hic-compartment-shift

To detect A/B compartment shifts in Hi-C data, compare PC1 eigenvectors across samples and perform differential PC1 analysis using LIMMA to identify regions switching compartments. This requires TSV files containing chromosome coordinates and E1 values.

Differential compartment analysis requires user-supplied TSV files with columns for Chrom, Start, End, Weight, and E1, representing two conditions with replicates. The tool converts these into PC1 bedGraph files and a unified bin list for LIMMA statistics.

No, detecting compartment switching regions requires Hi-C datasets with two or more replicates per condition. Replicate-aware LIMMA statistics are applied to PC1 eigenvectors to accurately identify differential compartments.

PC1 eigenvector comparison identifies chromatin compartment changes by extracting PC1 values across all samples using a unified genomic bin list. Shifts in PC1 signs across conditions indicate a switch between active A and inactive B compartments.

Hi-C A/B compartment shift analysis generates PC1 bedGraph files, genomic bin lists, and differential compartment tables. It also produces stitched compartment-shift domains to visualize regions switching between A and B states.