15/10/2017 Biology Computer Science Medicine
DOI: 10.1093/bioinformatics/btx406 SemanticScholar ID: 3824030 MAG: 2654600824

Identification of transcript regulatory patterns in cell differentiation

Publication Summary

Motivation Studying transcript regulatory patterns in cell differentiation is critical in understanding its complex nature of the formation and function of different cell types. This is done usually by measuring gene expression at different stages of the cell differentiation. However, if the gene expression data available are only from the mature cells, we have some challenges in identifying transcript regulatory patterns that govern the cell differentiation. Results We propose to exploit the information of the lineage of cell differentiation in terms of correlation structure between cell types. We assume that two different cell types that are close in the lineage will exhibit many common genes that are co-expressed relative to those that are far in the lineage. Current analysis methods tend to ignore this correlation by testing for differential expression assuming some sort of independence between cell types. We employ a Bayesian approach to estimate the posterior distribution of the mean of expression in each cell type, by taking into account the cell formation path in the lineage. This enables us to infer genes that are specific in each cell type, indicating the genes are involved in directing the cell differentiation to that particular cell type. We illustrate the method using gene expression data from a study of haematopoiesis. Availability and implementation R codes to perform the analysis are available in http://www1.maths.leeds.ac.uk/∼arief/R/CellDiff/. Contact [email protected]. Supplementary information Supplementary data are available at Bioinformatics online.

CAER Authors

Avatar Image for Arief Gusnanto

Dr. Arief Gusnanto

University of Leeds

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