 27542098540 18686856711
[3,] 141236692 161154539 110055848 119390039 183411946 88799743
[4,] 1374127749 1609172868 1240469934 1551775778 959126814 1441518203
[5,] 711510830 889235783 1114078381 807480809 730038584 1103440211
[6,] 685898577 488392855 898960080 647209727 625225322 492196078
Y13 Y14 Y15
[1,] 18769087 31522265 13833118
[2,] 24379205665 23654728747 28706961292
[3,] 129488807 303499529 1564839
[4,] 1406965363 29832199806 1648536466
[5,] 1057219333 977140013 702802584
[6,] 651534620 578519247 4264738
Please wait...
Call:
odm(x = toy, k = 3, method = "Zscore")
Outlier Detection for Multi-replicative High-throughput Data
Method: Z-score criterion ( Zscore )
k: 3 for |Z| > k
Number of Observations: 200
Number of Outliers: 10
Transformation: log2
Head of the Output Results
Outlier Z_Y1 Z_Y2 Z_Y3 Z_Y4 Z_Y5 Z_Y6 Z_Y7 Z_Y8 Z_Y9
1 TRUE -0.0954 -0.118 -0.6928 -0.494 3.491 0.244 -0.0347 -0.3515 -0.17956
2 TRUE 0.1778 0.284 0.2349 0.251 -3.581 0.321 0.2412 0.4451 0.08786
3 TRUE -0.0213 0.255 0.3374 0.482 0.113 0.324 0.2322 0.3417 0.02509
4 TRUE -0.3293 -0.458 -0.6748 0.166 -0.618 0.188 -0.2296 -0.0394 -0.35280
5 TRUE -0.2716 -0.294 0.3100 -0.397 -0.728 3.499 -0.4624 -0.2302 0.00445
6 TRUE 0.0702 0.236 0.0713 0.223 0.310 0.267 0.3758 0.1112 0.58657
Z_Y10 Z_Y11 Z_Y12 Z_Y13 Z_Y14 Z_Y15 SD LB UB
1 -0.5433 -0.228 -0.05872 -0.3673 0.0292 -0.6006 1.89 -3 3
2 0.4946 0.334 -0.05677 0.2109 0.1806 0.3755 1.43 -3 3
3 0.0927 0.449 -0.15305 0.1601 0.8671 -3.5054 1.74 -3 3
4 -0.0832 -0.663 -0.17193 -0.2011 3.4766 -0.0103 1.20 -3 3
5 -0.3306 -0.436 -0.00554 -0.0501 -0.1321 -0.4752 1.39 -3 3
6 0.3306 0.304 0.11728 0.3358 0.2432 -3.5822 1.85 -3 3
To see the full information for the result, use a command, 'output(your_object_name)'.
Call:
odm(x = toy, k = 3, method = "Zscore")
Outlier Detection for Multi-replicative High-throughput Data
Method: Z-score criterion ( Zscore )
k: 3 for |Z| > k
Number of Observations: 200
Number of Outliers: 10
Transformation: log2
Head of the Input Data
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
1 2.68e+07 2.60e+07 1.23e+07 1.59e+07 2.92e+09 4.17e+07 2.90e+07 1.92e+07
2 2.36e+10 2.62e+10 2.50e+10 2.54e+10 5.64e+08 2.72e+10 2.51e+10 3.08e+10
3 1.04e+08 1.45e+08 1.60e+08 1.91e+08 1.22e+08 1.58e+08 1.41e+08 1.61e+08
4 1.26e+09 1.14e+09 9.49e+08 1.91e+09 9.95e+08 1.94e+09 1.37e+09 1.61e+09
5 8.55e+08 8.36e+08 1.49e+09 7.57e+08 5.51e+08 3.20e+10 7.12e+08 8.89e+08
6 4.63e+08 5.73e+08 4.64e+08 5.64e+08 6.31e+08 5.97e+08 6.86e+08 4.88e+08
Y9 Y10 Y11 Y12 Y13 Y14 Y15
1 2.40e+07 1.49e+07 2.25e+07 2.81e+07 1.88e+07 3.15e+07 1.38e+07
2 2.16e+10 3.23e+10 2.75e+10 1.87e+10 2.44e+10 2.37e+10 2.87e+10
3 1.10e+08 1.19e+08 1.83e+08 8.88e+07 1.29e+08 3.03e+08 1.56e+06
4 1.24e+09 1.55e+09 9.59e+08 1.44e+09 1.41e+09 2.98e+10 1.65e+09
5 1.11e+09 8.07e+08 7.30e+08 1.10e+09 1.06e+09 9.77e+08 7.03e+08
6 8.99e+08 6.47e+08 6.25e+08 4.92e+08 6.52e+08 5.79e+08 4.26e+06
To see the full information of the input dataset, use a command, 'input(your_object_name)'.
Head of the Output Results
Outlier Z_Y1 Z_Y2 Z_Y3 Z_Y4 Z_Y5 Z_Y6 Z_Y7 Z_Y8 Z_Y9
1 TRUE -0.0954 -0.118 -0.6928 -0.494 3.491 0.244 -0.0347 -0.3515 -0.17956
2 TRUE 0.1778 0.284 0.2349 0.251 -3.581 0.321 0.2412 0.4451 0.08786
3 TRUE -0.0213 0.255 0.3374 0.482 0.113 0.324 0.2322 0.3417 0.02509
4 TRUE -0.3293 -0.458 -0.6748 0.166 -0.618 0.188 -0.2296 -0.0394 -0.35280
5 TRUE -0.2716 -0.294 0.3100 -0.397 -0.728 3.499 -0.4624 -0.2302 0.00445
6 TRUE 0.0702 0.236 0.0713 0.223 0.310 0.267 0.3758 0.1112 0.58657
Z_Y10 Z_Y11 Z_Y12 Z_Y13 Z_Y14 Z_Y15 SD LB UB
1 -0.5433 -0.228 -0.05872 -0.3673 0.0292 -0.6006 1.89 -3 3
2 0.4946 0.334 -0.05677 0.2109 0.1806 0.3755 1.43 -3 3
3 0.0927 0.449 -0.15305 0.1601 0.8671 -3.5054 1.74 -3 3
4 -0.0832 -0.663 -0.17193 -0.2011 3.4766 -0.0103 1.20 -3 3
5 -0.3306 -0.436 -0.00554 -0.0501 -0.1321 -0.4752 1.39 -3 3
6 0.3306 0.304 0.11728 0.3358 0.2432 -3.5822 1.85 -3 3
To see the full information for the result, use a command, 'output(your_object_name)'.
Head of the Peptide Numbers detected to be an Outlier
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
To see the full information for the candidate outliers, use a command, 'outliers(your_object_name)'.
Y1 Y2 Y3 Y4 Y5 Y6
1 26783800 25999425 12261435 15899444 2917266265 41721220
2 23590082416 26217449538 24966545767 25356948796 563871754 27198324106
3 104073761 145261911 160333545 190778246 122346655 157835864
4 1264867022 1136510555 949419238 1908955569 995395157 1944121226
5 854562021 836104879 1494102552 757282589 551074573 31969323311
6 463327911 573439414 463991305 563645631 630575491 596544140
Y7 Y8 Y9 Y10 Y11 Y12
1 28995062 19159370 23991286 14909836 22517745 28098385
2 25123907000 30762477540 21573786718 32314611785 27542098540 18686856711
3 141236692 161154539 110055848 119390039 183411946 88799743
4 1374127749 1609172868 1240469934 1551775778 959126814 1441518203
5 711510830 889235783 1114078381 807480809 730038584 1103440211
6 685898577 488392855 898960080 647209727 625225322 492196078
Y13 Y14 Y15
1 18769087 31522265 13833118
2 24379205665 23654728747 28706961292
3 129488807 303499529 1564839
4 1406965363 29832199806 1648536466
5 1057219333 977140013 702802584
6 651534620 578519247 4264738
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7
1 TRUE 24.67486 24.63198 23.54762 23.92247 31.44197 25.31428 24.78930
2 TRUE 34.45746 34.60981 34.53928 34.56166 29.07079 34.66280 34.54834
3 TRUE 26.63303 27.11408 27.25650 27.50732 26.86640 27.23385 27.07354
4 TRUE 30.23634 30.08196 29.82247 30.83014 29.89069 30.85647 30.35587
5 TRUE 29.67061 29.63911 30.47663 29.49626 29.03767 34.89597 29.40631
6 TRUE 28.78746 29.09507 28.78952 29.07021 29.23209 29.15205 29.35342
Y8 Y9 Y10 Y11 Y12 Y13 Y14 Y15
1 24.19155 24.51601 23.82976 24.42456 24.74398 24.16186 24.90987 23.72162
2 34.84045 34.32856 34.91147 34.68092 34.12130 34.50493 34.46141 34.74068
3 27.26387 26.71366 26.83111 27.45051 26.40405 26.94825 28.17712 20.57758
4 30.58367 30.20824 30.53127 29.83715 30.42494 30.38994 34.79615 30.61854
5 29.72799 30.05320 29.58885 29.44340 30.03936 29.97763 29.86399 29.38854
6 28.86347 29.74368 29.26966 29.21980 28.87466 29.27927 29.10779 22.02403
Z_Y1 Z_Y2 Z_Y3 Z_Y4 Z_Y5 Z_Y6 Z_Y7
1 -0.09536193 -0.1180897 -0.69281657 -0.4941398 3.4913351 0.2435431 -0.0347031
2 0.17781271 0.2841269 0.23490724 0.2505284 -3.5812350 0.3211058 0.2412329
3 -0.02130445 0.2554765 0.33742035 0.4817344 0.1129682 0.3243875 0.2321502
4 -0.32934778 -0.4581996 -0.67479138 0.1662774 -0.6178468 0.1882581 -0.2295793
5 -0.27163296 -0.2943647 0.31000233 -0.3974485 -0.7283684 3.4990495 -0.4623547
6 0.07019154 0.2363051 0.07130623 0.2228842 0.3103027 0.2670795 0.3758210
Z_Y8 Z_Y9 Z_Y10 Z_Y11 Z_Y12 Z_Y13
1 -0.35152563 -0.179555502 -0.54327887 -0.2280248 -0.058723561 -0.36726276
2 0.44508032 0.087860070 0.49463759 0.3337512 -0.056771661 0.21093976
3 0.34165996 0.025087160 0.09266224 0.4490483 -0.153051687 0.16006371
4 -0.03943888 -0.352801128 -0.08317492 -0.6625415 -0.171926255 -0.20114157
5 -0.23022628 0.004451239 -0.33062996 -0.4355922 -0.005537515 -0.05008534
6 0.11123749 0.586569339 0.33058802 0.3036643 0.117280868 0.33577680
Z_Y14 Z_Y15 SD LB UB
1 0.02919807 -0.60059406 1.886725 -3 3
2 0.18056790 0.37545581 1.432988 -3 3
3 0.86711471 -3.50541721 1.738017 -3 3
4 3.47659061 -0.01033688 1.198078 -3 3
5 -0.13208754 -0.47517499 1.385786 -3 3
6 0.24317622 -3.58218329 1.851790 -3 3
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12
1 TRUE 24.67 24.6 23.5 23.9 31.44 25.3 24.79 24.2 24.5 23.83 24.4 24.74
2 TRUE 34.46 34.6 34.5 34.6 29.07 34.7 34.55 34.8 34.3 34.91 34.7 34.12
3 TRUE 26.63 27.1 27.3 27.5 26.87 27.2 27.07 27.3 26.7 26.83 27.5 26.40
4 TRUE 30.24 30.1 29.8 30.8 29.89 30.9 30.36 30.6 30.2 30.53 29.8 30.42
5 TRUE 29.67 29.6 30.5 29.5 29.04 34.9 29.41 29.7 30.1 29.59 29.4 30.04
6 TRUE 28.79 29.1 28.8 29.1 29.23 29.2 29.35 28.9 29.7 29.27 29.2 28.87
7 TRUE 30.95 31.0 25.6 31.3 31.46 30.9 31.60 31.6 31.6 31.97 31.7 31.27
8 TRUE 9.25 11.1 10.6 30.5 6.32 13.2 6.16 5.3 15.7 7.14 12.8 1.27
9 TRUE 34.78 35.3 29.8 35.3 35.13 34.8 35.08 34.9 35.0 34.86 35.0 34.45
10 TRUE 23.85 23.3 24.2 24.7 24.27 24.4 24.54 22.9 23.7 24.56 14.6 22.84
Y13 Y14 Y15 Z_Y1 Z_Y2 Z_Y3 Z_Y4 Z_Y5 Z_Y6 Z_Y7
1 24.16 24.9 23.72 -0.0954 -0.1181 -0.6928 -0.494 3.491 0.2435 -0.0347
2 34.50 34.5 34.74 0.1778 0.2841 0.2349 0.251 -3.581 0.3211 0.2412
3 26.95 28.2 20.58 -0.0213 0.2555 0.3374 0.482 0.113 0.3244 0.2322
4 30.39 34.8 30.62 -0.3293 -0.4582 -0.6748 0.166 -0.618 0.1883 -0.2296
5 29.98 29.9 29.39 -0.2716 -0.2944 0.3100 -0.397 -0.728 3.4990 -0.4624
6 29.28 29.1 22.02 0.0702 0.2363 0.0713 0.223 0.310 0.2671 0.3758
7 31.25 31.7 31.58 -0.0557 -0.0454 -3.5420 0.191 0.277 -0.0617 0.3666
8 8.64 8.5 9.47 -0.1724 0.1042 0.0302 3.048 -0.617 0.4194 -0.6416
9 34.97 34.8 34.76 0.1331 0.5088 -3.5666 0.544 0.395 0.1560 0.3552
10 24.75 24.9 23.60 0.1766 -0.0354 0.2982 0.519 0.343 0.3792 0.4463
Z_Y8 Z_Y9 Z_Y10 Z_Y11 Z_Y12 Z_Y13 Z_Y14 Z_Y15 SD LB UB
1 -0.3515 -0.17956 -0.5433 -0.228 -0.05872 -0.3673 0.0292 -0.6006 1.89 -3 3
2 0.4451 0.08786 0.4946 0.334 -0.05677 0.2109 0.1806 0.3755 1.43 -3 3
3 0.3417 0.02509 0.0927 0.449 -0.15305 0.1601 0.8671 -3.5054 1.74 -3 3
4 -0.0394 -0.35280 -0.0832 -0.663 -0.17193 -0.2011 3.4766 -0.0103 1.20 -3 3
5 -0.2302 0.00445 -0.3306 -0.436 -0.00554 -0.0501 -0.1321 -0.4752 1.39 -3 3
6 0.1112 0.58657 0.3306 0.304 0.11728 0.3358 0.2432 -3.5822 1.85 -3 3
7 0.3382 0.39092 0.6139 0.440 0.15551 0.1410 0.4337 0.3573 1.53 -3 3
8 -0.7720 0.80280 -0.4930 0.365 -1.38238 -0.2656 -0.2863 -0.1388 6.59 -3 3
9 0.2449 0.31062 0.1934 0.273 -0.11471 0.2725 0.1750 0.1199 1.34 -3 3
10 -0.2023 0.12034 0.4547 -3.490 -0.22569 0.5324 0.6063 0.0777 2.53 -3 3
Z_Y1 Z_Y2 Z_Y3 Z_Y4 Z_Y5 Z_Y6
-0.32934778 -0.45819964 -0.67479138 0.16627737 -0.61784679 0.18825811
Z_Y7 Z_Y8 Z_Y9 Z_Y10 Z_Y11 Z_Y12
-0.22957934 -0.03943888 -0.35280113 -0.08317492 -0.66254152 -0.17192626
Z_Y13 Z_Y14 Z_Y15
-0.20114157 3.47659061 -0.01033688
Please wait...
Call:
odm(x = toy, method = "grubbs", alpha = 0.01)
Outlier Detection for Multi-replicative High-throughput Data
Method: Grubbs test ( grubbs )
Number of Observations: 200
Number of Outliers: 12
Transformation: log2
Head of the Output Results
Outlier G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15
1 TRUE . . . . 3.491 . . . . . . . . . .
2 TRUE . . . . -3.581 . . . . . . . . . .
3 TRUE . . . . . . . . . . . . . 2.434 -3.505
4 TRUE . . . . . . . . . . . . . 3.477 .
5 TRUE . . . . . 3.499 . . . . . . . . .
6 TRUE . . . . . . . . 2.378 . . . . . -3.582
To see the full information for the result, use a command, 'output(your_object_name)'.
Call:
odm(x = toy, method = "grubbs", alpha = 0.01)
Outlier Detection for Multi-replicative High-throughput Data
Method: Grubbs test ( grubbs )
Number of Observations: 200
Number of Outliers: 12
Transformation: log2
Head of the Input Data
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
1 2.68e+07 2.60e+07 1.23e+07 1.59e+07 2.92e+09 4.17e+07 2.90e+07 1.92e+07
2 2.36e+10 2.62e+10 2.50e+10 2.54e+10 5.64e+08 2.72e+10 2.51e+10 3.08e+10
3 1.04e+08 1.45e+08 1.60e+08 1.91e+08 1.22e+08 1.58e+08 1.41e+08 1.61e+08
4 1.26e+09 1.14e+09 9.49e+08 1.91e+09 9.95e+08 1.94e+09 1.37e+09 1.61e+09
5 8.55e+08 8.36e+08 1.49e+09 7.57e+08 5.51e+08 3.20e+10 7.12e+08 8.89e+08
6 4.63e+08 5.73e+08 4.64e+08 5.64e+08 6.31e+08 5.97e+08 6.86e+08 4.88e+08
Y9 Y10 Y11 Y12 Y13 Y14 Y15
1 2.40e+07 1.49e+07 2.25e+07 2.81e+07 1.88e+07 3.15e+07 1.38e+07
2 2.16e+10 3.23e+10 2.75e+10 1.87e+10 2.44e+10 2.37e+10 2.87e+10
3 1.10e+08 1.19e+08 1.83e+08 8.88e+07 1.29e+08 3.03e+08 1.56e+06
4 1.24e+09 1.55e+09 9.59e+08 1.44e+09 1.41e+09 2.98e+10 1.65e+09
5 1.11e+09 8.07e+08 7.30e+08 1.10e+09 1.06e+09 9.77e+08 7.03e+08
6 8.99e+08 6.47e+08 6.25e+08 4.92e+08 6.52e+08 5.79e+08 4.26e+06
To see the full information of the input dataset, use a command, 'input(your_object_name)'.
Head of the Output Results
Outlier G1 G2 G3 G4 G5 G6 G7 G8 G9
1 TRUE . . . . 3.49133507967136 . . . .
2 TRUE . . . . -3.58123496282741 . . . .
3 TRUE . . . . . . . . .
4 TRUE . . . . . . . . .
5 TRUE . . . . . 3.49904951444859 . . .
6 TRUE . . . . . . . . 2.37810886505246
G10 G11 G12 G13 G14 G15
1 . . . . . .
2 . . . . . .
3 . . . . 2.4344103286513 -3.50541721005009
4 . . . . 3.47659061386896 .
5 . . . . . .
6 . . . . . -3.58218328957321
To see the full information for the result, use a command, 'output(your_object_name)'.
Head of the Peptide Numbers detected to be an Outlier
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
To see the full information for the candidate outliers, use a command, 'outliers(your_object_name)'.
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7
1 TRUE 24.67486 24.63198 23.54762 23.92247 31.44197 25.31428 24.78930
2 TRUE 34.45746 34.60981 34.53928 34.56166 29.07079 34.66280 34.54834
3 TRUE 26.63303 27.11408 27.25650 27.50732 26.86640 27.23385 27.07354
4 TRUE 30.23634 30.08196 29.82247 30.83014 29.89069 30.85647 30.35587
5 TRUE 29.67061 29.63911 30.47663 29.49626 29.03767 34.89597 29.40631
6 TRUE 28.78746 29.09507 28.78952 29.07021 29.23209 29.15205 29.35342
Y8 Y9 Y10 Y11 Y12 Y13 Y14 Y15 G1 G2
1 24.19155 24.51601 23.82976 24.42456 24.74398 24.16186 24.90987 23.72162 NA NA
2 34.84045 34.32856 34.91147 34.68092 34.12130 34.50493 34.46141 34.74068 NA NA
3 27.26387 26.71366 26.83111 27.45051 26.40405 26.94825 28.17712 20.57758 NA NA
4 30.58367 30.20824 30.53127 29.83715 30.42494 30.38994 34.79615 30.61854 NA NA
5 29.72799 30.05320 29.58885 29.44340 30.03936 29.97763 29.86399 29.38854 NA NA
6 28.86347 29.74368 29.26966 29.21980 28.87466 29.27927 29.10779 22.02403 NA NA
G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15
1 NA NA 3.491335 NA NA NA NA NA NA NA NA NA NA
2 NA NA -3.581235 NA NA NA NA NA NA NA NA NA NA
3 NA NA NA NA NA NA NA NA NA NA NA 2.434410 -3.505417
4 NA NA NA NA NA NA NA NA NA NA NA 3.476591 NA
5 NA NA NA 3.49905 NA NA NA NA NA NA NA NA NA
6 NA NA NA NA NA NA 2.378109 NA NA NA NA NA -3.582183
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12
1 TRUE 24.67 24.6 23.5 23.9 31.44 25.3 24.79 24.2 24.5 23.83 24.4 24.74
2 TRUE 34.46 34.6 34.5 34.6 29.07 34.7 34.55 34.8 34.3 34.91 34.7 34.12
3 TRUE 26.63 27.1 27.3 27.5 26.87 27.2 27.07 27.3 26.7 26.83 27.5 26.40
4 TRUE 30.24 30.1 29.8 30.8 29.89 30.9 30.36 30.6 30.2 30.53 29.8 30.42
5 TRUE 29.67 29.6 30.5 29.5 29.04 34.9 29.41 29.7 30.1 29.59 29.4 30.04
6 TRUE 28.79 29.1 28.8 29.1 29.23 29.2 29.35 28.9 29.7 29.27 29.2 28.87
7 TRUE 30.95 31.0 25.6 31.3 31.46 30.9 31.60 31.6 31.6 31.97 31.7 31.27
8 TRUE 9.25 11.1 10.6 30.5 6.32 13.2 6.16 5.3 15.7 7.14 12.8 1.27
9 TRUE 34.78 35.3 29.8 35.3 35.13 34.8 35.08 34.9 35.0 34.86 35.0 34.45
10 TRUE 23.85 23.3 24.2 24.7 24.27 24.4 24.54 22.9 23.7 24.56 14.6 22.84
41 TRUE 22.00 23.2 22.1 22.0 20.96 21.8 22.56 19.0 22.0 20.95 22.3 21.40
186 TRUE 13.81 12.9 13.1 13.1 12.38 13.9 13.78 14.2 12.7 14.01 14.0 16.56
Y13 Y14 Y15 G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12
1 24.16 24.9 23.72 NA NA NA NA 3.49 NA NA NA NA NA NA NA
2 34.50 34.5 34.74 NA NA NA NA -3.58 NA NA NA NA NA NA NA
3 26.95 28.2 20.58 NA NA NA NA NA NA NA NA NA NA NA NA
4 30.39 34.8 30.62 NA NA NA NA NA NA NA NA NA NA NA NA
5 29.98 29.9 29.39 NA NA NA NA NA 3.5 NA NA NA NA NA NA
6 29.28 29.1 22.02 NA NA NA NA NA NA NA NA 2.38 NA NA NA
7 31.25 31.7 31.58 NA NA -3.54 NA NA NA NA NA NA NA NA NA
8 8.64 8.5 9.47 NA NA NA 3.05 NA NA NA NA NA NA NA NA
9 34.97 34.8 34.76 NA NA -3.57 NA NA NA NA NA NA NA NA NA
10 24.75 24.9 23.60 NA NA NA NA NA NA NA NA NA NA -3.49 NA
41 21.56 22.3 22.03 NA NA NA NA NA NA NA -2.88 NA NA NA NA
186 12.65 12.9 13.45 NA NA NA NA NA NA NA NA NA NA NA 2.96
G13 G14 G15
1 NA NA NA
2 NA NA NA
3 NA 2.43 -3.51
4 NA 3.48 NA
5 NA NA NA
6 NA NA -3.58
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
41 NA NA NA
186 NA NA NA
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
24.67486 24.63198 23.54762 23.92247 31.44197 25.31428 24.78930 24.19155
Y9 Y10 Y11 Y12 Y13 Y14 Y15
24.51601 23.82976 24.42456 24.74398 24.16186 24.90987 23.72162
Please wait...
Call:
odm(x = toy, k = 3, method = "iqr")
Outlier Detection for Multi-replicative High-throughput Data
Method: Interquartile range (IQR) criterion ( iqr )
k: 3 for k * IQR
Number of Observations: 200
Number of Outliers: 15
Transformation: log2
Head of the Output Results
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14
1 TRUE 24.7 24.6 23.5 23.9 31.4 25.3 24.8 24.2 24.5 23.8 24.4 24.7 24.2 24.9
2 TRUE 34.5 34.6 34.5 34.6 29.1 34.7 34.5 34.8 34.3 34.9 34.7 34.1 34.5 34.5
3 TRUE 26.6 27.1 27.3 27.5 26.9 27.2 27.1 27.3 26.7 26.8 27.5 26.4 26.9 28.2
4 TRUE 30.2 30.1 29.8 30.8 29.9 30.9 30.4 30.6 30.2 30.5 29.8 30.4 30.4 34.8
5 TRUE 29.7 29.6 30.5 29.5 29.0 34.9 29.4 29.7 30.1 29.6 29.4 30.0 30.0 29.9
6 TRUE 28.8 29.1 28.8 29.1 29.2 29.2 29.4 28.9 29.7 29.3 29.2 28.9 29.3 29.1
Y15 Q1 Q2 Q3 LB UB
1 23.7 24.0 24.5 24.8 21.9 26.9
2 34.7 34.5 34.5 34.7 33.8 35.3
3 20.6 26.8 27.1 27.3 25.3 28.7
4 30.6 30.1 30.4 30.6 28.8 32.0
5 29.4 29.5 29.7 30.0 27.9 31.6
6 22.0 28.9 29.1 29.3 27.7 30.4
To see the full information for the result, use a command, 'output(your_object_name)'.
Call:
odm(x = toy, k = 3, method = "iqr")
Outlier Detection for Multi-replicative High-throughput Data
Method: Interquartile range (IQR) criterion ( iqr )
k: 3 for k * IQR
Number of Observations: 200
Number of Outliers: 15
Transformation: log2
Head of the Input Data
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
1 2.68e+07 2.60e+07 1.23e+07 1.59e+07 2.92e+09 4.17e+07 2.90e+07 1.92e+07
2 2.36e+10 2.62e+10 2.50e+10 2.54e+10 5.64e+08 2.72e+10 2.51e+10 3.08e+10
3 1.04e+08 1.45e+08 1.60e+08 1.91e+08 1.22e+08 1.58e+08 1.41e+08 1.61e+08
4 1.26e+09 1.14e+09 9.49e+08 1.91e+09 9.95e+08 1.94e+09 1.37e+09 1.61e+09
5 8.55e+08 8.36e+08 1.49e+09 7.57e+08 5.51e+08 3.20e+10 7.12e+08 8.89e+08
6 4.63e+08 5.73e+08 4.64e+08 5.64e+08 6.31e+08 5.97e+08 6.86e+08 4.88e+08
Y9 Y10 Y11 Y12 Y13 Y14 Y15
1 2.40e+07 1.49e+07 2.25e+07 2.81e+07 1.88e+07 3.15e+07 1.38e+07
2 2.16e+10 3.23e+10 2.75e+10 1.87e+10 2.44e+10 2.37e+10 2.87e+10
3 1.10e+08 1.19e+08 1.83e+08 8.88e+07 1.29e+08 3.03e+08 1.56e+06
4 1.24e+09 1.55e+09 9.59e+08 1.44e+09 1.41e+09 2.98e+10 1.65e+09
5 1.11e+09 8.07e+08 7.30e+08 1.10e+09 1.06e+09 9.77e+08 7.03e+08
6 8.99e+08 6.47e+08 6.25e+08 4.92e+08 6.52e+08 5.79e+08 4.26e+06
To see the full information of the input dataset, use a command, 'input(your_object_name)'.
Head of the Output Results
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14
1 TRUE 24.7 24.6 23.5 23.9 31.4 25.3 24.8 24.2 24.5 23.8 24.4 24.7 24.2 24.9
2 TRUE 34.5 34.6 34.5 34.6 29.1 34.7 34.5 34.8 34.3 34.9 34.7 34.1 34.5 34.5
3 TRUE 26.6 27.1 27.3 27.5 26.9 27.2 27.1 27.3 26.7 26.8 27.5 26.4 26.9 28.2
4 TRUE 30.2 30.1 29.8 30.8 29.9 30.9 30.4 30.6 30.2 30.5 29.8 30.4 30.4 34.8
5 TRUE 29.7 29.6 30.5 29.5 29.0 34.9 29.4 29.7 30.1 29.6 29.4 30.0 30.0 29.9
6 TRUE 28.8 29.1 28.8 29.1 29.2 29.2 29.4 28.9 29.7 29.3 29.2 28.9 29.3 29.1
Y15 Q1 Q2 Q3 LB UB
1 23.7 24.0 24.5 24.8 21.9 26.9
2 34.7 34.5 34.5 34.7 33.8 35.3
3 20.6 26.8 27.1 27.3 25.3 28.7
4 30.6 30.1 30.4 30.6 28.8 32.0
5 29.4 29.5 29.7 30.0 27.9 31.6
6 22.0 28.9 29.1 29.3 27.7 30.4
To see the full information for the result, use a command, 'output(your_object_name)'.
Head of the Peptide Numbers detected to be an Outlier
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
To see the full information for the candidate outliers, use a command, 'outliers(your_object_name)'.
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11
1 TRUE 24.67 24.63 23.55 23.92 31.44 25.314 24.79 24.19 24.52 23.83 24.4
2 TRUE 34.46 34.61 34.54 34.56 29.07 34.663 34.55 34.84 34.33 34.91 34.7
3 TRUE 26.63 27.11 27.26 27.51 26.87 27.234 27.07 27.26 26.71 26.83 27.5
4 TRUE 30.24 30.08 29.82 30.83 29.89 30.856 30.36 30.58 30.21 30.53 29.8
5 TRUE 29.67 29.64 30.48 29.50 29.04 34.896 29.41 29.73 30.05 29.59 29.4
6 TRUE 28.79 29.10 28.79 29.07 29.23 29.152 29.35 28.86 29.74 29.27 29.2
7 TRUE 30.95 30.97 25.62 31.33 31.46 30.941 31.60 31.55 31.63 31.97 31.7
8 TRUE 9.25 11.07 10.59 30.48 6.32 13.151 6.16 5.30 15.68 7.14 12.8
9 TRUE 34.78 35.28 29.83 35.33 35.13 34.812 35.08 34.93 35.02 34.86 35.0
10 TRUE 23.85 23.32 24.16 24.72 24.27 24.367 24.54 22.90 23.71 24.56 14.6
12 TRUE 11.56 15.49 14.75 16.13 12.22 16.280 14.70 14.86 13.10 15.10 14.7
41 TRUE 22.00 23.18 22.09 22.00 20.96 21.847 22.56 19.01 22.04 20.95 22.3
68 TRUE 5.97 6.35 3.75 6.98 5.21 0.839 10.62 5.75 5.86 6.46 10.9
86 TRUE 10.63 10.93 8.20 17.06 11.93 11.930 11.20 10.57 18.94 8.64 12.0
111 TRUE 31.83 31.95 31.77 31.84 31.84 32.083 31.85 31.26 32.05 32.30 31.8
Y12 Y13 Y14 Y15 Q1 Q2 Q3 LB UB
1 24.74 24.16 24.91 23.72 24.04 24.52 24.77 21.87 26.9
2 34.12 34.50 34.46 34.74 34.46 34.55 34.67 33.82 35.3
3 26.40 26.95 28.18 20.58 26.77 27.07 27.26 25.31 28.7
4 30.42 30.39 34.80 30.62 30.15 30.39 30.60 28.78 32.0
5 30.04 29.98 29.86 29.39 29.47 29.67 30.01 27.85 31.6
6 28.87 29.28 29.11 22.02 28.87 29.11 29.25 27.72 30.4
7 31.27 31.25 31.70 31.58 31.11 31.46 31.61 29.59 33.1
8 1.27 8.64 8.50 9.47 6.73 9.25 11.93 -8.89 27.5
9 34.45 34.97 34.84 34.76 34.80 34.93 35.05 34.04 35.8
10 22.84 24.75 24.94 23.60 23.46 24.16 24.55 20.20 27.8
12 15.25 15.32 14.93 16.77 14.69 14.93 15.41 12.55 17.6
41 21.40 21.56 22.32 22.03 21.48 22.00 22.20 19.32 24.4
68 5.85 7.10 7.79 7.29 5.80 6.35 7.19 1.63 11.4
86 12.90 12.21 15.05 10.37 10.60 11.93 12.55 4.73 18.4
111 31.62 31.85 31.36 31.94 31.79 31.84 31.94 31.31 32.4
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
24.67486 24.63198 23.54762 23.92247 31.44197 25.31428 24.78930 24.19155
Y9 Y10 Y11 Y12 Y13 Y14 Y15
24.51601 23.82976 24.42456 24.74398 24.16186 24.90987 23.72162
Please wait...
Call:
odm(x = toy, k = 3, method = "siqr")
Outlier Detection for Multi-replicative High-throughput Data
Method: Semiinterquartile range (SIQR) criterion ( siqr )
k: 3 for 2k * SIQR
Number of Observations: 200
Number of Outliers: 28
Transformation: log2
Head of the Output Results
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14
1 TRUE 24.7 24.6 23.5 23.9 31.4 25.3 24.8 24.2 24.5 23.8 24.4 24.7 24.2 24.9
2 TRUE 34.5 34.6 34.5 34.6 29.1 34.7 34.5 34.8 34.3 34.9 34.7 34.1 34.5 34.5
3 TRUE 26.6 27.1 27.3 27.5 26.9 27.2 27.1 27.3 26.7 26.8 27.5 26.4 26.9 28.2
4 TRUE 30.2 30.1 29.8 30.8 29.9 30.9 30.4 30.6 30.2 30.5 29.8 30.4 30.4 34.8
5 TRUE 29.7 29.6 30.5 29.5 29.0 34.9 29.4 29.7 30.1 29.6 29.4 30.0 30.0 29.9
6 TRUE 28.8 29.1 28.8 29.1 29.2 29.2 29.4 28.9 29.7 29.3 29.2 28.9 29.3 29.1
Y15 Q1 Q2 Q3 LB UB
1 23.7 24.0 24.5 24.8 21.2 26.3
2 34.7 34.5 34.5 34.7 33.9 35.4
3 20.6 26.8 27.1 27.3 25.0 28.4
4 30.6 30.1 30.4 30.6 28.7 31.9
5 29.4 29.5 29.7 30.0 28.3 32.0
6 22.0 28.9 29.1 29.3 27.4 30.1
To see the full information for the result, use a command, 'output(your_object_name)'.
Call:
odm(x = toy, k = 3, method = "siqr")
Outlier Detection for Multi-replicative High-throughput Data
Method: Semiinterquartile range (SIQR) criterion ( siqr )
k: 3 for 2k * SIQR
Number of Observations: 200
Number of Outliers: 28
Transformation: log2
Head of the Input Data
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
1 2.68e+07 2.60e+07 1.23e+07 1.59e+07 2.92e+09 4.17e+07 2.90e+07 1.92e+07
2 2.36e+10 2.62e+10 2.50e+10 2.54e+10 5.64e+08 2.72e+10 2.51e+10 3.08e+10
3 1.04e+08 1.45e+08 1.60e+08 1.91e+08 1.22e+08 1.58e+08 1.41e+08 1.61e+08
4 1.26e+09 1.14e+09 9.49e+08 1.91e+09 9.95e+08 1.94e+09 1.37e+09 1.61e+09
5 8.55e+08 8.36e+08 1.49e+09 7.57e+08 5.51e+08 3.20e+10 7.12e+08 8.89e+08
6 4.63e+08 5.73e+08 4.64e+08 5.64e+08 6.31e+08 5.97e+08 6.86e+08 4.88e+08
Y9 Y10 Y11 Y12 Y13 Y14 Y15
1 2.40e+07 1.49e+07 2.25e+07 2.81e+07 1.88e+07 3.15e+07 1.38e+07
2 2.16e+10 3.23e+10 2.75e+10 1.87e+10 2.44e+10 2.37e+10 2.87e+10
3 1.10e+08 1.19e+08 1.83e+08 8.88e+07 1.29e+08 3.03e+08 1.56e+06
4 1.24e+09 1.55e+09 9.59e+08 1.44e+09 1.41e+09 2.98e+10 1.65e+09
5 1.11e+09 8.07e+08 7.30e+08 1.10e+09 1.06e+09 9.77e+08 7.03e+08
6 8.99e+08 6.47e+08 6.25e+08 4.92e+08 6.52e+08 5.79e+08 4.26e+06
To see the full information of the input dataset, use a command, 'input(your_object_name)'.
Head of the Output Results
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14
1 TRUE 24.7 24.6 23.5 23.9 31.4 25.3 24.8 24.2 24.5 23.8 24.4 24.7 24.2 24.9
2 TRUE 34.5 34.6 34.5 34.6 29.1 34.7 34.5 34.8 34.3 34.9 34.7 34.1 34.5 34.5
3 TRUE 26.6 27.1 27.3 27.5 26.9 27.2 27.1 27.3 26.7 26.8 27.5 26.4 26.9 28.2
4 TRUE 30.2 30.1 29.8 30.8 29.9 30.9 30.4 30.6 30.2 30.5 29.8 30.4 30.4 34.8
5 TRUE 29.7 29.6 30.5 29.5 29.0 34.9 29.4 29.7 30.1 29.6 29.4 30.0 30.0 29.9
6 TRUE 28.8 29.1 28.8 29.1 29.2 29.2 29.4 28.9 29.7 29.3 29.2 28.9 29.3 29.1
Y15 Q1 Q2 Q3 LB UB
1 23.7 24.0 24.5 24.8 21.2 26.3
2 34.7 34.5 34.5 34.7 33.9 35.4
3 20.6 26.8 27.1 27.3 25.0 28.4
4 30.6 30.1 30.4 30.6 28.7 31.9
5 29.4 29.5 29.7 30.0 28.3 32.0
6 22.0 28.9 29.1 29.3 27.4 30.1
To see the full information for the result, use a command, 'output(your_object_name)'.
Head of the Peptide Numbers detected to be an Outlier
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
To see the full information for the candidate outliers, use a command, 'outliers(your_object_name)'.
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11
1 TRUE 24.67 24.63 23.55 23.92 31.44 25.314 24.79 24.19 24.52 23.83 24.42
2 TRUE 34.46 34.61 34.54 34.56 29.07 34.663 34.55 34.84 34.33 34.91 34.68
3 TRUE 26.63 27.11 27.26 27.51 26.87 27.234 27.07 27.26 26.71 26.83 27.45
4 TRUE 30.24 30.08 29.82 30.83 29.89 30.856 30.36 30.58 30.21 30.53 29.84
5 TRUE 29.67 29.64 30.48 29.50 29.04 34.896 29.41 29.73 30.05 29.59 29.44
6 TRUE 28.79 29.10 28.79 29.07 29.23 29.152 29.35 28.86 29.74 29.27 29.22
7 TRUE 30.95 30.97 25.62 31.33 31.46 30.941 31.60 31.55 31.63 31.97 31.71
8 TRUE 9.25 11.07 10.59 30.48 6.32 13.151 6.16 5.30 15.68 7.14 12.79
9 TRUE 34.78 35.28 29.83 35.33 35.13 34.812 35.08 34.93 35.02 34.86 34.97
10 TRUE 23.85 23.32 24.16 24.72 24.27 24.367 24.54 22.90 23.71 24.56 14.57
12 TRUE 11.56 15.49 14.75 16.13 12.22 16.280 14.70 14.86 13.10 15.10 14.69
24 TRUE 18.74 17.09 17.28 18.03 17.83 20.621 17.82 16.30 17.85 16.21 17.36
45 TRUE 34.56 34.49 34.69 34.53 34.73 34.467 34.68 34.70 34.55 34.54 35.06
48 TRUE 9.05 12.25 6.75 2.14 7.67 7.889 6.39 7.28 11.16 7.58 5.73
63 TRUE 22.74 23.47 22.97 23.08 22.85 23.069 23.27 23.16 23.02 23.74 22.44
68 TRUE 5.97 6.35 3.75 6.98 5.21 0.839 10.62 5.75 5.86 6.46 10.91
86 TRUE 10.63 10.93 8.20 17.06 11.93 11.930 11.20 10.57 18.94 8.64 11.97
98 TRUE 13.77 12.59 10.14 13.31 10.43 15.095 17.20 13.27 11.18 13.19 12.03
111 TRUE 31.83 31.95 31.77 31.84 31.84 32.083 31.85 31.26 32.05 32.30 31.80
113 TRUE 13.43 14.88 16.36 9.20 14.60 12.970 17.41 11.97 16.13 12.54 13.66
121 TRUE 25.03 24.85 24.44 25.01 24.65 24.560 24.50 25.00 25.27 24.31 24.93
145 TRUE 21.46 20.66 20.71 20.51 20.71 21.889 20.56 21.91 20.65 19.91 18.62
162 TRUE 34.97 34.72 34.67 34.79 35.11 34.797 34.73 34.83 34.50 34.66 34.81
164 TRUE 10.26 10.38 10.36 14.40 8.03 10.042 10.43 10.89 5.70 12.30 10.54
190 TRUE 25.70 26.63 25.22 26.79 25.92 25.649 25.67 25.60 25.37 26.53 24.08
191 TRUE 20.38 21.16 20.41 19.31 20.08 20.243 20.47 19.89 21.67 20.25 20.38
196 TRUE 30.55 30.84 31.43 30.71 30.68 30.456 30.66 30.72 31.35 30.75 30.38
198 TRUE 23.33 23.15 23.79 23.13 22.21 23.336 24.70 22.77 23.17 23.41 22.66
Y12 Y13 Y14 Y15 Q1 Q2 Q3 LB UB
1 24.74 24.16 24.91 23.72 24.04 24.52 24.77 21.1991 26.3
2 34.12 34.50 34.46 34.74 34.46 34.55 34.67 33.9260 35.4
3 26.40 26.95 28.18 20.58 26.77 27.07 27.26 24.9654 28.4
4 30.42 30.39 34.80 30.62 30.15 30.39 30.60 28.6761 31.9
5 30.04 29.98 29.86 29.39 29.47 29.67 30.01 28.2651 32.0
6 28.87 29.28 29.11 22.02 28.87 29.11 29.25 27.4367 30.1
7 31.27 31.25 31.70 31.58 31.11 31.46 31.61 29.0027 32.5
8 1.27 8.64 8.50 9.47 6.73 9.25 11.93 -8.4133 28.0
9 34.45 34.97 34.84 34.76 34.80 34.93 35.05 33.9912 35.8
10 22.84 24.75 24.94 23.60 23.46 24.16 24.55 19.2536 26.9
12 15.25 15.32 14.93 16.77 14.69 14.93 15.41 13.3113 18.3
24 16.25 17.61 16.45 18.71 16.77 17.61 17.94 11.7393 20.0
45 34.50 34.83 35.02 34.73 34.53 34.68 34.73 33.6432 35.0
48 12.97 10.55 9.05 -2.68 6.57 7.67 9.80 -0.0349 22.6
63 23.49 23.57 21.76 23.39 22.91 23.08 23.43 21.9175 25.6
68 5.85 7.10 7.79 7.29 5.80 6.35 7.19 2.5320 12.3
86 12.90 12.21 15.05 10.37 10.60 11.93 12.55 2.6157 16.3
98 13.06 12.57 12.75 14.00 12.30 13.06 13.54 7.7271 16.4
111 31.62 31.85 31.36 31.94 31.79 31.84 31.94 31.4638 32.6
113 13.08 17.39 17.32 13.57 13.02 13.66 16.24 9.2084 31.8
121 26.17 25.00 24.57 25.61 24.57 24.93 25.02 22.3813 25.6
145 20.61 20.03 18.91 19.15 19.97 20.61 20.71 16.1097 21.3
162 34.81 34.84 34.81 34.78 34.72 34.80 34.82 34.2801 35.0
164 10.81 13.19 15.10 14.18 10.31 10.54 12.75 8.9661 26.0
190 25.15 26.06 26.35 26.34 25.48 25.70 26.34 24.1835 30.2
191 19.90 21.32 18.94 19.91 19.90 20.25 20.44 17.8440 21.6
196 30.42 30.11 30.52 30.60 30.49 30.66 30.74 29.4349 31.2
198 23.86 22.33 23.27 22.77 22.77 23.17 23.37 20.3838 24.6
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
24.67486 24.63198 23.54762 23.92247 31.44197 25.31428 24.78930 24.19155
Y9 Y10 Y11 Y12 Y13 Y14 Y15
24.51601 23.82976 24.42456 24.74398 24.16186 24.90987 23.72162
N1 N2 N3
1 3148600 3351500 2025000
4 162200 19480 293430
5 6170400 6917700 8854800
6 54839000 95569000 50611000
7 5056100 355700 5169900
8 35878900 52117200 61609400
Please wait...
Call:
odm(x = lcms3[, 1:2], k = 3, method = "pair")
Outlier Detection for Multi-replicative High-throughput Data
Method: Pairwise OutlierD algoirthm ( pair )
Reg: linear quantile regression ( linear )
k: 3 for k * IQR
Upper Quantile: 0.75
Lower Quantile: 0.25
Number of Observations: 922
Number of Outliers: 16
Centering: TRUE
Transformation: log2
Head of the Output Results
Outlier N1 N2 A M Q1 Q3 LB UB
1 FALSE 21.6 21.7 21.6 -0.328 -0.589 0.589 -4.12 4.12
2 FALSE 17.3 14.2 15.8 2.740 -0.886 0.886 -6.20 6.20
3 FALSE 22.6 22.7 22.6 -0.390 -0.537 0.537 -3.76 3.76
4 FALSE 25.7 26.5 26.1 -0.979 -0.361 0.361 -2.53 2.53
5 FALSE 22.3 18.4 20.4 3.574 -0.654 0.654 -4.58 4.58
6 FALSE 25.1 25.6 25.4 -0.726 -0.399 0.399 -2.79 2.79
To see the full information for the result, use a command, 'output(your_object_name)'.
Call:
odm(x = lcms3[, 1:2], k = 3, method = "pair")
Outlier Detection for Multi-replicative High-throughput Data
Method: Pairwise OutlierD algoirthm ( pair )
Regression: linear quantile regression ( linear )
k: 3 for k * IQR
Upper Quantile: 0.75
Lower Quantile: 0.25
Number of Observations: 922
Number of Outliers: 16
Centering: TRUE
Transformation: log2
Head of the Input Data
N1 N2
1 3148600 3351500
2 162200 19480
3 6170400 6917700
4 54839000 95569000
5 5056100 355700
6 35878900 52117200
To see the full information of the input dataset, use a command, 'input(your_object_name)'.
Head of the Output Results
Outlier N1 N2 A M Q1 Q3 LB UB
1 FALSE 21.6 21.7 21.6 -0.328 -0.589 0.589 -4.12 4.12
2 FALSE 17.3 14.2 15.8 2.740 -0.886 0.886 -6.20 6.20
3 FALSE 22.6 22.7 22.6 -0.390 -0.537 0.537 -3.76 3.76
4 FALSE 25.7 26.5 26.1 -0.979 -0.361 0.361 -2.53 2.53
5 FALSE 22.3 18.4 20.4 3.574 -0.654 0.654 -4.58 4.58
6 FALSE 25.1 25.6 25.4 -0.726 -0.399 0.399 -2.79 2.79
To see the full information for the result, use a command, 'output(your_object_name)'.
Head of the Peptide Numbers detected to be an Outlier
[1] "66" "94" "145" "236" "319" "324" "413" "448" "458" "460"
To see the full information for the candidate outliers, use a command, 'outliers(your_object_name)'.
Outlier N1 N2 A M Q1 Q3 LB
1 FALSE 21.58628 21.67638 21.63133 -0.3284366 -0.5887466 0.5887466 -4.121226
2 FALSE 17.30741 14.24971 15.77856 2.7402724 -0.8863551 0.8863551 -6.204486
3 FALSE 22.55693 22.72186 22.63940 -0.3896453 -0.5374871 0.5374871 -3.762409
4 FALSE 25.70870 26.51004 26.10937 -0.9791633 -0.3610417 0.3610417 -2.527292
5 FALSE 22.26959 18.44030 20.35495 3.5737027 -0.6536495 0.6536495 -4.575546
6 FALSE 25.09663 25.63526 25.36594 -0.7264936 -0.3988443 0.3988443 -2.791910
UB
1 4.121226
2 6.204486
3 3.762409
4 2.527292
5 4.575546
6 2.791910
Outlier N1 N2 A M Q1 Q3 LB UB
66 TRUE 32.3 32.8 32.6 -0.600 -0.0317 0.0317 -0.222 0.222
94 TRUE 23.7 18.2 21.0 5.220 -0.6227 0.6227 -4.359 4.359
145 TRUE 23.9 19.1 21.5 4.635 -0.5960 0.5960 -4.172 4.172
236 TRUE 24.9 19.8 22.4 4.878 -0.5505 0.5505 -3.854 3.854
319 TRUE 15.0 22.2 18.6 -7.473 -0.7407 0.7407 -5.185 5.185
324 TRUE 26.9 21.7 24.3 4.946 -0.4531 0.4531 -3.172 3.172
413 TRUE 19.7 24.2 21.9 -4.742 -0.5740 0.5740 -4.018 4.018
448 TRUE 31.7 32.1 31.9 -0.542 -0.0679 0.0679 -0.475 0.475
458 TRUE 25.3 28.6 26.9 -3.522 -0.3186 0.3186 -2.230 2.230
460 TRUE 28.7 17.5 23.1 10.955 -0.5122 0.5122 -3.586 3.586
541 TRUE 25.9 20.4 23.2 5.319 -0.5111 0.5111 -3.578 3.578
661 TRUE 15.5 22.7 19.1 -7.436 -0.7188 0.7188 -5.032 5.032
751 TRUE 18.0 23.5 20.7 -5.773 -0.6343 0.6343 -4.440 4.440
782 TRUE 28.1 24.9 26.5 3.029 -0.3419 0.3419 -2.393 2.393
796 TRUE 25.3 17.0 21.1 8.116 -0.6134 0.6134 -4.294 4.294
906 TRUE 30.6 20.2 25.4 10.297 -0.3975 0.3975 -2.783 2.783
Please wait...
Call:
odm(x = lcms3, k = 3, method = "proj", center = TRUE)
Outlier Detection for Multi-replicative High-throughput Data
Method: Projection-based OutlierD algorithm ( proj )
Reg: linear quantile regression ( linear )
k: 3 for k * IQR
Upper Quantile: 0.75
Lower Quantile: 0.25
Number of Observations: 922
Number of Outliers: 26
Transformation: log2
Head of the Output Results
Outlier N1 N2 N3 A M Q1 Q3 LB UB
1 FALSE -0.453 -0.0893 -1.3298 -1.08 0.900 0.364 1.113 -1.88 3.36
2 FALSE -4.732 -7.5160 -4.1167 -9.44 2.586 0.512 1.579 -2.69 4.78
3 FALSE 0.517 0.9562 0.7987 1.31 0.318 0.322 0.980 -1.65 2.95
4 FALSE 3.669 4.7443 3.3136 6.77 1.070 0.226 0.675 -1.12 2.02
5 FALSE 0.230 -3.3254 0.0224 -1.77 2.827 0.376 1.151 -1.95 3.48
6 FALSE 3.057 3.8696 3.5973 6.07 0.602 0.238 0.714 -1.19 2.14
To see the full information for the result, use a command, 'output(your_object_name)'.
Call:
odm(x = lcms3, k = 3, method = "proj", center = TRUE)
Outlier Detection for Multi-replicative High-throughput Data
Method: Projection-based OutlierD algorithm ( proj )
Regression: linear quantile regression ( linear )
k: 3 for k * IQR
Upper Quantile: 0.75
Lower Quantile: 0.25
Number of Observations: 922
Number of Outliers: 26
Transformation: log2
Head of the Input Data
N1 N2 N3
1 3148600 3351500 2025000
2 162200 19480 293430
3 6170400 6917700 8854800
4 54839000 95569000 50611000
5 5056100 355700 5169900
6 35878900 52117200 61609400
To see the full information of the input dataset, use a command, 'input(your_object_name)'.
Head of the Output Results
Outlier N1 N2 N3 A M Q1 Q3 LB UB
1 FALSE -0.453 -0.0893 -1.3298 -1.08 0.900 0.364 1.113 -1.88 3.36
2 FALSE -4.732 -7.5160 -4.1167 -9.44 2.586 0.512 1.579 -2.69 4.78
3 FALSE 0.517 0.9562 0.7987 1.31 0.318 0.322 0.980 -1.65 2.95
4 FALSE 3.669 4.7443 3.3136 6.77 1.070 0.226 0.675 -1.12 2.02
5 FALSE 0.230 -3.3254 0.0224 -1.77 2.827 0.376 1.151 -1.95 3.48
6 FALSE 3.057 3.8696 3.5973 6.07 0.602 0.238 0.714 -1.19 2.14
To see the full information for the result, use a command, 'output(your_object_name)'.
Head of the Peptide Numbers detected to be an Outlier
[1] "18" "50" "66" "94" "120" "145" "211" "236" "319" "324"
To see the full information for the candidate outliers, use a command, 'outliers(your_object_name)'.
Outlier N1 N2 N3 A M Q1 Q3 LB UB
18 TRUE -3.111 -1.6157 8.619 2.2484 9.029 0.3055 0.9273 -1.56008 2.7928
50 TRUE 8.614 8.6603 1.027 10.5631 6.219 0.1588 0.4635 -0.75551 1.3778
66 TRUE 10.293 11.0763 10.426 18.3552 0.647 0.0213 0.0289 -0.00151 0.0518
94 TRUE 1.658 -3.5350 1.733 -0.0713 4.271 0.3464 1.0567 -1.78455 3.1876
120 TRUE -2.042 1.4419 2.160 0.8942 3.180 0.3293 1.0028 -1.69113 3.0233
145 TRUE 1.887 -2.7151 1.595 0.4533 3.643 0.3371 1.0274 -1.73379 3.0983
211 TRUE 7.261 7.1830 5.221 11.3525 1.639 0.1449 0.4195 -0.67913 1.2435
236 TRUE 2.896 -1.9359 2.815 2.1906 3.906 0.3065 0.9305 -1.56568 2.8027
319 TRUE -6.994 0.4746 -2.498 -5.2217 5.302 0.4372 1.3439 -2.28293 4.0641
324 TRUE 4.833 -0.0407 4.944 5.6329 4.010 0.2458 0.7385 -1.23259 2.2169
380 TRUE 3.174 -0.0174 3.841 4.0475 2.906 0.2737 0.8270 -1.38600 2.4867
413 TRUE -2.371 2.4102 -0.352 -0.1910 3.394 0.3485 1.0634 -1.79614 3.2080
440 TRUE 1.556 -2.3869 1.491 0.3905 3.192 0.3382 1.0309 -1.73986 3.1090
448 TRUE 9.614 10.3301 9.703 17.1155 0.602 0.0432 0.0981 -0.12147 0.2627
458 TRUE 3.227 6.8557 3.069 7.5848 3.050 0.2113 0.6297 -1.04371 1.8847
460 TRUE 6.683 -4.2165 5.870 4.8380 8.573 0.2598 0.7829 -1.30951 2.3522
470 TRUE -1.971 -5.5501 0.641 -3.9630 4.404 0.4150 1.2737 -2.16114 3.8499
477 TRUE -4.025 0.3565 0.231 -1.9931 3.523 0.3803 1.1639 -1.97051 3.5147
541 TRUE 3.887 -1.3753 0.346 1.6610 3.790 0.3158 0.9601 -1.61693 2.8928
661 TRUE -6.547 0.8896 1.591 -2.3615 6.373 0.3868 1.1844 -2.00617 3.5774
747 TRUE -0.665 -5.1162 -0.190 -3.4368 3.853 0.4057 1.2444 -2.11022 3.7604
751 TRUE -4.066 1.7310 -2.152 -2.6033 4.169 0.3910 1.1979 -2.02956 3.6185
782 TRUE 6.047 3.1195 5.993 8.7592 2.344 0.1906 0.5642 -0.93007 1.6848
796 TRUE 3.288 -4.7988 3.450 1.1383 6.667 0.3250 0.9892 -1.66750 2.9818
844 TRUE 4.788 5.3965 -4.599 3.2194 7.927 0.2883 0.8731 -1.46613 2.6276
906 TRUE 8.595 -1.6157 -3.343 2.1187 9.120 0.3077 0.9345 -1.57263 2.8149
N1 N2 N3
-3.110796 -1.615662 8.619094
Please wait...
Please wait...
Please wait...
Please wait...
OutlierDM documentation built on May 1, 2019, 7:58 p.m.
R Package Documentation
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Description Usage Arguments Value References See Also Examples
Description
This function provides some routines for detecting outlying observations (peptides) for multi-replicated high-throughput data, especially in LC/MS experiments.
Usage
1 2 3 4 |
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 IQR-based algorithms such as |
quantreg |
type of quantile regression models used for the outlier detection method. You can use one of the |
method |
type of outlier detection methods. You can select one of the |
... |
minor tuning parameters used in odm.control(). See |
Value
|
evaluated function call |
|
raw dataset used in the model fitting |
|
result matrix of the model fitting. It consists of used data set with some transformation and outlying statistic. |
|
Object of class |
|
threshold parameter for constructing outlier detection methods |
|
matrix including the status of each outlying peptide and sample |
|
the number of outlying parameters (peptides) to be detected by the model fitting. |
|
type of quantile regression used for the model fitting |
|
type of outlier detection method used for the model fitting |
|
a list of minor parameters |
References
Eo, S-H and Cho, H (2015) OutlierDM: More robust outlier detection algorithms for multi-replicated high-throughput data.
Cho, H and Eo, S-H. (2015) Outlier detection for mass-spectrometry data.
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.
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 24:882–884.
Grubbs FE (1969) Procedures for detecting outlying observations in samples. Technometrics 11:1–21.
Dixon WJ (1951) Ratios involving extreme values. Ann Math Statistics 22:68–78.
Dixon WJ (1950) Analysis of extreme values. Ann Math Statistics 21:488–506.
Grubbs FE (1950) Sample criteria for testing outlying observations. Ann Math Statistics 21:27–58.
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | ## Not run:
##############################################################
#
# Outlier Detection for Mass Spectrometry Data
# Section 3. Illustration
# by HyungJun Cho and Soo-Heang Eo,
# Dept of Statistics, Korea University, Seoul, Korea
#
##############################################################
#####
# Load a package OutlierDM
# If an OutlierDM package is not installed on your system, type
#install.package('OutlierDM', dependency = TRUE)
library(OutlierDM)
#####
# Sec 3.1 When the number of replicates is large enough
## Load toy dataset
data(toy)
head(toy)
pairs(log2(toy), pch = 20, cex = .7)
#####
# Fit 1. Z-score based criterion
fit1 = odm(x = toy, method = "Zscore", k = 3)
fit1
summary(fit1)
head(input(fit1))
head(output(fit1))
print(outliers(fit1), digits = 3)
plot(fit1)
rect(1, -4, 10, 4, col = heat.colors(20,alpha = 0.3), border = heat.colors(20,alpha = 0.5))
oneplot(object = fit1, i = 4)
title("Outlier Detection by the Z-score criterion")
# Add a peptide name on a dot-plot
#oneplot(fit1, 191,1)
#title("Outlier Detection by the Z-score criterion")
#####
# Fit 2. Grubbs test criteria
fit2 = odm(x = toy, method ="grubbs", alpha = 0.01)
fit2
summary(fit2)
head(output(fit2))
print(outliers(fit2), digits = 3)
oneplot(object = fit2, i = 1)
title("Outlier Detection by the Grubbs criterion")
# Add text
#oneplot(fit2, 191,1)
#title("Outlier Detection by the Grubbs criterion")
#####
# Fit 3. IQR criteria
fit3 = odm(x = toy, method = "iqr", k = 3)
fit3
summary(fit3)
print(outliers(fit3), digits = 3)
plot(fit3)
rect(1, -4, 10, 40, col = heat.colors(20,alpha = 0.3), border = heat.colors(20,alpha = 0.5))
oneplot(fit3, 1)
title("Outlier Detection by the IQR criterion")
# Add a peptide name on a dot-plot
#oneplot(fit3, 1, 1)
#title("Outlier Detection by the IQR criterion")
#####
# Fit 4. SIQR criteria
fit4 = odm(x = toy, method = "siqr", k = 3)
fit4
summary(fit4)
print(outliers(fit4), digits = 3)
plot(fit4)
rect(1, -4, 10, 4, col = heat.colors(20,alpha = 0.3), border = heat.colors(20,alpha = 0.5))
oneplot(fit4, 1)
title("Outlier Detection by the SIQR criterion")
#####################
## Real data example
#####################
data(lcms3)
head(lcms3)
pairs(log2(lcms3), pch = 20, cex = .7)
#####
# Fit 5. OutlierD
fit5 = odm(lcms3[,1:2], method = "pair", k = 3)
fit5
summary(fit5)
head(output(fit5))
print(outliers(fit5), digits = 3)
plot(fit5)
title("Outlier Detection by the OutlierD algorithm")
#####
# Fit 6. OutlierDM
fit6 = odm(lcms3, method = "proj", k = 3, center = TRUE)
fit6
summary(fit6)
print(outliers(fit6), digits = 3)
plot(fit6)
title("Outlier Detection by the OutlierDM algorithm")
oneplot(fit6, 18)
#oneplot(fit6, 18, 1)
title("The dotplot for the 18th samples of the lcms3 data")
### End of the illustration
#####
# Other OutlierDM algorithms
data(lcms3)
## Load
## Fit projection approaches
fit.proj.const <- odm(lcms3, quantreg="constant")
fit.proj.linear <- odm(lcms3, quantreg="linear")
fit.proj.nonlin <- odm(lcms3, quantreg="nonlin")
fit.proj.nonpara <- odm(lcms3, quantreg="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")
## End(Not run)
|
Example output
Package OutlierDM (1.1.1) loaded.
Y1 Y2 Y3 Y4 Y5 Y6
[1,] 26783800 25999425 12261435 15899444 2917266265 41721220
[2,] 23590082416 26217449538 24966545767 25356948796 563871754 27198324106
[3,] 104073761 145261911 160333545 190778246 122346655 157835864
[4,] 1264867022 1136510555 949419238 1908955569 995395157 1944121226
[5,] 854562021 836104879 1494102552 757282589 551074573 31969323311
[6,] 463327911 573439414 463991305 563645631 630575491 596544140
Y7 Y8 Y9 Y10 Y11 Y12
[1,] 28995062 19159370 23991286 14909836 22517745 28098385
[2,] 25123907000 30762477540 21573786718 32314611785 27542098540 18686856711
[3,] 141236692 161154539 110055848 119390039 183411946 88799743
[4,] 1374127749 1609172868 1240469934 1551775778 959126814 1441518203
[5,] 711510830 889235783 1114078381 807480809 730038584 1103440211
[6,] 685898577 488392855 898960080 647209727 625225322 492196078
Y13 Y14 Y15
[1,] 18769087 31522265 13833118
[2,] 24379205665 23654728747 28706961292
[3,] 129488807 303499529 1564839
[4,] 1406965363 29832199806 1648536466
[5,] 1057219333 977140013 702802584
[6,] 651534620 578519247 4264738
Please wait...
Call:
odm(x = toy, k = 3, method = "Zscore")
Outlier Detection for Multi-replicative High-throughput Data
Method: Z-score criterion ( Zscore )
k: 3 for |Z| > k
Number of Observations: 200
Number of Outliers: 10
Transformation: log2
Head of the Output Results
Outlier Z_Y1 Z_Y2 Z_Y3 Z_Y4 Z_Y5 Z_Y6 Z_Y7 Z_Y8 Z_Y9
1 TRUE -0.0954 -0.118 -0.6928 -0.494 3.491 0.244 -0.0347 -0.3515 -0.17956
2 TRUE 0.1778 0.284 0.2349 0.251 -3.581 0.321 0.2412 0.4451 0.08786
3 TRUE -0.0213 0.255 0.3374 0.482 0.113 0.324 0.2322 0.3417 0.02509
4 TRUE -0.3293 -0.458 -0.6748 0.166 -0.618 0.188 -0.2296 -0.0394 -0.35280
5 TRUE -0.2716 -0.294 0.3100 -0.397 -0.728 3.499 -0.4624 -0.2302 0.00445
6 TRUE 0.0702 0.236 0.0713 0.223 0.310 0.267 0.3758 0.1112 0.58657
Z_Y10 Z_Y11 Z_Y12 Z_Y13 Z_Y14 Z_Y15 SD LB UB
1 -0.5433 -0.228 -0.05872 -0.3673 0.0292 -0.6006 1.89 -3 3
2 0.4946 0.334 -0.05677 0.2109 0.1806 0.3755 1.43 -3 3
3 0.0927 0.449 -0.15305 0.1601 0.8671 -3.5054 1.74 -3 3
4 -0.0832 -0.663 -0.17193 -0.2011 3.4766 -0.0103 1.20 -3 3
5 -0.3306 -0.436 -0.00554 -0.0501 -0.1321 -0.4752 1.39 -3 3
6 0.3306 0.304 0.11728 0.3358 0.2432 -3.5822 1.85 -3 3
To see the full information for the result, use a command, 'output(your_object_name)'.
Call:
odm(x = toy, k = 3, method = "Zscore")
Outlier Detection for Multi-replicative High-throughput Data
Method: Z-score criterion ( Zscore )
k: 3 for |Z| > k
Number of Observations: 200
Number of Outliers: 10
Transformation: log2
Head of the Input Data
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
1 2.68e+07 2.60e+07 1.23e+07 1.59e+07 2.92e+09 4.17e+07 2.90e+07 1.92e+07
2 2.36e+10 2.62e+10 2.50e+10 2.54e+10 5.64e+08 2.72e+10 2.51e+10 3.08e+10
3 1.04e+08 1.45e+08 1.60e+08 1.91e+08 1.22e+08 1.58e+08 1.41e+08 1.61e+08
4 1.26e+09 1.14e+09 9.49e+08 1.91e+09 9.95e+08 1.94e+09 1.37e+09 1.61e+09
5 8.55e+08 8.36e+08 1.49e+09 7.57e+08 5.51e+08 3.20e+10 7.12e+08 8.89e+08
6 4.63e+08 5.73e+08 4.64e+08 5.64e+08 6.31e+08 5.97e+08 6.86e+08 4.88e+08
Y9 Y10 Y11 Y12 Y13 Y14 Y15
1 2.40e+07 1.49e+07 2.25e+07 2.81e+07 1.88e+07 3.15e+07 1.38e+07
2 2.16e+10 3.23e+10 2.75e+10 1.87e+10 2.44e+10 2.37e+10 2.87e+10
3 1.10e+08 1.19e+08 1.83e+08 8.88e+07 1.29e+08 3.03e+08 1.56e+06
4 1.24e+09 1.55e+09 9.59e+08 1.44e+09 1.41e+09 2.98e+10 1.65e+09
5 1.11e+09 8.07e+08 7.30e+08 1.10e+09 1.06e+09 9.77e+08 7.03e+08
6 8.99e+08 6.47e+08 6.25e+08 4.92e+08 6.52e+08 5.79e+08 4.26e+06
To see the full information of the input dataset, use a command, 'input(your_object_name)'.
Head of the Output Results
Outlier Z_Y1 Z_Y2 Z_Y3 Z_Y4 Z_Y5 Z_Y6 Z_Y7 Z_Y8 Z_Y9
1 TRUE -0.0954 -0.118 -0.6928 -0.494 3.491 0.244 -0.0347 -0.3515 -0.17956
2 TRUE 0.1778 0.284 0.2349 0.251 -3.581 0.321 0.2412 0.4451 0.08786
3 TRUE -0.0213 0.255 0.3374 0.482 0.113 0.324 0.2322 0.3417 0.02509
4 TRUE -0.3293 -0.458 -0.6748 0.166 -0.618 0.188 -0.2296 -0.0394 -0.35280
5 TRUE -0.2716 -0.294 0.3100 -0.397 -0.728 3.499 -0.4624 -0.2302 0.00445
6 TRUE 0.0702 0.236 0.0713 0.223 0.310 0.267 0.3758 0.1112 0.58657
Z_Y10 Z_Y11 Z_Y12 Z_Y13 Z_Y14 Z_Y15 SD LB UB
1 -0.5433 -0.228 -0.05872 -0.3673 0.0292 -0.6006 1.89 -3 3
2 0.4946 0.334 -0.05677 0.2109 0.1806 0.3755 1.43 -3 3
3 0.0927 0.449 -0.15305 0.1601 0.8671 -3.5054 1.74 -3 3
4 -0.0832 -0.663 -0.17193 -0.2011 3.4766 -0.0103 1.20 -3 3
5 -0.3306 -0.436 -0.00554 -0.0501 -0.1321 -0.4752 1.39 -3 3
6 0.3306 0.304 0.11728 0.3358 0.2432 -3.5822 1.85 -3 3
To see the full information for the result, use a command, 'output(your_object_name)'.
Head of the Peptide Numbers detected to be an Outlier
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
To see the full information for the candidate outliers, use a command, 'outliers(your_object_name)'.
Y1 Y2 Y3 Y4 Y5 Y6
1 26783800 25999425 12261435 15899444 2917266265 41721220
2 23590082416 26217449538 24966545767 25356948796 563871754 27198324106
3 104073761 145261911 160333545 190778246 122346655 157835864
4 1264867022 1136510555 949419238 1908955569 995395157 1944121226
5 854562021 836104879 1494102552 757282589 551074573 31969323311
6 463327911 573439414 463991305 563645631 630575491 596544140
Y7 Y8 Y9 Y10 Y11 Y12
1 28995062 19159370 23991286 14909836 22517745 28098385
2 25123907000 30762477540 21573786718 32314611785 27542098540 18686856711
3 141236692 161154539 110055848 119390039 183411946 88799743
4 1374127749 1609172868 1240469934 1551775778 959126814 1441518203
5 711510830 889235783 1114078381 807480809 730038584 1103440211
6 685898577 488392855 898960080 647209727 625225322 492196078
Y13 Y14 Y15
1 18769087 31522265 13833118
2 24379205665 23654728747 28706961292
3 129488807 303499529 1564839
4 1406965363 29832199806 1648536466
5 1057219333 977140013 702802584
6 651534620 578519247 4264738
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7
1 TRUE 24.67486 24.63198 23.54762 23.92247 31.44197 25.31428 24.78930
2 TRUE 34.45746 34.60981 34.53928 34.56166 29.07079 34.66280 34.54834
3 TRUE 26.63303 27.11408 27.25650 27.50732 26.86640 27.23385 27.07354
4 TRUE 30.23634 30.08196 29.82247 30.83014 29.89069 30.85647 30.35587
5 TRUE 29.67061 29.63911 30.47663 29.49626 29.03767 34.89597 29.40631
6 TRUE 28.78746 29.09507 28.78952 29.07021 29.23209 29.15205 29.35342
Y8 Y9 Y10 Y11 Y12 Y13 Y14 Y15
1 24.19155 24.51601 23.82976 24.42456 24.74398 24.16186 24.90987 23.72162
2 34.84045 34.32856 34.91147 34.68092 34.12130 34.50493 34.46141 34.74068
3 27.26387 26.71366 26.83111 27.45051 26.40405 26.94825 28.17712 20.57758
4 30.58367 30.20824 30.53127 29.83715 30.42494 30.38994 34.79615 30.61854
5 29.72799 30.05320 29.58885 29.44340 30.03936 29.97763 29.86399 29.38854
6 28.86347 29.74368 29.26966 29.21980 28.87466 29.27927 29.10779 22.02403
Z_Y1 Z_Y2 Z_Y3 Z_Y4 Z_Y5 Z_Y6 Z_Y7
1 -0.09536193 -0.1180897 -0.69281657 -0.4941398 3.4913351 0.2435431 -0.0347031
2 0.17781271 0.2841269 0.23490724 0.2505284 -3.5812350 0.3211058 0.2412329
3 -0.02130445 0.2554765 0.33742035 0.4817344 0.1129682 0.3243875 0.2321502
4 -0.32934778 -0.4581996 -0.67479138 0.1662774 -0.6178468 0.1882581 -0.2295793
5 -0.27163296 -0.2943647 0.31000233 -0.3974485 -0.7283684 3.4990495 -0.4623547
6 0.07019154 0.2363051 0.07130623 0.2228842 0.3103027 0.2670795 0.3758210
Z_Y8 Z_Y9 Z_Y10 Z_Y11 Z_Y12 Z_Y13
1 -0.35152563 -0.179555502 -0.54327887 -0.2280248 -0.058723561 -0.36726276
2 0.44508032 0.087860070 0.49463759 0.3337512 -0.056771661 0.21093976
3 0.34165996 0.025087160 0.09266224 0.4490483 -0.153051687 0.16006371
4 -0.03943888 -0.352801128 -0.08317492 -0.6625415 -0.171926255 -0.20114157
5 -0.23022628 0.004451239 -0.33062996 -0.4355922 -0.005537515 -0.05008534
6 0.11123749 0.586569339 0.33058802 0.3036643 0.117280868 0.33577680
Z_Y14 Z_Y15 SD LB UB
1 0.02919807 -0.60059406 1.886725 -3 3
2 0.18056790 0.37545581 1.432988 -3 3
3 0.86711471 -3.50541721 1.738017 -3 3
4 3.47659061 -0.01033688 1.198078 -3 3
5 -0.13208754 -0.47517499 1.385786 -3 3
6 0.24317622 -3.58218329 1.851790 -3 3
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12
1 TRUE 24.67 24.6 23.5 23.9 31.44 25.3 24.79 24.2 24.5 23.83 24.4 24.74
2 TRUE 34.46 34.6 34.5 34.6 29.07 34.7 34.55 34.8 34.3 34.91 34.7 34.12
3 TRUE 26.63 27.1 27.3 27.5 26.87 27.2 27.07 27.3 26.7 26.83 27.5 26.40
4 TRUE 30.24 30.1 29.8 30.8 29.89 30.9 30.36 30.6 30.2 30.53 29.8 30.42
5 TRUE 29.67 29.6 30.5 29.5 29.04 34.9 29.41 29.7 30.1 29.59 29.4 30.04
6 TRUE 28.79 29.1 28.8 29.1 29.23 29.2 29.35 28.9 29.7 29.27 29.2 28.87
7 TRUE 30.95 31.0 25.6 31.3 31.46 30.9 31.60 31.6 31.6 31.97 31.7 31.27
8 TRUE 9.25 11.1 10.6 30.5 6.32 13.2 6.16 5.3 15.7 7.14 12.8 1.27
9 TRUE 34.78 35.3 29.8 35.3 35.13 34.8 35.08 34.9 35.0 34.86 35.0 34.45
10 TRUE 23.85 23.3 24.2 24.7 24.27 24.4 24.54 22.9 23.7 24.56 14.6 22.84
Y13 Y14 Y15 Z_Y1 Z_Y2 Z_Y3 Z_Y4 Z_Y5 Z_Y6 Z_Y7
1 24.16 24.9 23.72 -0.0954 -0.1181 -0.6928 -0.494 3.491 0.2435 -0.0347
2 34.50 34.5 34.74 0.1778 0.2841 0.2349 0.251 -3.581 0.3211 0.2412
3 26.95 28.2 20.58 -0.0213 0.2555 0.3374 0.482 0.113 0.3244 0.2322
4 30.39 34.8 30.62 -0.3293 -0.4582 -0.6748 0.166 -0.618 0.1883 -0.2296
5 29.98 29.9 29.39 -0.2716 -0.2944 0.3100 -0.397 -0.728 3.4990 -0.4624
6 29.28 29.1 22.02 0.0702 0.2363 0.0713 0.223 0.310 0.2671 0.3758
7 31.25 31.7 31.58 -0.0557 -0.0454 -3.5420 0.191 0.277 -0.0617 0.3666
8 8.64 8.5 9.47 -0.1724 0.1042 0.0302 3.048 -0.617 0.4194 -0.6416
9 34.97 34.8 34.76 0.1331 0.5088 -3.5666 0.544 0.395 0.1560 0.3552
10 24.75 24.9 23.60 0.1766 -0.0354 0.2982 0.519 0.343 0.3792 0.4463
Z_Y8 Z_Y9 Z_Y10 Z_Y11 Z_Y12 Z_Y13 Z_Y14 Z_Y15 SD LB UB
1 -0.3515 -0.17956 -0.5433 -0.228 -0.05872 -0.3673 0.0292 -0.6006 1.89 -3 3
2 0.4451 0.08786 0.4946 0.334 -0.05677 0.2109 0.1806 0.3755 1.43 -3 3
3 0.3417 0.02509 0.0927 0.449 -0.15305 0.1601 0.8671 -3.5054 1.74 -3 3
4 -0.0394 -0.35280 -0.0832 -0.663 -0.17193 -0.2011 3.4766 -0.0103 1.20 -3 3
5 -0.2302 0.00445 -0.3306 -0.436 -0.00554 -0.0501 -0.1321 -0.4752 1.39 -3 3
6 0.1112 0.58657 0.3306 0.304 0.11728 0.3358 0.2432 -3.5822 1.85 -3 3
7 0.3382 0.39092 0.6139 0.440 0.15551 0.1410 0.4337 0.3573 1.53 -3 3
8 -0.7720 0.80280 -0.4930 0.365 -1.38238 -0.2656 -0.2863 -0.1388 6.59 -3 3
9 0.2449 0.31062 0.1934 0.273 -0.11471 0.2725 0.1750 0.1199 1.34 -3 3
10 -0.2023 0.12034 0.4547 -3.490 -0.22569 0.5324 0.6063 0.0777 2.53 -3 3
Z_Y1 Z_Y2 Z_Y3 Z_Y4 Z_Y5 Z_Y6
-0.32934778 -0.45819964 -0.67479138 0.16627737 -0.61784679 0.18825811
Z_Y7 Z_Y8 Z_Y9 Z_Y10 Z_Y11 Z_Y12
-0.22957934 -0.03943888 -0.35280113 -0.08317492 -0.66254152 -0.17192626
Z_Y13 Z_Y14 Z_Y15
-0.20114157 3.47659061 -0.01033688
Please wait...
Call:
odm(x = toy, method = "grubbs", alpha = 0.01)
Outlier Detection for Multi-replicative High-throughput Data
Method: Grubbs test ( grubbs )
Number of Observations: 200
Number of Outliers: 12
Transformation: log2
Head of the Output Results
Outlier G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15
1 TRUE . . . . 3.491 . . . . . . . . . .
2 TRUE . . . . -3.581 . . . . . . . . . .
3 TRUE . . . . . . . . . . . . . 2.434 -3.505
4 TRUE . . . . . . . . . . . . . 3.477 .
5 TRUE . . . . . 3.499 . . . . . . . . .
6 TRUE . . . . . . . . 2.378 . . . . . -3.582
To see the full information for the result, use a command, 'output(your_object_name)'.
Call:
odm(x = toy, method = "grubbs", alpha = 0.01)
Outlier Detection for Multi-replicative High-throughput Data
Method: Grubbs test ( grubbs )
Number of Observations: 200
Number of Outliers: 12
Transformation: log2
Head of the Input Data
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
1 2.68e+07 2.60e+07 1.23e+07 1.59e+07 2.92e+09 4.17e+07 2.90e+07 1.92e+07
2 2.36e+10 2.62e+10 2.50e+10 2.54e+10 5.64e+08 2.72e+10 2.51e+10 3.08e+10
3 1.04e+08 1.45e+08 1.60e+08 1.91e+08 1.22e+08 1.58e+08 1.41e+08 1.61e+08
4 1.26e+09 1.14e+09 9.49e+08 1.91e+09 9.95e+08 1.94e+09 1.37e+09 1.61e+09
5 8.55e+08 8.36e+08 1.49e+09 7.57e+08 5.51e+08 3.20e+10 7.12e+08 8.89e+08
6 4.63e+08 5.73e+08 4.64e+08 5.64e+08 6.31e+08 5.97e+08 6.86e+08 4.88e+08
Y9 Y10 Y11 Y12 Y13 Y14 Y15
1 2.40e+07 1.49e+07 2.25e+07 2.81e+07 1.88e+07 3.15e+07 1.38e+07
2 2.16e+10 3.23e+10 2.75e+10 1.87e+10 2.44e+10 2.37e+10 2.87e+10
3 1.10e+08 1.19e+08 1.83e+08 8.88e+07 1.29e+08 3.03e+08 1.56e+06
4 1.24e+09 1.55e+09 9.59e+08 1.44e+09 1.41e+09 2.98e+10 1.65e+09
5 1.11e+09 8.07e+08 7.30e+08 1.10e+09 1.06e+09 9.77e+08 7.03e+08
6 8.99e+08 6.47e+08 6.25e+08 4.92e+08 6.52e+08 5.79e+08 4.26e+06
To see the full information of the input dataset, use a command, 'input(your_object_name)'.
Head of the Output Results
Outlier G1 G2 G3 G4 G5 G6 G7 G8 G9
1 TRUE . . . . 3.49133507967136 . . . .
2 TRUE . . . . -3.58123496282741 . . . .
3 TRUE . . . . . . . . .
4 TRUE . . . . . . . . .
5 TRUE . . . . . 3.49904951444859 . . .
6 TRUE . . . . . . . . 2.37810886505246
G10 G11 G12 G13 G14 G15
1 . . . . . .
2 . . . . . .
3 . . . . 2.4344103286513 -3.50541721005009
4 . . . . 3.47659061386896 .
5 . . . . . .
6 . . . . . -3.58218328957321
To see the full information for the result, use a command, 'output(your_object_name)'.
Head of the Peptide Numbers detected to be an Outlier
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
To see the full information for the candidate outliers, use a command, 'outliers(your_object_name)'.
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7
1 TRUE 24.67486 24.63198 23.54762 23.92247 31.44197 25.31428 24.78930
2 TRUE 34.45746 34.60981 34.53928 34.56166 29.07079 34.66280 34.54834
3 TRUE 26.63303 27.11408 27.25650 27.50732 26.86640 27.23385 27.07354
4 TRUE 30.23634 30.08196 29.82247 30.83014 29.89069 30.85647 30.35587
5 TRUE 29.67061 29.63911 30.47663 29.49626 29.03767 34.89597 29.40631
6 TRUE 28.78746 29.09507 28.78952 29.07021 29.23209 29.15205 29.35342
Y8 Y9 Y10 Y11 Y12 Y13 Y14 Y15 G1 G2
1 24.19155 24.51601 23.82976 24.42456 24.74398 24.16186 24.90987 23.72162 NA NA
2 34.84045 34.32856 34.91147 34.68092 34.12130 34.50493 34.46141 34.74068 NA NA
3 27.26387 26.71366 26.83111 27.45051 26.40405 26.94825 28.17712 20.57758 NA NA
4 30.58367 30.20824 30.53127 29.83715 30.42494 30.38994 34.79615 30.61854 NA NA
5 29.72799 30.05320 29.58885 29.44340 30.03936 29.97763 29.86399 29.38854 NA NA
6 28.86347 29.74368 29.26966 29.21980 28.87466 29.27927 29.10779 22.02403 NA NA
G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15
1 NA NA 3.491335 NA NA NA NA NA NA NA NA NA NA
2 NA NA -3.581235 NA NA NA NA NA NA NA NA NA NA
3 NA NA NA NA NA NA NA NA NA NA NA 2.434410 -3.505417
4 NA NA NA NA NA NA NA NA NA NA NA 3.476591 NA
5 NA NA NA 3.49905 NA NA NA NA NA NA NA NA NA
6 NA NA NA NA NA NA 2.378109 NA NA NA NA NA -3.582183
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12
1 TRUE 24.67 24.6 23.5 23.9 31.44 25.3 24.79 24.2 24.5 23.83 24.4 24.74
2 TRUE 34.46 34.6 34.5 34.6 29.07 34.7 34.55 34.8 34.3 34.91 34.7 34.12
3 TRUE 26.63 27.1 27.3 27.5 26.87 27.2 27.07 27.3 26.7 26.83 27.5 26.40
4 TRUE 30.24 30.1 29.8 30.8 29.89 30.9 30.36 30.6 30.2 30.53 29.8 30.42
5 TRUE 29.67 29.6 30.5 29.5 29.04 34.9 29.41 29.7 30.1 29.59 29.4 30.04
6 TRUE 28.79 29.1 28.8 29.1 29.23 29.2 29.35 28.9 29.7 29.27 29.2 28.87
7 TRUE 30.95 31.0 25.6 31.3 31.46 30.9 31.60 31.6 31.6 31.97 31.7 31.27
8 TRUE 9.25 11.1 10.6 30.5 6.32 13.2 6.16 5.3 15.7 7.14 12.8 1.27
9 TRUE 34.78 35.3 29.8 35.3 35.13 34.8 35.08 34.9 35.0 34.86 35.0 34.45
10 TRUE 23.85 23.3 24.2 24.7 24.27 24.4 24.54 22.9 23.7 24.56 14.6 22.84
41 TRUE 22.00 23.2 22.1 22.0 20.96 21.8 22.56 19.0 22.0 20.95 22.3 21.40
186 TRUE 13.81 12.9 13.1 13.1 12.38 13.9 13.78 14.2 12.7 14.01 14.0 16.56
Y13 Y14 Y15 G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12
1 24.16 24.9 23.72 NA NA NA NA 3.49 NA NA NA NA NA NA NA
2 34.50 34.5 34.74 NA NA NA NA -3.58 NA NA NA NA NA NA NA
3 26.95 28.2 20.58 NA NA NA NA NA NA NA NA NA NA NA NA
4 30.39 34.8 30.62 NA NA NA NA NA NA NA NA NA NA NA NA
5 29.98 29.9 29.39 NA NA NA NA NA 3.5 NA NA NA NA NA NA
6 29.28 29.1 22.02 NA NA NA NA NA NA NA NA 2.38 NA NA NA
7 31.25 31.7 31.58 NA NA -3.54 NA NA NA NA NA NA NA NA NA
8 8.64 8.5 9.47 NA NA NA 3.05 NA NA NA NA NA NA NA NA
9 34.97 34.8 34.76 NA NA -3.57 NA NA NA NA NA NA NA NA NA
10 24.75 24.9 23.60 NA NA NA NA NA NA NA NA NA NA -3.49 NA
41 21.56 22.3 22.03 NA NA NA NA NA NA NA -2.88 NA NA NA NA
186 12.65 12.9 13.45 NA NA NA NA NA NA NA NA NA NA NA 2.96
G13 G14 G15
1 NA NA NA
2 NA NA NA
3 NA 2.43 -3.51
4 NA 3.48 NA
5 NA NA NA
6 NA NA -3.58
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
41 NA NA NA
186 NA NA NA
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
24.67486 24.63198 23.54762 23.92247 31.44197 25.31428 24.78930 24.19155
Y9 Y10 Y11 Y12 Y13 Y14 Y15
24.51601 23.82976 24.42456 24.74398 24.16186 24.90987 23.72162
Please wait...
Call:
odm(x = toy, k = 3, method = "iqr")
Outlier Detection for Multi-replicative High-throughput Data
Method: Interquartile range (IQR) criterion ( iqr )
k: 3 for k * IQR
Number of Observations: 200
Number of Outliers: 15
Transformation: log2
Head of the Output Results
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14
1 TRUE 24.7 24.6 23.5 23.9 31.4 25.3 24.8 24.2 24.5 23.8 24.4 24.7 24.2 24.9
2 TRUE 34.5 34.6 34.5 34.6 29.1 34.7 34.5 34.8 34.3 34.9 34.7 34.1 34.5 34.5
3 TRUE 26.6 27.1 27.3 27.5 26.9 27.2 27.1 27.3 26.7 26.8 27.5 26.4 26.9 28.2
4 TRUE 30.2 30.1 29.8 30.8 29.9 30.9 30.4 30.6 30.2 30.5 29.8 30.4 30.4 34.8
5 TRUE 29.7 29.6 30.5 29.5 29.0 34.9 29.4 29.7 30.1 29.6 29.4 30.0 30.0 29.9
6 TRUE 28.8 29.1 28.8 29.1 29.2 29.2 29.4 28.9 29.7 29.3 29.2 28.9 29.3 29.1
Y15 Q1 Q2 Q3 LB UB
1 23.7 24.0 24.5 24.8 21.9 26.9
2 34.7 34.5 34.5 34.7 33.8 35.3
3 20.6 26.8 27.1 27.3 25.3 28.7
4 30.6 30.1 30.4 30.6 28.8 32.0
5 29.4 29.5 29.7 30.0 27.9 31.6
6 22.0 28.9 29.1 29.3 27.7 30.4
To see the full information for the result, use a command, 'output(your_object_name)'.
Call:
odm(x = toy, k = 3, method = "iqr")
Outlier Detection for Multi-replicative High-throughput Data
Method: Interquartile range (IQR) criterion ( iqr )
k: 3 for k * IQR
Number of Observations: 200
Number of Outliers: 15
Transformation: log2
Head of the Input Data
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
1 2.68e+07 2.60e+07 1.23e+07 1.59e+07 2.92e+09 4.17e+07 2.90e+07 1.92e+07
2 2.36e+10 2.62e+10 2.50e+10 2.54e+10 5.64e+08 2.72e+10 2.51e+10 3.08e+10
3 1.04e+08 1.45e+08 1.60e+08 1.91e+08 1.22e+08 1.58e+08 1.41e+08 1.61e+08
4 1.26e+09 1.14e+09 9.49e+08 1.91e+09 9.95e+08 1.94e+09 1.37e+09 1.61e+09
5 8.55e+08 8.36e+08 1.49e+09 7.57e+08 5.51e+08 3.20e+10 7.12e+08 8.89e+08
6 4.63e+08 5.73e+08 4.64e+08 5.64e+08 6.31e+08 5.97e+08 6.86e+08 4.88e+08
Y9 Y10 Y11 Y12 Y13 Y14 Y15
1 2.40e+07 1.49e+07 2.25e+07 2.81e+07 1.88e+07 3.15e+07 1.38e+07
2 2.16e+10 3.23e+10 2.75e+10 1.87e+10 2.44e+10 2.37e+10 2.87e+10
3 1.10e+08 1.19e+08 1.83e+08 8.88e+07 1.29e+08 3.03e+08 1.56e+06
4 1.24e+09 1.55e+09 9.59e+08 1.44e+09 1.41e+09 2.98e+10 1.65e+09
5 1.11e+09 8.07e+08 7.30e+08 1.10e+09 1.06e+09 9.77e+08 7.03e+08
6 8.99e+08 6.47e+08 6.25e+08 4.92e+08 6.52e+08 5.79e+08 4.26e+06
To see the full information of the input dataset, use a command, 'input(your_object_name)'.
Head of the Output Results
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14
1 TRUE 24.7 24.6 23.5 23.9 31.4 25.3 24.8 24.2 24.5 23.8 24.4 24.7 24.2 24.9
2 TRUE 34.5 34.6 34.5 34.6 29.1 34.7 34.5 34.8 34.3 34.9 34.7 34.1 34.5 34.5
3 TRUE 26.6 27.1 27.3 27.5 26.9 27.2 27.1 27.3 26.7 26.8 27.5 26.4 26.9 28.2
4 TRUE 30.2 30.1 29.8 30.8 29.9 30.9 30.4 30.6 30.2 30.5 29.8 30.4 30.4 34.8
5 TRUE 29.7 29.6 30.5 29.5 29.0 34.9 29.4 29.7 30.1 29.6 29.4 30.0 30.0 29.9
6 TRUE 28.8 29.1 28.8 29.1 29.2 29.2 29.4 28.9 29.7 29.3 29.2 28.9 29.3 29.1
Y15 Q1 Q2 Q3 LB UB
1 23.7 24.0 24.5 24.8 21.9 26.9
2 34.7 34.5 34.5 34.7 33.8 35.3
3 20.6 26.8 27.1 27.3 25.3 28.7
4 30.6 30.1 30.4 30.6 28.8 32.0
5 29.4 29.5 29.7 30.0 27.9 31.6
6 22.0 28.9 29.1 29.3 27.7 30.4
To see the full information for the result, use a command, 'output(your_object_name)'.
Head of the Peptide Numbers detected to be an Outlier
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
To see the full information for the candidate outliers, use a command, 'outliers(your_object_name)'.
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11
1 TRUE 24.67 24.63 23.55 23.92 31.44 25.314 24.79 24.19 24.52 23.83 24.4
2 TRUE 34.46 34.61 34.54 34.56 29.07 34.663 34.55 34.84 34.33 34.91 34.7
3 TRUE 26.63 27.11 27.26 27.51 26.87 27.234 27.07 27.26 26.71 26.83 27.5
4 TRUE 30.24 30.08 29.82 30.83 29.89 30.856 30.36 30.58 30.21 30.53 29.8
5 TRUE 29.67 29.64 30.48 29.50 29.04 34.896 29.41 29.73 30.05 29.59 29.4
6 TRUE 28.79 29.10 28.79 29.07 29.23 29.152 29.35 28.86 29.74 29.27 29.2
7 TRUE 30.95 30.97 25.62 31.33 31.46 30.941 31.60 31.55 31.63 31.97 31.7
8 TRUE 9.25 11.07 10.59 30.48 6.32 13.151 6.16 5.30 15.68 7.14 12.8
9 TRUE 34.78 35.28 29.83 35.33 35.13 34.812 35.08 34.93 35.02 34.86 35.0
10 TRUE 23.85 23.32 24.16 24.72 24.27 24.367 24.54 22.90 23.71 24.56 14.6
12 TRUE 11.56 15.49 14.75 16.13 12.22 16.280 14.70 14.86 13.10 15.10 14.7
41 TRUE 22.00 23.18 22.09 22.00 20.96 21.847 22.56 19.01 22.04 20.95 22.3
68 TRUE 5.97 6.35 3.75 6.98 5.21 0.839 10.62 5.75 5.86 6.46 10.9
86 TRUE 10.63 10.93 8.20 17.06 11.93 11.930 11.20 10.57 18.94 8.64 12.0
111 TRUE 31.83 31.95 31.77 31.84 31.84 32.083 31.85 31.26 32.05 32.30 31.8
Y12 Y13 Y14 Y15 Q1 Q2 Q3 LB UB
1 24.74 24.16 24.91 23.72 24.04 24.52 24.77 21.87 26.9
2 34.12 34.50 34.46 34.74 34.46 34.55 34.67 33.82 35.3
3 26.40 26.95 28.18 20.58 26.77 27.07 27.26 25.31 28.7
4 30.42 30.39 34.80 30.62 30.15 30.39 30.60 28.78 32.0
5 30.04 29.98 29.86 29.39 29.47 29.67 30.01 27.85 31.6
6 28.87 29.28 29.11 22.02 28.87 29.11 29.25 27.72 30.4
7 31.27 31.25 31.70 31.58 31.11 31.46 31.61 29.59 33.1
8 1.27 8.64 8.50 9.47 6.73 9.25 11.93 -8.89 27.5
9 34.45 34.97 34.84 34.76 34.80 34.93 35.05 34.04 35.8
10 22.84 24.75 24.94 23.60 23.46 24.16 24.55 20.20 27.8
12 15.25 15.32 14.93 16.77 14.69 14.93 15.41 12.55 17.6
41 21.40 21.56 22.32 22.03 21.48 22.00 22.20 19.32 24.4
68 5.85 7.10 7.79 7.29 5.80 6.35 7.19 1.63 11.4
86 12.90 12.21 15.05 10.37 10.60 11.93 12.55 4.73 18.4
111 31.62 31.85 31.36 31.94 31.79 31.84 31.94 31.31 32.4
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
24.67486 24.63198 23.54762 23.92247 31.44197 25.31428 24.78930 24.19155
Y9 Y10 Y11 Y12 Y13 Y14 Y15
24.51601 23.82976 24.42456 24.74398 24.16186 24.90987 23.72162
Please wait...
Call:
odm(x = toy, k = 3, method = "siqr")
Outlier Detection for Multi-replicative High-throughput Data
Method: Semiinterquartile range (SIQR) criterion ( siqr )
k: 3 for 2k * SIQR
Number of Observations: 200
Number of Outliers: 28
Transformation: log2
Head of the Output Results
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14
1 TRUE 24.7 24.6 23.5 23.9 31.4 25.3 24.8 24.2 24.5 23.8 24.4 24.7 24.2 24.9
2 TRUE 34.5 34.6 34.5 34.6 29.1 34.7 34.5 34.8 34.3 34.9 34.7 34.1 34.5 34.5
3 TRUE 26.6 27.1 27.3 27.5 26.9 27.2 27.1 27.3 26.7 26.8 27.5 26.4 26.9 28.2
4 TRUE 30.2 30.1 29.8 30.8 29.9 30.9 30.4 30.6 30.2 30.5 29.8 30.4 30.4 34.8
5 TRUE 29.7 29.6 30.5 29.5 29.0 34.9 29.4 29.7 30.1 29.6 29.4 30.0 30.0 29.9
6 TRUE 28.8 29.1 28.8 29.1 29.2 29.2 29.4 28.9 29.7 29.3 29.2 28.9 29.3 29.1
Y15 Q1 Q2 Q3 LB UB
1 23.7 24.0 24.5 24.8 21.2 26.3
2 34.7 34.5 34.5 34.7 33.9 35.4
3 20.6 26.8 27.1 27.3 25.0 28.4
4 30.6 30.1 30.4 30.6 28.7 31.9
5 29.4 29.5 29.7 30.0 28.3 32.0
6 22.0 28.9 29.1 29.3 27.4 30.1
To see the full information for the result, use a command, 'output(your_object_name)'.
Call:
odm(x = toy, k = 3, method = "siqr")
Outlier Detection for Multi-replicative High-throughput Data
Method: Semiinterquartile range (SIQR) criterion ( siqr )
k: 3 for 2k * SIQR
Number of Observations: 200
Number of Outliers: 28
Transformation: log2
Head of the Input Data
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
1 2.68e+07 2.60e+07 1.23e+07 1.59e+07 2.92e+09 4.17e+07 2.90e+07 1.92e+07
2 2.36e+10 2.62e+10 2.50e+10 2.54e+10 5.64e+08 2.72e+10 2.51e+10 3.08e+10
3 1.04e+08 1.45e+08 1.60e+08 1.91e+08 1.22e+08 1.58e+08 1.41e+08 1.61e+08
4 1.26e+09 1.14e+09 9.49e+08 1.91e+09 9.95e+08 1.94e+09 1.37e+09 1.61e+09
5 8.55e+08 8.36e+08 1.49e+09 7.57e+08 5.51e+08 3.20e+10 7.12e+08 8.89e+08
6 4.63e+08 5.73e+08 4.64e+08 5.64e+08 6.31e+08 5.97e+08 6.86e+08 4.88e+08
Y9 Y10 Y11 Y12 Y13 Y14 Y15
1 2.40e+07 1.49e+07 2.25e+07 2.81e+07 1.88e+07 3.15e+07 1.38e+07
2 2.16e+10 3.23e+10 2.75e+10 1.87e+10 2.44e+10 2.37e+10 2.87e+10
3 1.10e+08 1.19e+08 1.83e+08 8.88e+07 1.29e+08 3.03e+08 1.56e+06
4 1.24e+09 1.55e+09 9.59e+08 1.44e+09 1.41e+09 2.98e+10 1.65e+09
5 1.11e+09 8.07e+08 7.30e+08 1.10e+09 1.06e+09 9.77e+08 7.03e+08
6 8.99e+08 6.47e+08 6.25e+08 4.92e+08 6.52e+08 5.79e+08 4.26e+06
To see the full information of the input dataset, use a command, 'input(your_object_name)'.
Head of the Output Results
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14
1 TRUE 24.7 24.6 23.5 23.9 31.4 25.3 24.8 24.2 24.5 23.8 24.4 24.7 24.2 24.9
2 TRUE 34.5 34.6 34.5 34.6 29.1 34.7 34.5 34.8 34.3 34.9 34.7 34.1 34.5 34.5
3 TRUE 26.6 27.1 27.3 27.5 26.9 27.2 27.1 27.3 26.7 26.8 27.5 26.4 26.9 28.2
4 TRUE 30.2 30.1 29.8 30.8 29.9 30.9 30.4 30.6 30.2 30.5 29.8 30.4 30.4 34.8
5 TRUE 29.7 29.6 30.5 29.5 29.0 34.9 29.4 29.7 30.1 29.6 29.4 30.0 30.0 29.9
6 TRUE 28.8 29.1 28.8 29.1 29.2 29.2 29.4 28.9 29.7 29.3 29.2 28.9 29.3 29.1
Y15 Q1 Q2 Q3 LB UB
1 23.7 24.0 24.5 24.8 21.2 26.3
2 34.7 34.5 34.5 34.7 33.9 35.4
3 20.6 26.8 27.1 27.3 25.0 28.4
4 30.6 30.1 30.4 30.6 28.7 31.9
5 29.4 29.5 29.7 30.0 28.3 32.0
6 22.0 28.9 29.1 29.3 27.4 30.1
To see the full information for the result, use a command, 'output(your_object_name)'.
Head of the Peptide Numbers detected to be an Outlier
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
To see the full information for the candidate outliers, use a command, 'outliers(your_object_name)'.
Outlier Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11
1 TRUE 24.67 24.63 23.55 23.92 31.44 25.314 24.79 24.19 24.52 23.83 24.42
2 TRUE 34.46 34.61 34.54 34.56 29.07 34.663 34.55 34.84 34.33 34.91 34.68
3 TRUE 26.63 27.11 27.26 27.51 26.87 27.234 27.07 27.26 26.71 26.83 27.45
4 TRUE 30.24 30.08 29.82 30.83 29.89 30.856 30.36 30.58 30.21 30.53 29.84
5 TRUE 29.67 29.64 30.48 29.50 29.04 34.896 29.41 29.73 30.05 29.59 29.44
6 TRUE 28.79 29.10 28.79 29.07 29.23 29.152 29.35 28.86 29.74 29.27 29.22
7 TRUE 30.95 30.97 25.62 31.33 31.46 30.941 31.60 31.55 31.63 31.97 31.71
8 TRUE 9.25 11.07 10.59 30.48 6.32 13.151 6.16 5.30 15.68 7.14 12.79
9 TRUE 34.78 35.28 29.83 35.33 35.13 34.812 35.08 34.93 35.02 34.86 34.97
10 TRUE 23.85 23.32 24.16 24.72 24.27 24.367 24.54 22.90 23.71 24.56 14.57
12 TRUE 11.56 15.49 14.75 16.13 12.22 16.280 14.70 14.86 13.10 15.10 14.69
24 TRUE 18.74 17.09 17.28 18.03 17.83 20.621 17.82 16.30 17.85 16.21 17.36
45 TRUE 34.56 34.49 34.69 34.53 34.73 34.467 34.68 34.70 34.55 34.54 35.06
48 TRUE 9.05 12.25 6.75 2.14 7.67 7.889 6.39 7.28 11.16 7.58 5.73
63 TRUE 22.74 23.47 22.97 23.08 22.85 23.069 23.27 23.16 23.02 23.74 22.44
68 TRUE 5.97 6.35 3.75 6.98 5.21 0.839 10.62 5.75 5.86 6.46 10.91
86 TRUE 10.63 10.93 8.20 17.06 11.93 11.930 11.20 10.57 18.94 8.64 11.97
98 TRUE 13.77 12.59 10.14 13.31 10.43 15.095 17.20 13.27 11.18 13.19 12.03
111 TRUE 31.83 31.95 31.77 31.84 31.84 32.083 31.85 31.26 32.05 32.30 31.80
113 TRUE 13.43 14.88 16.36 9.20 14.60 12.970 17.41 11.97 16.13 12.54 13.66
121 TRUE 25.03 24.85 24.44 25.01 24.65 24.560 24.50 25.00 25.27 24.31 24.93
145 TRUE 21.46 20.66 20.71 20.51 20.71 21.889 20.56 21.91 20.65 19.91 18.62
162 TRUE 34.97 34.72 34.67 34.79 35.11 34.797 34.73 34.83 34.50 34.66 34.81
164 TRUE 10.26 10.38 10.36 14.40 8.03 10.042 10.43 10.89 5.70 12.30 10.54
190 TRUE 25.70 26.63 25.22 26.79 25.92 25.649 25.67 25.60 25.37 26.53 24.08
191 TRUE 20.38 21.16 20.41 19.31 20.08 20.243 20.47 19.89 21.67 20.25 20.38
196 TRUE 30.55 30.84 31.43 30.71 30.68 30.456 30.66 30.72 31.35 30.75 30.38
198 TRUE 23.33 23.15 23.79 23.13 22.21 23.336 24.70 22.77 23.17 23.41 22.66
Y12 Y13 Y14 Y15 Q1 Q2 Q3 LB UB
1 24.74 24.16 24.91 23.72 24.04 24.52 24.77 21.1991 26.3
2 34.12 34.50 34.46 34.74 34.46 34.55 34.67 33.9260 35.4
3 26.40 26.95 28.18 20.58 26.77 27.07 27.26 24.9654 28.4
4 30.42 30.39 34.80 30.62 30.15 30.39 30.60 28.6761 31.9
5 30.04 29.98 29.86 29.39 29.47 29.67 30.01 28.2651 32.0
6 28.87 29.28 29.11 22.02 28.87 29.11 29.25 27.4367 30.1
7 31.27 31.25 31.70 31.58 31.11 31.46 31.61 29.0027 32.5
8 1.27 8.64 8.50 9.47 6.73 9.25 11.93 -8.4133 28.0
9 34.45 34.97 34.84 34.76 34.80 34.93 35.05 33.9912 35.8
10 22.84 24.75 24.94 23.60 23.46 24.16 24.55 19.2536 26.9
12 15.25 15.32 14.93 16.77 14.69 14.93 15.41 13.3113 18.3
24 16.25 17.61 16.45 18.71 16.77 17.61 17.94 11.7393 20.0
45 34.50 34.83 35.02 34.73 34.53 34.68 34.73 33.6432 35.0
48 12.97 10.55 9.05 -2.68 6.57 7.67 9.80 -0.0349 22.6
63 23.49 23.57 21.76 23.39 22.91 23.08 23.43 21.9175 25.6
68 5.85 7.10 7.79 7.29 5.80 6.35 7.19 2.5320 12.3
86 12.90 12.21 15.05 10.37 10.60 11.93 12.55 2.6157 16.3
98 13.06 12.57 12.75 14.00 12.30 13.06 13.54 7.7271 16.4
111 31.62 31.85 31.36 31.94 31.79 31.84 31.94 31.4638 32.6
113 13.08 17.39 17.32 13.57 13.02 13.66 16.24 9.2084 31.8
121 26.17 25.00 24.57 25.61 24.57 24.93 25.02 22.3813 25.6
145 20.61 20.03 18.91 19.15 19.97 20.61 20.71 16.1097 21.3
162 34.81 34.84 34.81 34.78 34.72 34.80 34.82 34.2801 35.0
164 10.81 13.19 15.10 14.18 10.31 10.54 12.75 8.9661 26.0
190 25.15 26.06 26.35 26.34 25.48 25.70 26.34 24.1835 30.2
191 19.90 21.32 18.94 19.91 19.90 20.25 20.44 17.8440 21.6
196 30.42 30.11 30.52 30.60 30.49 30.66 30.74 29.4349 31.2
198 23.86 22.33 23.27 22.77 22.77 23.17 23.37 20.3838 24.6
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
24.67486 24.63198 23.54762 23.92247 31.44197 25.31428 24.78930 24.19155
Y9 Y10 Y11 Y12 Y13 Y14 Y15
24.51601 23.82976 24.42456 24.74398 24.16186 24.90987 23.72162
N1 N2 N3
1 3148600 3351500 2025000
4 162200 19480 293430
5 6170400 6917700 8854800
6 54839000 95569000 50611000
7 5056100 355700 5169900
8 35878900 52117200 61609400
Please wait...
Call:
odm(x = lcms3[, 1:2], k = 3, method = "pair")
Outlier Detection for Multi-replicative High-throughput Data
Method: Pairwise OutlierD algoirthm ( pair )
Reg: linear quantile regression ( linear )
k: 3 for k * IQR
Upper Quantile: 0.75
Lower Quantile: 0.25
Number of Observations: 922
Number of Outliers: 16
Centering: TRUE
Transformation: log2
Head of the Output Results
Outlier N1 N2 A M Q1 Q3 LB UB
1 FALSE 21.6 21.7 21.6 -0.328 -0.589 0.589 -4.12 4.12
2 FALSE 17.3 14.2 15.8 2.740 -0.886 0.886 -6.20 6.20
3 FALSE 22.6 22.7 22.6 -0.390 -0.537 0.537 -3.76 3.76
4 FALSE 25.7 26.5 26.1 -0.979 -0.361 0.361 -2.53 2.53
5 FALSE 22.3 18.4 20.4 3.574 -0.654 0.654 -4.58 4.58
6 FALSE 25.1 25.6 25.4 -0.726 -0.399 0.399 -2.79 2.79
To see the full information for the result, use a command, 'output(your_object_name)'.
Call:
odm(x = lcms3[, 1:2], k = 3, method = "pair")
Outlier Detection for Multi-replicative High-throughput Data
Method: Pairwise OutlierD algoirthm ( pair )
Regression: linear quantile regression ( linear )
k: 3 for k * IQR
Upper Quantile: 0.75
Lower Quantile: 0.25
Number of Observations: 922
Number of Outliers: 16
Centering: TRUE
Transformation: log2
Head of the Input Data
N1 N2
1 3148600 3351500
2 162200 19480
3 6170400 6917700
4 54839000 95569000
5 5056100 355700
6 35878900 52117200
To see the full information of the input dataset, use a command, 'input(your_object_name)'.
Head of the Output Results
Outlier N1 N2 A M Q1 Q3 LB UB
1 FALSE 21.6 21.7 21.6 -0.328 -0.589 0.589 -4.12 4.12
2 FALSE 17.3 14.2 15.8 2.740 -0.886 0.886 -6.20 6.20
3 FALSE 22.6 22.7 22.6 -0.390 -0.537 0.537 -3.76 3.76
4 FALSE 25.7 26.5 26.1 -0.979 -0.361 0.361 -2.53 2.53
5 FALSE 22.3 18.4 20.4 3.574 -0.654 0.654 -4.58 4.58
6 FALSE 25.1 25.6 25.4 -0.726 -0.399 0.399 -2.79 2.79
To see the full information for the result, use a command, 'output(your_object_name)'.
Head of the Peptide Numbers detected to be an Outlier
[1] "66" "94" "145" "236" "319" "324" "413" "448" "458" "460"
To see the full information for the candidate outliers, use a command, 'outliers(your_object_name)'.
Outlier N1 N2 A M Q1 Q3 LB
1 FALSE 21.58628 21.67638 21.63133 -0.3284366 -0.5887466 0.5887466 -4.121226
2 FALSE 17.30741 14.24971 15.77856 2.7402724 -0.8863551 0.8863551 -6.204486
3 FALSE 22.55693 22.72186 22.63940 -0.3896453 -0.5374871 0.5374871 -3.762409
4 FALSE 25.70870 26.51004 26.10937 -0.9791633 -0.3610417 0.3610417 -2.527292
5 FALSE 22.26959 18.44030 20.35495 3.5737027 -0.6536495 0.6536495 -4.575546
6 FALSE 25.09663 25.63526 25.36594 -0.7264936 -0.3988443 0.3988443 -2.791910
UB
1 4.121226
2 6.204486
3 3.762409
4 2.527292
5 4.575546
6 2.791910
Outlier N1 N2 A M Q1 Q3 LB UB
66 TRUE 32.3 32.8 32.6 -0.600 -0.0317 0.0317 -0.222 0.222
94 TRUE 23.7 18.2 21.0 5.220 -0.6227 0.6227 -4.359 4.359
145 TRUE 23.9 19.1 21.5 4.635 -0.5960 0.5960 -4.172 4.172
236 TRUE 24.9 19.8 22.4 4.878 -0.5505 0.5505 -3.854 3.854
319 TRUE 15.0 22.2 18.6 -7.473 -0.7407 0.7407 -5.185 5.185
324 TRUE 26.9 21.7 24.3 4.946 -0.4531 0.4531 -3.172 3.172
413 TRUE 19.7 24.2 21.9 -4.742 -0.5740 0.5740 -4.018 4.018
448 TRUE 31.7 32.1 31.9 -0.542 -0.0679 0.0679 -0.475 0.475
458 TRUE 25.3 28.6 26.9 -3.522 -0.3186 0.3186 -2.230 2.230
460 TRUE 28.7 17.5 23.1 10.955 -0.5122 0.5122 -3.586 3.586
541 TRUE 25.9 20.4 23.2 5.319 -0.5111 0.5111 -3.578 3.578
661 TRUE 15.5 22.7 19.1 -7.436 -0.7188 0.7188 -5.032 5.032
751 TRUE 18.0 23.5 20.7 -5.773 -0.6343 0.6343 -4.440 4.440
782 TRUE 28.1 24.9 26.5 3.029 -0.3419 0.3419 -2.393 2.393
796 TRUE 25.3 17.0 21.1 8.116 -0.6134 0.6134 -4.294 4.294
906 TRUE 30.6 20.2 25.4 10.297 -0.3975 0.3975 -2.783 2.783
Please wait...
Call:
odm(x = lcms3, k = 3, method = "proj", center = TRUE)
Outlier Detection for Multi-replicative High-throughput Data
Method: Projection-based OutlierD algorithm ( proj )
Reg: linear quantile regression ( linear )
k: 3 for k * IQR
Upper Quantile: 0.75
Lower Quantile: 0.25
Number of Observations: 922
Number of Outliers: 26
Transformation: log2
Head of the Output Results
Outlier N1 N2 N3 A M Q1 Q3 LB UB
1 FALSE -0.453 -0.0893 -1.3298 -1.08 0.900 0.364 1.113 -1.88 3.36
2 FALSE -4.732 -7.5160 -4.1167 -9.44 2.586 0.512 1.579 -2.69 4.78
3 FALSE 0.517 0.9562 0.7987 1.31 0.318 0.322 0.980 -1.65 2.95
4 FALSE 3.669 4.7443 3.3136 6.77 1.070 0.226 0.675 -1.12 2.02
5 FALSE 0.230 -3.3254 0.0224 -1.77 2.827 0.376 1.151 -1.95 3.48
6 FALSE 3.057 3.8696 3.5973 6.07 0.602 0.238 0.714 -1.19 2.14
To see the full information for the result, use a command, 'output(your_object_name)'.
Call:
odm(x = lcms3, k = 3, method = "proj", center = TRUE)
Outlier Detection for Multi-replicative High-throughput Data
Method: Projection-based OutlierD algorithm ( proj )
Regression: linear quantile regression ( linear )
k: 3 for k * IQR
Upper Quantile: 0.75
Lower Quantile: 0.25
Number of Observations: 922
Number of Outliers: 26
Transformation: log2
Head of the Input Data
N1 N2 N3
1 3148600 3351500 2025000
2 162200 19480 293430
3 6170400 6917700 8854800
4 54839000 95569000 50611000
5 5056100 355700 5169900
6 35878900 52117200 61609400
To see the full information of the input dataset, use a command, 'input(your_object_name)'.
Head of the Output Results
Outlier N1 N2 N3 A M Q1 Q3 LB UB
1 FALSE -0.453 -0.0893 -1.3298 -1.08 0.900 0.364 1.113 -1.88 3.36
2 FALSE -4.732 -7.5160 -4.1167 -9.44 2.586 0.512 1.579 -2.69 4.78
3 FALSE 0.517 0.9562 0.7987 1.31 0.318 0.322 0.980 -1.65 2.95
4 FALSE 3.669 4.7443 3.3136 6.77 1.070 0.226 0.675 -1.12 2.02
5 FALSE 0.230 -3.3254 0.0224 -1.77 2.827 0.376 1.151 -1.95 3.48
6 FALSE 3.057 3.8696 3.5973 6.07 0.602 0.238 0.714 -1.19 2.14
To see the full information for the result, use a command, 'output(your_object_name)'.
Head of the Peptide Numbers detected to be an Outlier
[1] "18" "50" "66" "94" "120" "145" "211" "236" "319" "324"
To see the full information for the candidate outliers, use a command, 'outliers(your_object_name)'.
Outlier N1 N2 N3 A M Q1 Q3 LB UB
18 TRUE -3.111 -1.6157 8.619 2.2484 9.029 0.3055 0.9273 -1.56008 2.7928
50 TRUE 8.614 8.6603 1.027 10.5631 6.219 0.1588 0.4635 -0.75551 1.3778
66 TRUE 10.293 11.0763 10.426 18.3552 0.647 0.0213 0.0289 -0.00151 0.0518
94 TRUE 1.658 -3.5350 1.733 -0.0713 4.271 0.3464 1.0567 -1.78455 3.1876
120 TRUE -2.042 1.4419 2.160 0.8942 3.180 0.3293 1.0028 -1.69113 3.0233
145 TRUE 1.887 -2.7151 1.595 0.4533 3.643 0.3371 1.0274 -1.73379 3.0983
211 TRUE 7.261 7.1830 5.221 11.3525 1.639 0.1449 0.4195 -0.67913 1.2435
236 TRUE 2.896 -1.9359 2.815 2.1906 3.906 0.3065 0.9305 -1.56568 2.8027
319 TRUE -6.994 0.4746 -2.498 -5.2217 5.302 0.4372 1.3439 -2.28293 4.0641
324 TRUE 4.833 -0.0407 4.944 5.6329 4.010 0.2458 0.7385 -1.23259 2.2169
380 TRUE 3.174 -0.0174 3.841 4.0475 2.906 0.2737 0.8270 -1.38600 2.4867
413 TRUE -2.371 2.4102 -0.352 -0.1910 3.394 0.3485 1.0634 -1.79614 3.2080
440 TRUE 1.556 -2.3869 1.491 0.3905 3.192 0.3382 1.0309 -1.73986 3.1090
448 TRUE 9.614 10.3301 9.703 17.1155 0.602 0.0432 0.0981 -0.12147 0.2627
458 TRUE 3.227 6.8557 3.069 7.5848 3.050 0.2113 0.6297 -1.04371 1.8847
460 TRUE 6.683 -4.2165 5.870 4.8380 8.573 0.2598 0.7829 -1.30951 2.3522
470 TRUE -1.971 -5.5501 0.641 -3.9630 4.404 0.4150 1.2737 -2.16114 3.8499
477 TRUE -4.025 0.3565 0.231 -1.9931 3.523 0.3803 1.1639 -1.97051 3.5147
541 TRUE 3.887 -1.3753 0.346 1.6610 3.790 0.3158 0.9601 -1.61693 2.8928
661 TRUE -6.547 0.8896 1.591 -2.3615 6.373 0.3868 1.1844 -2.00617 3.5774
747 TRUE -0.665 -5.1162 -0.190 -3.4368 3.853 0.4057 1.2444 -2.11022 3.7604
751 TRUE -4.066 1.7310 -2.152 -2.6033 4.169 0.3910 1.1979 -2.02956 3.6185
782 TRUE 6.047 3.1195 5.993 8.7592 2.344 0.1906 0.5642 -0.93007 1.6848
796 TRUE 3.288 -4.7988 3.450 1.1383 6.667 0.3250 0.9892 -1.66750 2.9818
844 TRUE 4.788 5.3965 -4.599 3.2194 7.927 0.2883 0.8731 -1.46613 2.6276
906 TRUE 8.595 -1.6157 -3.343 2.1187 9.120 0.3077 0.9345 -1.57263 2.8149
N1 N2 N3
-3.110796 -1.615662 8.619094
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