@article{Benjamini2001,
abstract = {Benjamini and Hochberg suggest that the false discovery rate may be the appropriate error rate to control in many applied multiple testing prob-lems. A simple procedure was given there as an FDR controlling procedure for independent test statistics and was shown to be much more powerful than comparable procedures which control the traditional familywise error rate. We prove that this same procedure also controls the false discovery rate when the test statistics have positive regression dependency on each of the test statistics corresponding to the true null hypotheses. This condition for positive dependency is general enough to cover many problems of prac-tical interest, including the comparisons of many treatments with a single control, multivariate normal test statistics with positive correlation matrix and multivariate t. Furthermore, the test statistics may be discrete, and the tested hypotheses composite without posing special difficulties. For all other forms of dependency, a simple conservative modification of the proce-dure controls the false discovery rate. Thus the range of problems for which a procedure with proven FDR control can be offered is greatly increased.},
archivePrefix = {arXiv},
arxivId = {0801.1095},
author = {Benjamini, Yoav and Yekutieli, Daniel},
doi = {10.1214/aos/1013699998},
eprint = {0801.1095},
file = {::},
isbn = {0090-5364},
issn = {00905364},
journal = {Annals of Statistics},
keywords = {Discrete test statistics,FDR,Hochberg's procedure,MTP2 densities,Multiple comparisons procedures,Multiple endpoints many-to-one comparisons,Positive regression dependency,Simes' equality,Unidimensional latent variables},
number = {4},
pages = {1165--1188},
pmid = {18298808},
title = {{The control of the false discovery rate in multiple testing under dependency}},
url = {https://projecteuclid.org/euclid.aos/1013699998},
volume = {29},
year = {2001}
}
@article{Dobin2013,
abstract = {MOTIVATION: Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases.$\backslash$n$\backslash$nRESULTS: To align our large ({\textgreater}80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of {\textgreater}50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90{\%} success rate, corroborating the high precision of the STAR mapping strategy.$\backslash$n$\backslash$nAVAILABILITY AND IMPLEMENTATION: STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.},
author = {Dobin, Alexander and Davis, Carrie A. and Schlesinger, Felix and Drenkow, Jorg and Zaleski, Chris and Jha, Sonali and Batut, Philippe and Chaisson, Mark and Gingeras, Thomas R.},
doi = {10.1093/bioinformatics/bts635},
isbn = {1367-4811 (Electronic)$\backslash$n1367-4803 (Linking)},
issn = {13674803},
journal = {Bioinformatics},
number = {1},
pages = {15--21},
pmid = {23104886},
title = {{STAR: Ultrafast universal RNA-seq aligner}},
url = {https://doi.org/10.1093/bioinformatics/bts635},
volume = {29},
year = {2013}
}
@article{Kremer2017,
abstract = {Across a variety of Mendelian disorders, B50–75{\%} of patients do not receive a genetic diagnosis by exome sequencing indicating disease-causing variants in non-coding regions. Although genome sequencing in principle reveals all genetic variants, their sizeable number and poorer annotation make prioritization challenging. Here, we demonstrate the power of transcriptome sequencing to molecularly diagnose 10{\%} (5 of 48) of mitochondriopathy patients and identify candidate genes for the remainder. We find a median of one aberrantly expressed gene, five aberrant splicing events and six mono-allelically expressed rare variants in patient-derived fibroblasts and establish disease-causing roles for each kind. Private exons often arise from cryptic splice sites providing an important clue for variant prioritization. One such event is found in the complex I assembly factor TIMMDC1 establishing a novel disease-associated gene. In conclusion, our study expands the diagnostic tools for detecting non-exonic variants and provides examples of intronic loss-of-function variants with pathological relevance.},
author = {Kremer, Laura S and Bader, Daniel M and Mertes, Christian and Kopajtich, Robert and Pichler, Garwin and Iuso, Arcangela and Haack, Tobias B and Graf, Elisabeth and Schwarzmayr, Thomas and Terrile, Caterina and Koňař{\'{i}}kov{\'{a}}, Eli{\v{s}}ka and Repp, Birgit and Kastenm{\"{u}}ller, Gabi and Adamski, Jerzy and Lichtner, Peter and Leonhardt, Christoph and Funalot, Benoit and Donati, Alice and Tiranti, Valeria and Lombes, Anne and Jardel, Claude and Gl{\"{a}}ser, Dieter and Taylor, Robert W and Ghezzi, Daniele and Mayr, Johannes A and R{\"{o}}tig, Agnes and Freisinger, Peter and Distelmaier, Felix and Strom, Tim M and Meitinger, Thomas and Gagneur, Julien and Prokisch, Holger},
doi = {10.1038/ncomms15824},
file = {::},
issn = {2041-1723},
journal = {Nature Communications},
month = {jun},
pages = {15824},
title = {{Genetic diagnosis of Mendelian disorders via RNA sequencing}},
url = {https://www.nature.com/articles/ncomms15824.pdf},
volume = {8},
year = {2017}
}
@article{Love2014,
abstract = {In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at $\backslash$n http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html$\backslash$n $\backslash$n .},
author = {Love, Michael I and Huber, Wolfgang and Anders, Simon},
doi = {10.1186/s13059-014-0550-8},
file = {::},
isbn = {1465-6906},
issn = {1474-760X},
journal = {Genome Biology},
month = {dec},
number = {12},
pages = {550},
pmid = {25516281},
title = {{Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2}},
url = {http://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8},
volume = {15},
year = {2014}
}
@article{Robinson2010,
abstract = {SUMMARY It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. AVAILABILITY The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).},
author = {Robinson, Mark D and McCarthy, Davis J. and Smyth, Gordon K},
doi = {10.1093/bioinformatics/btp616},
file = {::},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
month = {jan},
number = {1},
pages = {139--40},
pmid = {19910308},
title = {{edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.}},
url = {https://doi.org/10.1093/bioinformatics/btp616},
volume = {26},
year = {2010}
}
@article{Stegle2012,
abstract = {We present PEER (probabilistic estimation of expression residuals), a software package implementing statistical models that improve the sensitivity and interpretability of genetic associations in population-scale expression data. This approach builds on factor analysis methods that infer broad variance components in the measurements. PEER takes as input transcript profiles and covariates from a set of individuals, and then outputs hidden factors that explain much of the expression variability. Optionally, these factors can be interpreted as pathway or transcription factor activations by providing prior information about which genes are involved in the pathway or targeted by the factor. The inferred factors are used in genetic association analyses. First, they are treated as additional covariates, and are included in the model to increase detection power for mapping expression traits. Second, they are analyzed as phenotypes themselves to understand the causes of global expression variability. PEER extends previous related surrogate variable models and can be implemented within hours on a desktop computer.},
author = {Stegle, Oliver and Parts, Leopold and Piipari, Matias and Winn, John and Durbin, Richard},
doi = {10.1038/nprot.2011.457},
file = {:Users/gagneurlab/Library/Application Support/Mendeley Desktop/Downloaded/Stegle et al. - 2012 - Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of g.pdf:pdf},
isbn = {1750-2799 (Electronic)$\backslash$r1750-2799 (Linking)},
issn = {17542189},
journal = {Nature Protocols},
number = {3},
pages = {500--507},
pmid = {22343431},
title = {{Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses}},
volume = {7},
year = {2012}
}
@article{Gavish2013,
 abstract = {We consider recovery of low-rank matrices from noisy data by hard thresholding of singular values, where singular values below a prescribed threshold {\$}\lambda{\$} are set to 0. We study the asymptotic MSE in a framework where the matrix size is large compared to the rank of the matrix to be recovered, and the signal-to-noise ratio of the low-rank piece stays constant. The AMSE-optimal choice of hard threshold, in the case of n-by-n matrix in noise level $\backslash$sigma, is simply {\$}(4/\sqrt{3}) \sqrt{n}$\backslash$sigma $\backslash$approx 2.309 \sqrt{n}$\backslash$sigma{\$} when {\$}$\backslash$sigma{\$} is known, or simply {\$}2.858$\backslash$cdot y{\_}{med}{\$} when {\$}$\backslash$sigma{\$} is unknown, where {\$}y{\_}{med}{\$} is the median empirical singular value. For nonsquare $m$ by $n$ matrices with {\$}m $\backslash$neq n{\$}, these thresholding coefficients are replaced with different provided constants. In our asymptotic framework, this thresholding rule adapts to unknown rank and to unknown noise level in an optimal manner: it is always better than hard thresholding at any other value, no matter what the matrix is that we are trying to recover, and is always better than ideal Truncated SVD (TSVD), which truncates at the true rank of the low-rank matrix we are trying to recover. Hard thresholding at the recommended value to recover an n-by-n matrix of rank r guarantees an AMSE at most {\$}3nr$\backslash$sigma{\^{}}2{\$}. In comparison, the guarantee provided by TSVD is {\$}5nr$\backslash$sigma{\^{}}2{\$}, the guarantee provided by optimally tuned singular value soft thresholding is {\$}6nr$\backslash$sigma{\^{}}2{\$}, and the best guarantee achievable by any shrinkage of the data singular values is {\$}2nr$\backslash$sigma{\^{}}2{\$}. Empirical evidence shows that these AMSE properties of the {\$}4/\sqrt{3}{\$} thresholding rule remain valid even for relatively small n, and that performance improvement over TSVD and other shrinkage rules is substantial, turning it into the practical hard threshold of choice.},
 author = {Gavish, Matan and Donoho, David L.},
 date = {25.05.2013},
 title = {{The Optimal Hard Threshold for Singular Values is 4/sqrt(3)}},
 url = {http://arxiv.org/pdf/1305.5870},
 file = {Gavish, Donoho 25.05.2013 - The Optimal Hard Threshold:Attachments/Gavish, Donoho 25.05.2013 - The Optimal Hard Threshold.pdf:application/pdf}
}