@article{Anders2010,
abstract = {High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.},
author = {Anders, Simon and Huber, Wolfgang},
doi = {10.1186/gb-2010-11-10-r106},
file = {:home/andreas/Dokumente/Paper/Anders, Huber - 2010 - Differential expression analysis for sequence count data.pdf:pdf},
issn = {1465-6914},
journal = {Genome Biology},
keywords = {Animals,Binomial Distribution,Chromatin Immunoprecipitation,Chromatin Immunoprecipitation: methods,Computational Biology,Computational Biology: methods,Drosophila,Drosophila: genetics,Gene Expression Profiling,Gene Expression Profiling: methods,Genetic,High-Throughput Nucleotide Sequencing,High-Throughput Nucleotide Sequencing: methods,Linear Models,Models,RNA,RNA: methods,Saccharomyces cerevisiae,Saccharomyces cerevisiae: genetics,Sequence Analysis,Stem Cells,Tissue Culture Techniques},
month = jan,
number = {10},
pages = {R106},
pmid = {20979621},
publisher = {BioMed Central Ltd},
title = {{Differential expression analysis for sequence count data.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3218662\&tool=pmcentrez\&rendertype=abstract},
volume = {11},
year = {2010}
}
@article{Brooks2011,
abstract = {Alternative splicing is generally controlled by proteins that bind directly to regulatory sequence elements and either activate or repress splicing of adjacent splice sites in a target pre-mRNA. Here, we have combined RNAi and mRNA-seq to identify exons that are regulated by Pasilla (PS), the Drosophila melanogaster ortholog of mammalian NOVA1 and NOVA2. We identified 405 splicing events in 323 genes that are significantly affected upon depletion of ps, many of which were annotated as being constitutively spliced. The sequence regions upstream and within PS-repressed exons and downstream from PS-activated exons are enriched for YCAY repeats, and these are consistent with the location of these motifs near NOVA-regulated exons in mammals. Thus, the RNA regulatory map of PS and NOVA1/2 is highly conserved between insects and mammals despite the fact that the target gene orthologs regulated by PS and NOVA1/2 are almost entirely nonoverlapping. This observation suggests that the regulatory codes of individual RNA binding proteins may be nearly immutable, yet the regulatory modules controlled by these proteins are highly evolvable.},
author = {Brooks, Angela N and Yang, Li and Duff, Michael O and Hansen, Kasper D and Park, Jung W and Dudoit, Sandrine and Brenner, Steven E and Graveley, Brenton R},
institution = {Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA.},
journal = {Genome Research},
number = {2},
pages = {193--202},
publisher = {Cold Spring Harbor Laboratory Press},
title = {{Conservation of an RNA regulatory map between Drosophila and mammals}},
url = {http://genome.cshlp.org/cgi/doi/10.1101/gr.108662.110},
volume = {21},
year = {2011}
}
@article{Goeman2007,
abstract = {MOTIVATION: Many statistical tests have been proposed in recent years for analyzing gene expression data in terms of gene sets, usually from Gene Ontology. These methods are based on widely different methodological assumptions. Some approaches test differential expression of each gene set against differential expression of the rest of the genes, whereas others test each gene set on its own. Also, some methods are based on a model in which the genes are the sampling units, whereas others treat the subjects as the sampling units. This article aims to clarify the assumptions behind different approaches and to indicate a preferential methodology of gene set testing. RESULTS: We identify some crucial assumptions which are needed by the majority of methods. P-values derived from methods that use a model which takes the genes as the sampling unit are easily misinterpreted, as they are based on a statistical model that does not resemble the biological experiment actually performed. Furthermore, because these models are based on a crucial and unrealistic independence assumption between genes, the P-values derived from such methods can be wildly anti-conservative, as a simulation experiment shows. We also argue that methods that competitively test each gene set against the rest of the genes create an unnecessary rift between single gene testing and gene set testing.},
author = {Goeman, Jelle J and B\"{u}hlmann, Peter},
doi = {10.1093/bioinformatics/btm051},
file = {:home/andreas/Dokumente/Paper/Bioinformatics-2007-Goeman-980-7.pdf:pdf},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
keywords = {Algorithms,Artifacts,Data Interpretation, Statistical,Databases, Genetic,Gene Expression Profiling,Gene Expression Profiling: methods,Information Storage and Retrieval,Information Storage and Retrieval: methods,Reproducibility of Results,Sensitivity and Specificity},
month = apr,
number = {8},
pages = {980--7},
pmid = {17303618},
title = {{Analyzing gene expression data in terms of gene sets: methodological issues.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17303618},
volume = {23},
year = {2007}
}
@article{Robinson2008,
abstract = {We derive a quantile-adjusted conditional maximum likelihood estimator for the dispersion parameter of the negative binomial distribution and compare its performance, in terms of bias, to various other methods. Our estimation scheme outperforms all other methods in very small samples, typical of those from serial analysis of gene expression studies, the motivating data for this study. The impact of dispersion estimation on hypothesis testing is studied. We derive an "exact" test that outperforms the standard approximate asymptotic tests.},
annote = {
        Estimation through
        
- Conditional Maximum Likelihood (CML)
- Quantile Adjusted CML
- Exact Test to get two-sided p-value},
author = {Robinson, Mark D and Smyth, Gordon K},
doi = {10.1093/biostatistics/kxm030},
file = {:home/andreas/Dokumente/Paper/Robinson, Smyth - 2008 - Small-sample estimation of negative binomial dispersion, with applications to SAGE data.pdf:pdf},
issn = {1465-4644},
journal = {Biostatistics},
keywords = {Bias (Epidemiology),Binomial Distribution,Biometry,Biometry: methods,Expressed Sequence Tags,Gene Expression Profiling,Gene Expression Profiling: methods,Gene Expression Profiling: statistics \& numerical,Gene Library,Humans,Information Storage and Retrieval,Information Storage and Retrieval: methods,Information Storage and Retrieval: statistics \& nu,Likelihood Functions,Messenger,Messenger: analysis,RNA,Regression Analysis,Research Design,Research Design: statistics \& numerical data,Sample Size,Stochastic Processes,Weights and Measures},
month = apr,
number = {2},
pages = {321--32},
pmid = {17728317},
title = {{Small-sample estimation of negative binomial dispersion, with applications to SAGE data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17728317},
volume = {9},
year = {2008}
}
