Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
Bioinformatics. 2020 May 1;36(9):2902-2904. doi: 10.1093/bioinformatics/btaa048.
Single-cell Hi-C (scHi-C) allows the study of cell-to-cell variability in chromatin structure and dynamics. However, the high level of noise inherent in current scHi-C protocols necessitates careful assessment of data quality before biological conclusions can be drawn. Here, we present GiniQC, which quantifies unevenness in the distribution of inter-chromosomal reads in the scHi-C contact matrix to measure the level of noise. Our examples show the utility of GiniQC in assessing the quality of scHi-C data as a complement to existing quality control measures. We also demonstrate how GiniQC can help inform the impact of various data processing steps on data quality.
Source code and documentation are freely available at https://github.com/4dn-dcic/GiniQC.
Supplementary data are available at Bioinformatics online.
单细胞 Hi-C（scHi-C）允许研究染色质结构和动力学的细胞间可变性。然而，当前 scHi-C 协议中固有的高水平噪声需要在得出生物学结论之前仔细评估数据质量。在这里，我们提出了 GiniQC，它量化了 scHi-C 接触矩阵中染色体间读取分布不均匀的程度，以衡量噪声水平。我们的示例展示了 GiniQC 在评估 scHi-C 数据质量方面的效用，它是对现有质量控制措施的补充。我们还展示了 GiniQC 如何帮助了解各种数据处理步骤对数据质量的影响。
源代码和文档可在 https://github.com/4dn-dcic/GiniQC 上免费获得。
补充数据可在生物信息学在线获得。
