Statistical Evaluation of Labeled Comparative Profiling Proteomics Experiments Using Permutation Test

Nguyen, Hien D.; McLachlan, Geoffrey J.; Hill, Michelle M.

doi:10.1007/978-1-4939-6740-7_9

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…The same as the above two methods, the permutation test method [10] is also used FWER standard. The difference is that it can well deal with the correlation between different tests.…”

Section: Multiple Correction Methodsmentioning

confidence: 99%

Analysis of Corrections Methods in Genome-Wide Association Studies

Zheng¹,

Zhuo²

2017

Proceedings of the 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017)

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Abstract. Genome wide association study (GWAS) can directly study the relationship between human behavior and genotype, which provides a new way for researchers to explore the genetic basis of human behavior from the whole genome level. GWAS involves a large number of sites and behavior of the association test, so we must use multiple correction to control the overall false. Although there are a variety of correction methods to choose from, but the applicability of different correction methods in GWAS research is still lack of systematic research, which makes the choice of multiple correction methods in GWAS lack of theoretical and empirical basis. GWAS commonly used in the correction method is based on the family-wise error rate (FWER) standard Bonferroni correction method, Holm regression adjustment method, permutation test method and false detection rate (FDR) standard BH method. In this paper, the principle and process of the 4 kinds of multiple correction methods are described in detail. A simulation method of GWAS data is presented. The results show that the first 3 methods based on FWER are very small, they are the most stringent control of false, but the number of bits of the real association is significantly lower than the FDR based BH method. Independent data, the BH reported by the SNPs method has the highest rate of interpretation, that is, compared with other methods, the BH method better balance the false and hit. The BH method can be used to correct the results in future studies.

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“…The same as the above two methods, the permutation test method [10] is also used FWER standard. The difference is that it can well deal with the correlation between different tests.…”

Section: Multiple Correction Methodsmentioning

confidence: 99%

Analysis of Corrections Methods in Genome-Wide Association Studies

Zheng¹,

Zhuo²

2017

Proceedings of the 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017)

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“…The second class of methods is moderated testing statistics. Permutation is one prominent example which requires no assumption of data normality or independence of the data (as normal hypothesis testing approaches do), which has been proved to offer robust and sensitive detection of protein changes (Nguyen et al, ). Significance Analysis of Microarray (SAM) determines whether a protein is significantly changed by calculating a score contrasting the measured level of changes via repeated measurement of the given protein (Tusher et al, ), which proves to perform better than FC or conventional t ‐test methods (Roxas & Li, ).…”

Section: Important Considerations For Ms1‐based Quantification In Larmentioning

confidence: 99%

MS1 ion current‐based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

Wang¹,

Shen²,

Rasam

et al. 2019

Mass Spectrometry Reviews

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The rapidly‐advancing field of pharmaceutical and clinical research calls for systematic, molecular‐level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large‐cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large‐cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false‐positive discovery of altered proteins when sample size increases. MS1 ion current‐based methods, which have become an important class of label‐free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1‐based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high‐resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post‐feature‐generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1‐based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

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Statistical Evaluation of Labeled Comparative Profiling Proteomics Experiments Using Permutation Test

Cited by 2 publications

References 10 publications

Analysis of Corrections Methods in Genome-Wide Association Studies

Analysis of Corrections Methods in Genome-Wide Association Studies

MS1 ion current‐based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

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