Hi-C Compartment Shift Analysis FAQs

Question 1

What does the Hi-C Compartment Shift Analysis skill do?

Accepted Answer

This skill automates the detection of A/B compartment changes between Hi-C conditions. It processes PC1 eigenvector data, performs statistical comparisons using the limma framework, and identifies genomic regions that shift or flip between active and inactive states.

Question 2

When should I use this skill?

Accepted Answer

Use this skill when comparing chromatin architecture across different biological conditions, such as comparing wild-type vs. mutant cell lines or different developmental stages, to find statistically significant structural changes.

Question 3

Does this skill support biological replicates?

Accepted Answer

Yes. Unlike simple subtraction methods, this skill uses replicate-aware statistics (via limma) to ensure that identified compartment shifts are consistent across your samples and statistically robust.

Question 4

How does this skill improve my bioinformatics workflow?

Accepted Answer

It eliminates the manual overhead of converting TSV data to bedGraph formats, generating unified bin lists, and writing complex R scripts for differential analysis. Claude guides you through genome selection and handles the multi-step HOMER pipeline automatically.

Question 5

What are the required inputs for this analysis?

Accepted Answer

You need TSV files for at least two conditions (ideally with 2+ replicates each) containing Chromosome, Start, End, and PC1 (E1) values. The skill will prompt you for the specific genome assembly used for your study.

Hi-C Compartment Shift Analysis

Hi-C Compartment Shift Analysis

Key Features

Use Cases

Key Features

Use Cases