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Gene selection for the reconstruction of stem cell differentiation trees: a linear programming approach
Author(s) -
Mohamed Ali Ghadie,
Nathalie Japkowicz,
Theodore J. Perkins
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btv192
Subject(s) - hierarchy , hierarchical clustering , gene , computational biology , metric (unit) , tree (set theory) , euclidean distance , cluster analysis , biology , computer science , mathematics , genetics , artificial intelligence , combinatorics , operations management , economics , market economy
Stem cell differentiation is largely guided by master transcriptional regulators, but it also depends on the expression of other types of genes, such as cell cycle genes, signaling genes, metabolic genes, trafficking genes, etc. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering can organize cell types into a tree, but in general this tree is different from the differentiation hierarchy itself.

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