Package: MEB
Type: Package
Title: A normalization-invariant minimum enclosing ball method to
        detect differentially expressed genes for RNA-seq data
Version: 1.2.0
Author: Yan Zhou, Jiadi Zhu
Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn>,
 Yan Zhou <zhouy1016@szu.edu.cn>
Description: Identifying differentially expressed genes between the
    same or different species is an urgent demand for biological and
    medical research. For RNA-seq data, systematic technical effects
    and different sequencing depths are usually encountered when conducting
    experiments. Normalization is regarded as an essential step
    in the discovery of biologically important changes in expression. The
    present methods usually involve normalization of the data with a scaling
    factor, followed by detection of significant genes. However, more
    than one scaling factor may exist because of the complexity of real
    data. Consequently, methods that normalize data by a single scaling
    factor may deliver suboptimal performance or may not even work.
    The development of modern machine learning techniques has
    provided a new perspective regarding discrimination between differentially
    expressed (DE) and non-DE genes. However, in reality, the
    non-DE genes comprise only a small set and may contain housekeeping
    genes (in same species) or conserved orthologous genes (in
    different species). Therefore, the process of detecting DE genes can
    be formulated as a one-class classification problem, where only non-DE 
    genes are observed, while DE genes are completely absent from
    the training data.
    We transform the problem to an outlier detection
    problem by treating DE genes as outliers, and we propose a
    normalization-invariant minimum enclosing ball (NIMEB) method to
    construct a smallest possible ball to contain the known non-DE genes
    in a feature space. The genes outside the minimum enclosing ball
    can then be naturally considered to be DE genes. Compared with
    the existing methods, the proposed NIMEB method does not require
    data normalization, which is particularly attractive when the RNA-seq
    data include more than one scaling factor. Furthermore, the NIMEB
    method could be easily extended to different species without normalization.
License: GPL-2
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.6.0)
Suggests: knitr,rmarkdown,BiocStyle
VignetteBuilder: knitr
RoxygenNote: 7.1.0
Imports: e1071, SummarizedExperiment
biocViews: DifferentialExpression, GeneExpression, Normalization,
        Classification, Sequencing
git_url: https://git.bioconductor.org/packages/MEB
git_branch: RELEASE_3_11
git_last_commit: 998cd04
git_last_commit_date: 2020-04-27
Date/Publication: 2020-04-27
NeedsCompilation: no
Packaged: 2020-04-28 07:10:44 UTC; biocbuild
Built: R 4.0.0; ; 2020-04-28 18:13:58 UTC; windows
