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Epigenetic signature of human induced pluripotent stem cells identified with the linear machine learning model

ABSTRACT: Epigenetic signature of human induced pluripotent stem cells identified with the linear machine learning model

PROVIDER: PRJNA593774 | ENA |

REPOSITORIES: ENA

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Epigenetic signature of human induced pluripotent stem cells identified with the linear machine learning model

Project description:Human induced pluripotent stem cells (iPSCs) were established as an artificial embryonic stem cells (ESCs) to avoid immune rejection, for ethical issues in regenerative medicine, and for biological research. Comparison analyses in previous studies revealed that there is no hot spot that distinguishes iPSCs from ESCs. We herewith established a learning model using Jubatus, as a machine learning platform, with linear model for classification to distinguish human iPSCs from ESCs based on DNA methylation profiles. We found that the linear model classification is most suitable for the analysis of human iPSCs whose line number is practically 10 to 100. The learning models discriminated ESCs and iPSCs with an accuracy of ≥ 85.71 % and ≥ 90.91 %, respectively. In addition, the epigenetic signature of iPSCs was identified by component analysis of the learning models. The iPSC-specific fluctuated methylation regions were abundant at chromosome 7, 8, 12, and 22. The method can be utilized with comprehensive data and can also be widely applied to many aspects of molecular biology research.

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