HiCzin: Normalizing metagenomic Hi-C data and detecting spurious contacts using zero-inflated negative binomial regression
High-throughput chromosome conformation capture (Hi-C) has recently been applied to natural microbial communities and revealed great potential to study multiple genomes simultaneously. Several extraneous factors may influence chromosomal contacts rendering the normalization of Hi-C contact maps essential for downstream analyses. However, the current paucity of metagenomic Hi-C normalization methods and the ignorance for spurious inter-species contacts weaken the interpretability of the data. Here, we report on two types of biases in metagenomic Hi-C experiments: explicit biases and implicit biases, and introduce HiCzin, a parametric model to correct both types of biases and remove spurious inter-species contacts. We demonstrate that the normalized metagenomic Hi-C contact maps by HiCzin result in lower biases, higher capability to detect spurious contacts, and better performance in metagenomic contig clustering. The HiCzin software and Supplementary Material are available at https://github.com/dyxstat/HiCzin.