OmicsARules: a R package for integration of multi-omics datasets via association rules mining.
BMC Bioinformatics
; 20(1): 554, 2019 Nov 08.
Article
in En
| MEDLINE
| ID: mdl-31703610
ABSTRACT
BACKGROUND:
The improvements of high throughput technologies have produced large amounts of multi-omics experiments datasets. Initial analysis of these data has revealed many concurrent gene alterations within single dataset or/and among multiple omics datasets. Although powerful bioinformatics pipelines have been developed to store, manipulate and analyze these data, few explicitly find and assess the recurrent co-occurring aberrations across multiple regulation levels.RESULTS:
Here, we introduced a novel R-package (called OmicsARules) to identify the concerted changes among genes under association rules mining framework. OmicsARules embedded a new rule-interestingness measure, Lamda3, to evaluate the associated pattern and prioritize the most biologically meaningful gene associations. As demonstrated with DNA methlylation and RNA-seq datasets from breast invasive carcinoma (BRCA), esophageal carcinoma (ESCA) and lung adenocarcinoma (LUAD), Lamda3 achieved better biological significance over other rule-ranking measures. Furthermore, OmicsARules can illustrate the mechanistic connections between methlylation and transcription, based on combined omics dataset. OmicsARules is available as a free and open-source R package.CONCLUSIONS:
OmicsARules searches for concurrent patterns among frequently altered genes, thus provides a new dimension for exploring single or multiple omics data across sequencing platforms.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Software
/
Computational Biology
/
Genomics
/
Databases, Genetic
/
Data Mining
Type of study:
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
BMC Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2019
Document type:
Article
Affiliation country: