A framework using topological pathways for deeper analysis of transcriptome data.
BMC Genomics
; 21(Suppl 1): 834, 2020 Mar 05.
Article
en En
| MEDLINE
| ID: mdl-32138666
ABSTRACT
BACKGROUND:
Pathway analysis is one of the later stage data analysis steps essential in interpreting high-throughput gene expression data. We propose a set of algorithms which given gene expression data can recognize which portion of sub-pathways are actively utilized in the biological system being studied. The degree of activation is measured by conditional probability of the input expression data based on the Bayesian Network model constructed from the topological pathway.RESULTS:
We demonstrate the effectiveness of our pathway analysis method by conducting two case studies. The first one applies our method to a well-studied temporal microarray data set for the cell cycle using the KEGG Cell Cycle pathway. Our method closely reproduces the biological claims associated with the data sets, but unlike the original work ours can produce how pathway routes interact with each other above and beyond merely identifying which pathway routes are involved in the process. The second study applies the method to the p53 mutation microarray data to perform a comparative study.CONCLUSIONS:
We show that our method achieves comparable performance against all other pathway analysis systems included in this study in identifying p53 altered pathways. Our method could pave a new way of carrying out next generation pathway analysis.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Proteína p53 Supresora de Tumor
/
Perfilación de la Expresión Génica
/
Secuenciación de Nucleótidos de Alto Rendimiento
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Mutación
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Año:
2020
Tipo del documento:
Article