Software Open Access

A biplot correlation range for group-wise metabolite selection in mass spectrometry

Park, Younja; Roede, James R.; Kong, Taewoon; Jones, Dean P; Lee, Kichun

Background

Analytic methods are available to acquire extensive metabolic information in a cost-effective manner for personalized medicine, yet disease risk and diagnosis mostly rely upon individual biomarkers based on statistical principles of false discovery rate and correlation. Due to functional redundancies and multiple layers of regulation in complex biologic systems, individual biomarkers, while useful, are inherently limited in disease characterization.  Data reduction and discriminant analysis tools such as principal component analysis (PCA), partial least squares (PLS), or orthogonal PLS (O-PLS) provide approaches to separate the metabolic phenotypes, but do not offer a statistical basis for selection of group-wise metabolites as contributors to metabolic phenotypes.

Methods

We present a multivariate statistical approach termed ‘biplot correlation range (BCR)’ to provide the group-wise selection of metabolic markers contributing to metabolic phenotypes.

Results

Using a simulated multiple-layer system that often arises in complex biologic systems, we show the feasibility and superiority of the proposed approach in comparison of existing approaches based on false discovery rate and correlation. To demonstrate the proposed method in a real-life dataset, we used LC-MS based metabolomics to determine spectrum of metabolites present in liver mitochondria from wild-type (WT) mice and thioredoxin-2 transgenic (TG) mice. We select discriminatory variables in terms of increased score in the direction of class identity using BCR. The results show that BCR provides means to identify metabolites contributing to class separation in a manner that can complement a statistical method by false discovery rate in complex data analysis for predictive health and personalized medicine.

Files (2.2 MB)
Name Size
AE-WTvsTGmitochondria-males.xlsx
md5:b0d44ab78713af7f38aeaff29203dc9f
1.8 MB Download
AnalysisFilteredVariables.m
md5:5ce5457eb17d89558601786fd40e3742
5.9 kB Download
arrow.m
md5:a9fba6cb870e440f70d88c6a4849cb63
55.3 kB Download
cellwrite.m
md5:c493535d638d203ae0ca1f65ac1a4314
1.3 kB Download
centerData.m
md5:093e9f5e52364ec6c1e1d96679523b1e
151 Bytes Download
color_line.m
md5:e8a64a0ab185d1801bef2a37abb198d1
1.2 kB Download
computeAngleCI.m
md5:fb5772e5a8ce2bd2a23e4b29fbca0406
475 Bytes Download
computeLogisticRegressionPerformance.m
md5:d791ab80b5da63976876df17548f89de
877 Bytes Download
doFDR.m
md5:0ee890384bc52e6aae01eb84e27aacf0
1.4 kB Download
doFDR_logistic.m
md5:47a7d8b1e1235ae38648ba065f69ba47
1.1 kB Download
doFDRNest.m
md5:d75ca3fbdfe46bfd85ef792247dc15dc
1.4 kB Download
doFirstLabelMatch.m
md5:e3f487330f3f30bae3fdd1f919ae5d8c
1.5 kB Download
doMatchForGraph.m
md5:a199e03200f8abce82d481ff40e3b936
1.4 kB Download
doOPCInitialCheckup.m
md5:862a8899508e251ff33d19adbfd663e9
3.4 kB Download
doOPCreadData.m
md5:603663312d6dcb3df0f237ecf3099d1a
2.9 kB Download
doOPLS.m
md5:23c1ae3d04cfaae20286cdb8562f6f74
122 Bytes Download
doOPLSmatchSetup.m
md5:5385163b4baa15d56e2c0a5498768cdf
26.5 kB Download
doPCInitialCheckup.m
md5:7e0f041a50e9f8a419ac57db95e1aaba
2.1 kB Download
doPCmatchInternal.m
md5:e7b1079e5c83a064b5ba7201b36f77ad
39.0 kB Download
doPCmatchSetup.m
md5:93ac969ade81e65cdbadba33a059e069
63.8 kB Download
doPCreadData.m
md5:630443d5a3b7969103f9333dd5782f9b
15.4 kB Download
doReFDR.m
md5:596d856138cbe2f6dbdd24bb7144590a
1.4 kB Download
doSaveThreeLabelsMatch.m
md5:b135608ec718a18674a8df9c84e6a1a3
1.1 kB Download
doSaveTwoLabelsMatch.m
md5:aa07e51303aa875be0d4a2b5951e529a
802 Bytes Download
dosc.m
md5:223bbbf7fd6e77eb32cdcd661b884a0d
2.1 kB Download
dosc_pred.m
md5:b4c6cde30fce4cbf90ef6faff630c689
979 Bytes Download
doSecondLabelMatch.m
md5:925b0198d6f3ab35dc7143e62d3f3dd6
554 Bytes Download
doSTOCSY.m
md5:eb789b3043a92472d538f61413b8f899
2.0 kB Download
doThirdLabelMatch.m
md5:36e0673b632a956475fe0351a9fc2494
176 Bytes Download
drawMultiLabels2D.m
md5:424ea8907f40397e7b4fbd4faa64162e
980 Bytes Download
ellipse.m
md5:9c4242ab53e6a4bd35da99aad363673f
3.4 kB Download
Eucldistance.m
md5:7106cb052236170ba8e51666f1c68e3d
1.3 kB Download
fdr_bh.m
md5:55cb4ea8661780c3c5f9df3939d6c165
6.6 kB Download
gen2Dsample.m
md5:a46d34a3e5f99ad0e37fb47995021251
577 Bytes Download
gen2Dsample3.m
md5:f74d96b8a37dca1e4738b02e5bb582bf
758 Bytes Download
gen3Dsample.m
md5:c029fee1cf38bc2100e59b195589d468
622 Bytes Download
gen3Dsample3.m
md5:8c53bfa8d3aa3f089179cdd535022585
878 Bytes Download
gennetvar.m
md5:30ce1a9ca3bb8667fb04b16004f347b8
307 Bytes Download
getFDRSig.m
md5:de92699bcfb7ade9d9af160b09210867
973 Bytes Download
getIndexMean.m
md5:0a5159a7b3e2ad915dae34780ed68f70
422 Bytes Download
getVarIndexPCMatchNew.m
md5:9bfdd32db4ae19ff978802e464b2ba51
36.3 kB Download
LICENSE.txt
md5:e22b7facfc893ef7e195d7910ccfb42f
36.0 kB Download
myIsField.m
md5:f010761976494907f22f918b38edb9ce
87 Bytes Download
normalizeData.m
md5:04b423f11406c1b71f1a4cf558f59d09
280 Bytes Download
powerStudent.m
md5:75ac31ebcfa6884adca922585ab6a798
3.7 kB Download
README.txt
md5:e9b72a8c2f6d4d6bbe57a2981c0f84ee
4.6 kB Download
rotationmat3D.m
md5:0815ef984b31600d4661003e8cb2d0c9
1.2 kB Download
showresults.m
md5:81fc12c4664db4b7cee112c9670f729e
1.5 kB Download
test_AE_WT.m
md5:40bd72e24348ce46db3711e30d554551
246 Bytes Download
test_simulationTest1.m
md5:f2ae7b01c34cb97c7474b3bc3c047c19
20.1 kB Download
test_simulationTest2.m
md5:264f6b57ba94b732b6dd3b88fc046028
20.3 kB Download
test_simulationTest3.m
md5:e0befbfe9eefb33599102dc97eaa98e1
20.0 kB Download
test_simulationTest4.m
md5:0ef19e5520654e8409b639797408d204
19.9 kB Download
tp_idx_inside1.csv
md5:a32edb84299aa2dd9ec3e27ce7c9b229
287 Bytes Download
tp_idx_inside2.csv
md5:87965b1e21935c4d4ca28604834f4337
308 Bytes Download
45
328
views
downloads
All versions This version
Views 4545
Downloads 328328
Data volume 16.0 MB16.0 MB
Unique views 4141
Unique downloads 8484

Share

Cite as