Systematic clustering algorithm for chromatin accessibility data and its application to hematopoietic cells
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2019
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913540483317760 |
|---|---|
| author | Tanaka, Azusa Ishitsuka, Yasuhiro Ohta, Hiroki Fujimoto, Akihiro Yasunaga, Jun-ichirou Matsuoka, Masao |
| author_facet | Tanaka, Azusa Ishitsuka, Yasuhiro Ohta, Hiroki Fujimoto, Akihiro Yasunaga, Jun-ichirou Matsuoka, Masao |
| contents | 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 $1$s and $0$s 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1912_10641 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Systematic clustering algorithm for chromatin accessibility data and its application to hematopoietic cells Tanaka, Azusa Ishitsuka, Yasuhiro Ohta, Hiroki Fujimoto, Akihiro Yasunaga, Jun-ichirou Matsuoka, Masao Genomics Statistical Mechanics Data Analysis, Statistics and Probability Quantitative Methods 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 $1$s and $0$s 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. |
| title | Systematic clustering algorithm for chromatin accessibility data and its application to hematopoietic cells |
| topic | Genomics Statistical Mechanics Data Analysis, Statistics and Probability Quantitative Methods |
| url | https://arxiv.org/abs/1912.10641 |