**Overview of analysis methods for the interpretation of scRNA-seq data**

Rostom R, Svensson V, Teichmann SA, Kar G. (2017) **Computational approaches for interpreting scRNA-seq data**. *FEBS Lett* [Epub ahead of print]. [article]

The researchers identified a number of chromatin profiles that contain functional information and are biologically interpretable. They also observed that epigenetic profiles are characterized by specific genomic contexts and show significant association with distinct genomic features. Moreover, analysis of RNA-seq data reveals that distinct chromatin signatures correlate with the level of gene expression.

**Non-negative matrix factorization of epigenetic data**

*The scheme gives an intuitive representation of how NMF can be used to approximate a multivariate epigenetic signal in a pre-defined number of signal patterns. The algorithm takes as input a data-matrix (V) with rows corresponding to a series of genomic intervals (or loci) and columns corresponding to different epigenetic tracks for the marks. Each cell in the matrix defines the normalized/background corrected signal of a given epigenetic mark (y) in a given locus (x) ( a). As result, a standard NMF procedure yields two sparse matrices W (the weight matrix) and H (the coefficient matrix) describing the contribution of each code/profile to single loci and single marks respectively (b)*

Overall, this study highlights the utility of NMF in studying functional relationships between different epigenetic modifications and may provide new biological insights for the interpretation of the chromatin dynamics.

Gandolfi F, Tramontano A. (2017) **A computational approach for the functional classification of the epigenome.** *Epigenetics Chromatin* 10:26. [article]

The Sincell R package implements a methodological toolbox allowing flexible workflows under such a framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. The functionalities of Sincell are illustrated in a real case study, which demonstrates its ability to discriminate noisy from stable cell-state hierarchies.

*Overall workflow for the statistical assessment of cell-state hierarchies implemented by the Sincell R package. Dashed arrows correspond to optional steps in the analysis.*

**Availability** – Sincell is an open-source R/Bioconductor package available at http://bioconductor.org/packages/3.1/bioc/html/sincell.html. A detailed manual and vignette is provided with the package.

**Contact** – antonio.rausell@isb-sib.ch

JuliĆ” M, Telenti A, Rausell A. (2015) **Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq**. *Bioinformatics* [Epub ahead of print]. [article]