Genetics: Interpreting Hi-C Maps
While Hi-C maps can be pleasing to look at, it can take time to learn how to read them. This article will focus on enabling one to interpret these data-rich figures. A summary of Hi-C is here, from another one of my articles.
“Cis-Trans Interaction Ratio”¹
Cis interactions are between genetic elements on the same chromosome, and trans interactions are between genetic factors on different chromosomes¹. Given how Hi-C matrices often plot chromosomes against one another, a signal that veers from the diagonal can be interpreted as interactions between different chromosomes¹. This measure is typically used to evaluate data quality¹. Background noise from random processes would affect cis and trans interactions, decreasing the ratio¹. An ideal ratio is between 0.40 and 0.60¹.
“Distance-Dependent Interaction Frequency”¹
Generally, cis components' interaction decreases with the genomic distance represented on the map as reducing intensity away from the diagonal¹. This is thought to be due to random chromosome movement¹. We can study this using either interpolation by creating a function modelling the findings or segregating data based on genomic distances and averaging it (binning)¹.
These compartments, as I have described in my introductory article to Hi-C, refer to regions that can or cannot bind to other components with active more likely to match with active and inactive with inactive¹. They are represented on the map as checkboard-like patterns and tend to be the first principal component for PCA¹. They represent broader trends versus more focused changes¹.
While genomic compartments typically represent broader trends, topological domains can represent more narrow trends¹. Enhanced square boxes represent them along the diagonal¹. Various methods can quantitatively evaluate these by focusing on bins, which I will not cover here¹.
This is a more complex topic that I will not be discussing in this article.