# Correction to: SpectralTAD: an R package for defining a hierarchy of topologically associated domains using spectral clustering

The Original Article was published on 20 July 2020

Correction to: BMC Bioinformatics 21, 319 (2020)

https://doi.org/10.1186/s12859-020-03652-w

Following publication of the original article , the authors identified misformatted equations in the published article. The correctly formatted equations are given below.

1. Calculating the normalized symmetric Laplacian:

$$\overline{L}={D}^{-\frac{1}{2}}C{D}^{-\frac{1}{2}}$$

2. Solve the generalized eigenvalue problem:

$$\overline{L}\overline{V}=\lambda \overline{V}$$

3. The result is a matrix of eigenvectors $${\overline{V}}_{w\times k}$$, where w is the window size, and k is the number of eigenvectors used, and a vector of eigenvalues where each entry λi corresponds to the ith eigenvalue of the normalized Laplacian $$\overline{L}$$.

4. Normalize rows and columns to sum to 1:

$$\hat{V_{i.}}=\frac{\overline{V_{i.}}}{\left\Vert {\overline{V}}_{i.}\right\Vert }$$

5. Find the mean silhouette score over all possible numbers of clusters m and organize into a vector of means:

$${\overline{s}}_m=\frac{\sum_{i=1}^m{s}_i}{m}$$

6. Find the value of m which maximizes $${\overline{s}}_m$$

The original article has been updated.

## Reference

1. 1.

Cresswell, et al. SpectralTAD: an R package for defining a hierarchy of topologically associated domains using spectral clustering. BMC Bioinformatics. 2020;21:319. https://doi.org/10.1186/s12859-020-03652-w.

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Correspondence to Mikhail G. Dozmorov.

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