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. Author manuscript; available in PMC: 2021 Apr 19.
Published in final edited form as: Nat Genet. 2020 Oct 19;52(11):1247–1255. doi: 10.1038/s41588-020-00712-y

Extended Data Fig. 2. Technical details of the SSIM algorithm applied to Hi-C matrices.

Extended Data Fig. 2

a, Schematic overview of the structural similarity algorithm (SSIM). SSIM scores are calculated on all submatrices of R / Q at a given window size (WS). The final SSIM score is the mean of all SSIM submatrix scores. b, SSIM submatrix formula. Different components are coloured: illuminance (green), structure * contrast (red). x, y refer to submatrices (at the same positions) of the two full matrices for which the SSIM average is computed (see panel a). μ indicates the mean, σ the standard deviation, c1 and c2 are small constants that are introduced only for numerical reasons. c and d, SSIM comparisons of a matrix to itself (red dots) and 1,000 random matrices of the same size (blue dots). c, SSIM component values in dependence of SSIM score for different SSIM window sizes. d, Scatterplots of ranked SSIM scores at window size 100 vs. ranked scores at smaller window sizes.