Figure 3. Eigenvector decomposition of iteratively corrected Hi-C data reveals genome-wide features of chromosome organization.

(a) Profiles of E¹ and genomic features along chr1 (1Mb resolution), E¹ from Hi-C HindIII data⁸(b) Scatter plot of E¹ vs GC content. Gray dots show GC content and E¹ of individual 1Mb regions. Black squares show mean chromosomal values of E¹ and mean GC content. Several chromosomes are indicated by numbers. (c) Heatmap of inter-chromosomal contacts between pairs of genomic regions as a function of their E¹ values; heatmap shows natural log of contact enrichment (see Online Methods). Notice the tendency of regions with similar values of E¹ to interact with each other. (d) (Left) Distribution of E¹ values. (Right) Autocorrelation of E¹ (blue) compared to 1000 shuffled E¹ (gray line shows mean, errorbars show standard deviation). (e) (Left) Distribution of observed eigenvalues (λ_k) and the distribution of eigenvalues for randomly re-sampled data (see Online Methods). Thirteen significant eigenvalues are shown in red. (Right) Matrix of Pearson correlation coefficients of leading eigenvectors obtained for Ncol and HindIII Hi-C data, revealing robustness of top three eigenvectors. (f) Variation of E² along chromosomal arms, with higher values near centromeres and telomeres. Grey points show values for individual genomic regions, black line shows the mean. (g) Genome-wide inter-chromosomal interactions mapped onto E¹ and E² space at 1Mb resolution. Regions are colored according to previously proposed¹⁷ chromatin types¹⁷. Notice no ¹⁷evident separation into distinct clusters. E¹ and E² calculated for Hi-C HindIII dataset⁸.