The huge amount of data acquired by high-throughput sequencing requires data reduction for effective analysis. Here we develop a new data reduction method for genome-wide open chromatin data toward cell type classification. Regarding the genome as a string of 1s and 0s based on a systematically optimized set of peaks and calculating the Hamming distance enables us to quantitatively evaluate differences between samples of hematopoietic cells , classify cell types , and infer the origin of leukemic cells , potentially leading to a better understanding of leukemia pathogenesis.
The huge amount of data acquired by high-throughput sequencing requires data reduction for effective analysis. Here we give a clustering algorithm for genome-wide open chromatin data using a new data reduction method. This method regards the genome as a string of 1s and 0s based on a set of peaks and calculates the Hamming distances between the strings. This algorithm with the systematically optimized set of peaks enables us to quantitatively evaluate differences between samples of hematopoietic cells and classify cell types , potentially leading to a better understanding of leukemia pathogenesis.
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develop a new data reduction method
give a clustering algorithm
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toward cell type classification. Regarding
using a new data reduction method. This method regards
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systematically optimized
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calculating the Hamming distance
calculates the Hamming distances between the strings. This algorithm with the systematically optimized set of peaks
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,
and
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, and infer the origin of leukemic cells