Mixture models for gene expression experiments with two species
نویسندگان
چکیده
منابع مشابه
Gene Expression Clustering with Functional Mixture Models
We propose a functional mixture model for simultaneous clustering and alignment of sets of curves measured on a discrete time grid. The model is specifically tailored to gene expression time course data. Each functional cluster center is a nonlinear combination of solutions of a simple linear differential equation that describes the change of individual mRNA levels when the synthesis and decay ...
متن کاملMixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments
Data variability can be important in microarray data analysis. Thus, when clustering gene expression profiles, it could be judicious to make use of repeated data. In this paper, the problem of analysing repeated data in the model-based cluster analysis context is considered. Linear mixed models are chosen to take into account data variability and mixture of these models are considered. This lea...
متن کاملMixture modelling of gene expression data from microarray experiments
MOTIVATION Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present nov...
متن کاملBayesian Mixture Models for Gene Expression and Protein Profiles
We review the use of semi-parametric mixture models for Bayesian inference in high throughput genomic data. We discuss three specific approaches for microarray data, for protein mass spectrometry experiments, and for SAGE data. For the microarray data and the protein mass spectrometry we assume group comparison experiments, i.e., experiments that seek to identify genes and proteins that are dif...
متن کاملGene expression Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models
Motivation: In recent years, gene expression studies have increasingly made use of high-throughput sequencing technology. In turn, research concerning the appropriate statistical methods for the analysis of digital gene expression (DGE) has flourished, primarily in the context of normalization and differential analysis. Results: In this work, we focus on the question of clustering DGE profiles ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Human Genomics
سال: 2014
ISSN: 1479-7364
DOI: 10.1186/1479-7364-8-12