Considerations and caveats for analyzing chromatin compartments

Abstract

Genomes are organized into nuclear compartments, separating active from inactive chromatin. Chromatin compartments are readily visible in a large number of species by experiments that map chromatin conformation genome-wide. When analyzing these maps, a common step is the identification of genomic intervals that interact within A (active) and B (inactive) compartments. It has also become increasingly common to identify and analyze subcompartments. We review different strategies to identify A/B and subcompartment intervals, including a discussion of various machine-learning approaches to predict these features. We then discuss the strengths and limitations of current strategies and examine how these aspects of analysis may have impacted our understanding of chromatin compartments.

Keywords: Hi-C; Micro-C; chromatin organization; compartments; eigenvector; subcompartment.

FIGURE 1

An illustration of the checkerboard pattern commonly found by whole-genome chromatin conformation assays such as Hi-C and Micro-C. The top tracks illustrate an example eigenvector as well as A/B and subcompartment classification.

FIGURE 2

An example of the steps involved in A/B compartment identification, including matrix balancing, distance normalization, Pearson correlation, and PCA. Contact Map from Rowley et al., 2017.

FIGURE 3

Illustration of how other organizational features may be detected by the leading eigenvector instead of A/B compartments. Contact map from Rowley et al., 2017.

FIGURE 4

Illustration of the relationship between bin size and matrix size. Heatmap sizes are proportional to the number of bins. Contact map from Rao et al., 2014.

FIGURE 5

(A) Demonstration of how data binning results in the lost ability to detect smaller features. Bottom: Binning of Hi-C data within a 500 kb region. Contact map from Rao et al., 2014. Top: The same binning of an image depicting the Carina Nebula from the James Webb Space Telescope; Credit: NASA. (B) Histogram of the reported number of combined contact pairs for Hi-C experiments uploaded to the 4DNucleome database.

FIGURE 6

Demonstration of how matrix balancing can obscure widespread changes. Top row: An example checkerboard matrix (control) where the signal along the A compartment becomes decreased (treatment), the differences shown by the intensity of blue signal on the right (difference). Bottom row: The same checkerboards and the differences after matrix balancing.

FIGURE 7

Illustrative example of how saddle plot analysis can detect changes in compartment interaction intensity. The example matrices from Figure 6 were used to demonstrate how resorting interactions can provide a useful visualization.