DEclust: A statistical approach for obtaining differential expression profiles of multiple conditions
نویسندگان
چکیده
High-throughput RNA sequencing technology is widely used to comprehensively detect and quantify cellular gene expression. Thus, numerous analytical methods have been proposed for identifying differentially expressed genes (DEGs) between paired samples such as tumor and control specimens, but few studies have reported methods for analyzing differential expression under multiple conditions. We propose a novel method, DEclust, for differential expression analysis among more than two matched samples from distinct tissues or conditions. As compared to conventional clustering methods, DEclust more accurately extracts statistically significant gene clusters from multi-conditional transcriptome data, particularly when replicates of quantitative experiments are available. DEclust can be used for any multi-conditional transcriptome data, as well as for extending any DEG detection tool for paired samples to multiple samples. Accordingly, DEclust can be used for a wide range of applications for transcriptome data analysis. DEclust is freely available at http://www.dna.bio.keio.ac.jp/software/DEclust.
منابع مشابه
Differential Expression of Arabidopsis thaliana Acid Phosphatases in Response to Abiotic Stresses
The objective of this research is to identify Arabidopsis thaliana genes encoding acid phosphatases induced by phosphate starvation. Multiple alignments of eukaryotic acid phosphatase amino acid sequences led to the classification of these proteins into four groups including purple acid phosphatases (PAPs). Specific primers were degenerated and designed based on conserved sequences of PAPs isol...
متن کاملExact Implementation of Multiple Initial Conditions in the DQ Solution of Higher-Order ODEs
The differential quadrature method (DQM) is one of the most elegant and useful approximate methods for solving initial and/or boundary value problems. It is easy to use and also straightforward to implement. However, the conventional DQM is well-known to have some difficulty in implementing multiple initial and/or boundary conditions at a given discrete point. To overcome this difficulty, this ...
متن کاملAn Effective Nonparametric Clustering Algorithm Based on Statistical Features of Neighborhood
Many clustering algorithms require to input user-specific parameter or acquire some prior knowledge. It is not practical in some real-world applications. In this paper, an algorithm without predetermined parameters or prior knowledge, called DECLUST, is proposed to cluster spatial numeric data set. DECLUST calculates the statistical features of neighborhood for each point, and creates the candi...
متن کاملFlexible temporal expression profile modelling using the Gaussian process
Motivation: Time course gene expression experiments have proved valuable in a variety of biological studies (e.g., Chuang et al., 2002; Edwards et al., 2003). A general goal common to many of these time course experiments is to identify genes that exhibit different temporal expression profiles across multiple biological conditions. Such experiments are, however, often hampered by the lack of da...
متن کاملMicroarray data analysis and mining.
DNA microarray is an innovative technology for obtaining information on gene function. Because it is a high-throughput method, computational tools are essential in data analysis and mining to extract the knowledge from experimental results. Filtering procedures and statistical approaches are frequently combined to identify differentially expressed genes. However, obtaining a list of differentia...
متن کامل