A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data
نویسندگان
چکیده
منابع مشابه
A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data
Recent developments in RNA-sequencing (RNA-seq) technology have led to a rapid increase in gene expression data in the form of counts. RNA-seq can be used for a variety of applications, however, identifying differential expression (DE) remains a key task in functional genomics. There have been a number of statistical methods for DE detection for RNA-seq data. One common feature of several leadi...
متن کاملDifferential Expression Analysis for RNA-Seq Data
RNA-Seq is increasingly being used for gene expression profiling. In this approach, next-generation sequencing (NGS) platforms are used for sequencing. Due to highly parallel nature, millions of reads are generated in a short time and at low cost. Therefore analysis of the data is a major challenge and development of statistical and computational methods is essential for drawing meaningful conc...
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In this section, we will use the Hammer et. al. data [1] to illustrate how to use the functions in the sSeq package. The two conditions are control Sprague Dawley after 2 months (Condition A) and L5 SNL Sprague Dawley after 2 months (Condition B). There are two samples within each condition. This data is included in the sSeq package as an example, and can be imported as follows. “countsTable” i...
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MOTIVATION Alternative splicing is central for cellular processes and substantially increases transcriptome and proteome diversity. Aberrant splicing events often have pathological consequences and are associated with various diseases and cancer types. The emergence of next-generation RNA sequencing (RNA-seq) provides an exciting new technology to analyse alternative splicing on a large scale. ...
متن کاملEmpirical likelihood tests for nonparametric detection of differential expression from RNA-seq data.
The availability of large quantities of transcriptomic data in the form of RNA-seq count data has necessitated the development of methods to identify genes differentially expressed between experimental conditions. Many existing approaches apply a parametric model of gene expression and so place strong assumptions on the distribution of the data. Here we explore an alternate nonparametric approa...
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ژورنال
عنوان ژورنال: Biostatistics
سال: 2012
ISSN: 1468-4357,1465-4644
DOI: 10.1093/biostatistics/kxs033