GiniQC

GiniQC quantifies noise in single-cell Hi-C (scHi-C) data by measuring unevenness in the distribution of inter-chromosomal reads within contact matrices to assess data quality.

Key Features:

• Noise Quantification: Quantifies unevenness in the distribution of inter-chromosomal reads within the scHi-C contact matrix as a metric of noise.
• Complementary Quality Control: Provides a QC measure specifically targeting inter-chromosomal read distribution that complements existing quality metrics.
• Impact Assessment of Data Processing Steps: Evaluates how data-processing steps influence the inter-chromosomal read unevenness metric to inform workflow decisions.

Scientific Applications:

• Chromatin architecture analysis: Assess scHi-C data quality for studies of chromatin architecture and dynamics at single-cell resolution.
• Cell-to-cell variability interpretation: Identify noisy cells or datasets to improve reliability of detected cell-to-cell variability in chromatin structure.
• Processing pipeline evaluation: Compare effects of preprocessing or processing steps on noise levels in scHi-C datasets.

Methodology:

Computes a metric quantifying unevenness of inter-chromosomal read distribution within scHi-C contact matrices and compares that metric across cells or processing conditions to assess noise and the effects of data-processing steps.