Interpretation of 'Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes
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Yves A. Lussier | Joanne Berghout | Qike Li | Francesca Vitali | Colleen Kenost | Dillon Aberasturi | Samir Rachid Zaim | Haiquan Li | Helen Hao Zhang | Y. Lussier | J. Berghout | C. Kenost | Haiquan Li | Francesca Vitali | Dillon Aberasturi | Qike Li | H. Zhang | F. Vitali
[1] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[2] M. Stephens,et al. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. , 2008, Genome research.
[3] Margaret J. Robertson,et al. Design and Analysis of Experiments , 2006, Handbook of statistics.
[4] B. Efron. Large-Scale Simultaneous Hypothesis Testing , 2004 .
[5] Qian Wang,et al. GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data , 2012, Bioinform..
[6] John D. Storey,et al. Empirical Bayes Analysis of a Microarray Experiment , 2001 .
[7] Juliana Costa-Silva,et al. RNA-Seq differential expression analysis: An extended review and a software tool , 2017, PloS one.
[8] Joaquín Dopazo,et al. NOIseq: a RNA-seq differential expression method robust for sequencing depth biases , 2012 .
[9] Charity W. Law,et al. voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.
[10] Eric J Topol,et al. Individualized Medicine from Prewomb to Tomb , 2014, Cell.
[11] W. Huber,et al. which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets , 2011 .
[12] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[13] Yves A. Lussier,et al. N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes , 2017, BMC Medical Genomics.
[14] Charlotte Soneson,et al. Bias, robustness and scalability in single-cell differential expression analysis , 2018, Nature Methods.
[15] Samir Rachid Zaim,et al. Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine , 2018 .
[16] Ian T. Foster,et al. ‘N-of-1-pathways’ unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine , 2014, J. Am. Medical Informatics Assoc..
[17] F. Hampel. The Influence Curve and Its Role in Robust Estimation , 1974 .
[18] Peter Craven,et al. Smoothing noisy data with spline functions , 1978 .
[19] Jeff H. Chang,et al. The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq , 2011 .
[20] M. Robinson,et al. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. , 2007, Biostatistics.
[21] Yves A. Lussier,et al. Emergence of pathway-level composite biomarkers from converging gene set signals of heterogeneous transcriptomic responses , 2018, PSB.
[22] N. Schork. Personalized medicine: Time for one-person trials , 2015, Nature.
[23] R. Tibshirani,et al. Using specially designed exponential families for density estimation , 1996 .
[24] Wolfgang Huber,et al. Differential expression of RNA-Seq data at the gene level – the DESeq package , 2012 .
[25] Yves A. Lussier,et al. kMEn: Analyzing noisy and bidirectional transcriptional pathway responses in single subjects , 2017, J. Biomed. Informatics.
[26] N. Laubscher,et al. On Stabilizing the Binomial and Negative Binomial Variances , 1961 .
[27] B. Efron. Size, power and false discovery rates , 2007, 0710.2245.
[28] Jocelyn Kaiser,et al. Obama gives East Room rollout to Precision Medicine Initiative , 2015 .
[29] Yves A. Lussier,et al. Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival , 2015, Bioinform..
[30] B. Efron. Correlation and Large-Scale Simultaneous Significance Testing , 2007 .
[31] Christian P. Robert,et al. Large-scale inference , 2010 .
[32] Joanne Berghout,et al. Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes , 2017, Briefings Bioinform..
[33] Adrian E. Raftery,et al. Normal uniform mixture differential gene expression detection for cDNA microarrays , 2005, BMC Bioinformatics.
[34] Xuegong Zhang,et al. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data , 2010, Bioinform..
[35] Mark D. Robinson,et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..