Adaptive local false discovery rate procedures for highly spiky data and their application RNA sequencing data of yeast SET4 deletion mutants

2021 ◽  
Author(s):  
Mark Louie Ramos ◽  
DoHwan Park ◽  
Johan Lim ◽  
Junyong Park ◽  
Khoa Tran ◽  
...  
Keyword(s):  
False Discovery Rate ◽  
Rna Sequencing ◽  
Deletion Mutants ◽  
Local False Discovery Rate ◽  
Sequencing Data ◽  
False Discovery

scholarly journals Interpretation of ‘Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes

2018 ◽  
Author(s):  
Qike Li ◽  
Samir Rachid Zaim ◽  
Dillon Aberasturi ◽  
Joanne Berghout ◽  
Haiquan Li ◽  
...  
Keyword(s):  
False Discovery Rate ◽  
Rna Sequencing ◽  
Mixture Model ◽  
Rate Control ◽  
Empirical Bayes ◽  
Single Subject ◽  
Local False Discovery Rate ◽  
Simulation Studies ◽  
Extensive Simulation ◽  
False Discovery

AbstractCalculating Differentially Expressed Genes (DEGs) from RNA-sequencing requires replicates to estimate gene-wise variability, infeasible in clinics. By imposing restrictive transcriptome-wide assumptions limiting inferential opportunities of conventional methods (edgeR, NOISeq-sim, DESeq, DEGseq), comparing two conditions without replicates (TCWR) has been proposed, but not evaluated. Under TCWR conditions (e.g., unaffected tissue vs. tumor), differences of transformed expression of the proposed individualized DEG (iDEG) method follow a distribution calculated across a local partition of related transcripts at baseline expression; thereafter the probability of each DEG is estimated by empirical Bayes with local false discovery rate control using a two-group mixture model. In extensive simulation studies of TCWR methods, iDEG and NOISeq are more accurate at 5%<DEGs<20% (precision>90%, recall>75%, false_positive_rate<1%) and 30%<DEGs<40% (precision=recall∼90%), respectively.The proposed iDEG method borrows localized distribution information from the same individual, a strategy that improves accuracy to compare transcriptomes in absence of replicates at low DEGs conditions. http://www.lussiergroup.org/publications/iDEG

scholarly journals Normalization, testing, and false discovery rate estimation for RNA-sequencing data

Biostatistics ◽  
2011 ◽  
Vol 13 (3) ◽  
pp. 523-538 ◽  
Author(s):  
J. Li ◽  
D. M. Witten ◽  
I. M. Johnstone ◽  
R. Tibshirani
Keyword(s):  
False Discovery Rate ◽  
Rna Sequencing ◽  
Sequencing Data ◽  
Rate Estimation ◽  
False Discovery ◽  
False Discovery Rate Estimation

scholarly journals cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate

2012 ◽  
Vol 40 (9) ◽  
pp. e69-e69 ◽  
Author(s):  
Günter Klambauer ◽  
Karin Schwarzbauer ◽  
Andreas Mayr ◽  
Djork-Arné Clevert ◽  
Andreas Mitterecker ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
False Discovery Rate ◽  
Copy Number ◽  
Copy Number Variations ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
False Discovery ◽  
Generation Sequencing

scholarly journals Local false discovery rate estimation using feature reliability in LC/MS metabolomics data

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Elizabeth Y. Chong ◽  
Yijian Huang ◽  
Hao Wu ◽  
Nima Ghasemzadeh ◽  
Karan Uppal ◽  
...  
Keyword(s):  
False Discovery Rate ◽  
Local False Discovery Rate ◽  
Metabolomics Data ◽  
Rate Estimation ◽  
False Discovery ◽  
False Discovery Rate Estimation

scholarly journals SEMI-PARAMETRIC COVARIATE-MODULATED LOCAL FALSE DISCOVERY RATE FOR GENOME-WIDE ASSOCIATION STUDIES

2017 ◽  
Author(s):  
Rong W. Zablocki ◽  
Richard A. Levine ◽  
Andrew J. Schork ◽  
Shujing Xu ◽  
Yunpeng Wang ◽  
...  
Keyword(s):  
False Discovery Rate ◽  
Complex Traits ◽  
Association Studies ◽  
Logistic Function ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Local False Discovery Rate ◽  
B Spline ◽  
False Discovery ◽  
Genome Wide

While genome-wide association studies (GWAS) have discovered thousands of risk loci for heritable disorders, so far even very large meta-analyses have recovered only a fraction of the heritability of most complex traits. Recent work utilizing variance components models has demonstrated that a larger fraction of the heritability of complex phenotypes is captured by the additive effects of SNPs than is evident only in loci surpassing genome-wide significance thresholds, typically set at a Bonferroni-inspired p ≤ 5 x 10-8. Procedures that control false discovery rate can be more powerful, yet these are still under-powered to detect the majority of non-null effects from GWAS. The current work proposes a novel Bayesian semi-parametric two-group mixture model and develops a Markov Chain Monte Carlo (MCMC) algorithm for a covariate-modulated local false discovery rate (cmfdr). The probability of being non-null depends on a set of covariates via a logistic function, and the non-null distribution is approximated as a linear combination of B-spline densities, where the weight of each B-spline density depends on a multinomial function of the covariates. The proposed methods were motivated by work on a large meta-analysis of schizophrenia GWAS performed by the Psychiatric Genetics Consortium (PGC). We show that the new cmfdr model fits the PGC schizophrenia GWAS test statistics well, performing better than our previously proposed parametric gamma model for estimating the non-null density and substantially improving power over usual fdr. Using loci declared significant at cmfdr ≤ 0.20, we perform follow-up pathway analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) homo sapiens pathways database. We demonstrate that the increased yield from the cmfdr model results in an improved ability to test for pathways associated with schizophrenia compared to using those SNPs selected according to usual fdr.

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