Data-driven characterization of molecular phenotypes across heterogeneous sample collections.
Nucleic Acids Res
; 47(13): e76, 2019 07 26.
Article
en En
| MEDLINE
| ID: mdl-31329928
ABSTRACT
Existing large gene expression data repositories hold enormous potential to elucidate disease mechanisms, characterize changes in cellular pathways, and to stratify patients based on molecular profiles. To achieve this goal, integrative resources and tools are needed that allow comparison of results across datasets and data types. We propose an intuitive approach for data-driven stratifications of molecular profiles and benchmark our methodology using the dimensionality reduction algorithm t-distributed stochastic neighbor embedding (t-SNE) with multi-study and multi-platform data on hematological malignancies. Our approach enables assessing the contribution of biological versus technical variation to sample clustering, direct incorporation of additional datasets to the same low dimensional representation, comparison of molecular disease subtypes identified from separate t-SNE representations, and characterization of the obtained clusters based on pathway databases and additional data. In this manner, we performed an integrative analysis across multi-omics acute myeloid leukemia studies. Our approach indicated new molecular subtypes with differential survival and drug responsiveness among samples lacking fusion genes, including a novel myelodysplastic syndrome-like cluster and a cluster characterized with CEBPA mutations and differential activity of the S-adenosylmethionine-dependent DNA methylation pathway. In summary, integration across multiple studies can help to identify novel molecular disease subtypes and generate insight into disease biology.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Fenotipo
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Leucemia Mieloide Aguda
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Análisis por Conglomerados
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Regulación Leucémica de la Expresión Génica
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Biología Computacional
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Perfilación de la Expresión Génica
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Minería de Datos
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Conjuntos de Datos como Asunto
Límite:
Humans
Idioma:
En
Revista:
Nucleic Acids Res
Año:
2019
Tipo del documento:
Article
País de afiliación:
Finlandia