Letter to the EditorComment on “Analysis of Microarray-Identified Genes andMicroRNAs Associated with Idiopathic Pulmonary Fibrosis”
Chenyu Li , Shujuan Wang, Lin Che, Xianghua Wang, and Yan Xu
Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China
Correspondence should be addressed to Yan Xu; [email protected]
Received 12 June 2017; Accepted 11 January 2018; Published 27 September 2018
Academic Editor: Hermann Gram
Copyright © 2018 Chenyu Li et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.
Recently, we read the article by Dr. Fan and colleagues,“Analysis of Microarray-Identified Genes and MicroRNAsAssociated with Idiopathic Pulmonary Fibrosis” [1] whichappeared in the 14 May 2017 issue of Mediators ofInflammation. Since the results of the article are veryuseful, we collected original data and used different bioin-formatics methods to reanalyse the raw data; however, weget different results compared with those of the article,and we think that the author’s methods in bioinformaticsanalysis are inappropriate.
We noticed that the author did not perform qualityassessment for the microarray; therefore, we utilized Nor-malized Unscaled Standard Errors (NUSE) [2]. NUSE is amore sensitive measure than Relative Log Expression(RLE). If the analysts are skeptical about the quality of a chipin the RLE charts, that suspicion can easily be determinedwhen using the NUSE diagram. The calculation of NUSE isactually very simple, it is the standard deviation of a chiprelative to the standard deviation of the entire group. If thewhole group of chips is reliable, their standard deviation willbe very close and usually around 1. Therefore, if there is aproblem with the quality of the chip, it will significantlydeviate from 1, which will affect the NUSE values of the other
chips in the opposite direction. Of course, there is a veryextreme situation, that is, when most chips have qualityproblems but their standard deviation is relatively close,which also appears that the NUSE value of the qualified chipswill be significantly deviated from 1.
We collected raw data from GSE32537 and GSE32538from the GEO database and used the R tool to performquality assessment of the microarray. Since the platform ofGSE32537 is the Affymetrix Human Gene 1.0 ST Array, weused the oligo [3] package for quality assessment. Figure 1shows that GSM806284’s NUSE is higher than 1.05,obviously, which means that GSM806284 is an unqualifiedsample and cannot be used for further analysis. ForGSE32538 (Affymetrix Multispecies miRNA-1 Array), weused the AffyPLM [2] package for quality assessment.Figure 2 shows that GSM806429’s NUSE is around 1.05which means that GSM806284 is an unqualified sample andalso cannot be used for further analysis. In summary, qualityassessment is a very important part of bioinformaticsanalysis. Performing the quality assessment and obtainingmore accurate and convincing results of differentiallyexpressed gene analysis is the basis for further analysis suchas GO enrichment analysis and KEGG pathway analysis.
HindawiMediators of InflammationVolume 2018, Article ID 4789035, 2 pageshttps://doi.org/10.1155/2018/4789035
Conflicts of Interest
The authors declare that there is no conflict of interestregarding the publication of this paper.
References
[1] L. Fan, X. Yu, Z. Huang et al., “Analysis of microarray-identifiedgenes and microRNAs associated with idiopathic pulmonaryfibrosis,” Mediators of Inflammation, vol. 2017, Article ID1804240, 9 pages, 2017.
[2] S. Heber and B. Sick, “Quality assessment of AffymetrixGeneChip data,” OMICS, vol. 10, no. 3, pp. 358–368, 2006.
[3] B. S. Carvalho and R. A. Irizarry, “A framework for oligonucle-otide microarray preprocessing,” Bioinformatics, vol. 26, no. 19,pp. 2363–2367, 2010.
NUSE
0.95
GSM
806259
GSM
806260
GSM
806261
GSM
806262
GSM
806263
GSM
806264
GSM
806265
GSM
806266
GSM
806267
GSM
806268
GSM
806269
GSM
806270
GSM
806271
GSM
806272
GSM
806273
GSM
806274
GSM
806275
GSM
806276
GSM
806277
GSM
806278
GSM
806279
GSM
806280
GSM
806281
GSM
806282
GSM
806283
GSM
806284
GSM
806285
GSM
806286
GSM
806287
GSM
806288
GSM
806289
GSM
806290
GSM
806291
GSM
806292
GSM
806293
GSM
806294
GSM
806295
GSM
806296
GSM
806297
GSM
806298
GSM
806299
GSM
806300
GSM
806301
GSM
806302
GSM
806303
GSM
806304
GSM
806305
GSM
806306
GSM
806307
GSM
806308
GSM
806309
GSM
806310
GSM
806311
GSM
806312
GSM
806313
1.00
1.05
1.10
Figure 1: The NUSE plot of GSE32537.
GSM806429
1.10
1.05NUSE
1.00
Figure 2: The NUSE plot of GSE32538.
2 Mediators of Inflammation
Stem Cells International
Hindawiwww.hindawi.com Volume 2018
Hindawiwww.hindawi.com Volume 2018
MEDIATORSINFLAMMATION
of
EndocrinologyInternational Journal of
Hindawiwww.hindawi.com Volume 2018
Hindawiwww.hindawi.com Volume 2018
Disease Markers
Hindawiwww.hindawi.com Volume 2018
BioMed Research International
OncologyJournal of
Hindawiwww.hindawi.com Volume 2013
Hindawiwww.hindawi.com Volume 2018
Oxidative Medicine and Cellular Longevity
Hindawiwww.hindawi.com Volume 2018
PPAR Research
Hindawi Publishing Corporation http://www.hindawi.com Volume 2013Hindawiwww.hindawi.com
The Scientific World Journal
Volume 2018
Immunology ResearchHindawiwww.hindawi.com Volume 2018
Journal of
ObesityJournal of
Hindawiwww.hindawi.com Volume 2018
Hindawiwww.hindawi.com Volume 2018
Computational and Mathematical Methods in Medicine
Hindawiwww.hindawi.com Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwww.hindawi.com Volume 2018
Diabetes ResearchJournal of
Hindawiwww.hindawi.com Volume 2018
Hindawiwww.hindawi.com Volume 2018
Research and TreatmentAIDS
Hindawiwww.hindawi.com Volume 2018
Gastroenterology Research and Practice
Hindawiwww.hindawi.com Volume 2018
Parkinson’s Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwww.hindawi.com
Submit your manuscripts atwww.hindawi.com