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DIME specifications

Information


Unique identifier OMICS_00473
Name DIME
Alternative name Differential Identification using Mixtures Ensemble
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data An R list that contains normalized differences for chromosome(s) that need to be analyzed.
Output data The output of DIME is a list containing four elements each corresponding to the results of the overall best model or those of an individual class.
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 2.0
Computer skills Advanced
Version 1.2
Stability Stable
Maintained Yes

Versioning


No version available

Documentation


Maintainer


  • person_outline Cenny Taslim

Publication for Differential Identification using Mixtures Ensemble

DIME citations

 (5)
library_books

BOG: R package for Bacterium and virus analysis of Orthologous Groups

2015
Comput Struct Biotechnol J
PMCID: 4475783
PMID: 26106460
DOI: 10.1016/j.csbj.2015.05.002

[…] hat is capable of quick and accurate identification of COGs that are over-represented among differentially expressed genes through rigorous statistical tests.BOG consists of three modules: (optional) DIME processing, analysis, and output modules ((a)). More specifically, after reading in a raw input data set, BOG performs a differential analysis through a mixture ensemble procedure and computes lo […]

library_books

dCaP: detecting differential binding events in multiple conditions and proteins

2014
BMC Genomics
PMCID: 4290593
PMID: 25522020
DOI: 10.1186/1471-2164-15-S9-S12

[…] -T2-all is substantially more powerful than MANOVA at all levels of binding strengths. We further compared the performance of dCaP-T2 in a two-sample-one-factor scenario (dCaP-T2-pair) with ANOVA and DIME []. The ROC curves (Figure ) again show that dCaP-T2-pair performs consistently better than the other two methods at all levels of binding strengths, and the performance of DIME is better than AN […]

library_books

A Mixture Modeling Framework for Differential Analysis of High Throughput Data

2014
PMCID: 4095709
PMID: 25057284
DOI: 10.1155/2014/758718

[…] first step of the classification criterion, three of the normal components were designated as differential components (see ). shows the QQ-plot, which indicates a good fit of the model to the data. DIME identified around 21% (3,909) of the genes as having enriched Pol II binding quantity in OHT cell line when compared with MCF7. […]

library_books

Risk of Type 1 Diabetes Progression in Islet Autoantibody Positive Children Can Be Further Stratified Using Expression Patterns of Multiple Genes Implicated in Peripheral Blood Lymphocyte Activation and Function

2014
PMCID: 4066338
PMID: 24595351
DOI: 10.2337/db13-1716

[…] and reliability. Finally, cohorts in most previous studies were restricted to children with a family history of T1D, such as the genetic studies in BABYDIAB (–), the Belgian Diabetes Registry (), and DiMe (Childhood Diabetes in Finland) () and metabolic marker studies in Diabetes Prevention Trial–Type 1 (–). The DAISY cohort used in the current study includes children from the general population a […]

library_books

Practical Guidelines for the Comprehensive Analysis of ChIP seq Data

2013
PLoS Comput Biol
PMCID: 3828144
PMID: 24244136
DOI: 10.1371/journal.pcbi.1003326

[…] ons (e.g., p-values or q-values linked to read-enrichment fold changes). It is strongly advised to verify that the data fulfill the requirements of the software chosen for the analysis. For instance, DIME assumes that a significant proportion of peaks are common to the conditions under comparison, MAnorm assumes that peaks that are common in both conditions do not change significantly, while othe […]

Citations

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DIME institution(s)
Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University, Columbus, OH, USA; Department of Statistics, The Ohio State University, Columbus, OH, USA
DIME funding source(s)
This work was supported in part by National Cancer Institute (grant U54CA113001) and National Science Foundation (grant DMS-1042946).

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