Millefy – visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets

Read coverage of RNA sequencing data reflects gene expression and RNA processing events. Single-cell RNA sequencing (scRNA-seq) methods, particularly “full-length” ones, provide read coverage of many individual cells and have the potential to reveal cellular heterogeneity in RNA transcription and processing. However, visualization tools suited to highlighting cell-to-cell heterogeneity in read coverage are still lacking.

Researchers at the University of Tsukuba, Japan have developed Millefy, a tool for visualizing read coverage of scRNA-seq data in genomic contexts. Millefy is designed to show read coverage of all individual cells at once in genomic contexts and to highlight cell-to-cell heterogeneity in read coverage. By visualizing read coverage of all cells as a heat map and dynamically reordering cells based on diffusion maps, Millefy facilitates discovery of “local” region-specific, cell-to-cell heterogeneity in read coverage. The developers applied Millefy to scRNA-seq data sets of mouse embryonic stem cells and triple-negative breast cancers and showed variability of transcribed regions including antisense RNAs, 3 ′ UTR lengths, and enhancer RNA transcription.

Overview of Millefy

Fig. 1

 

a Millefy imports scRNA-seq data and visualizes read coverage of individual cells as a heat map. The rows (i.e., cells) of the heat map are dynamically reordered by diffusion maps. This automatic reordering highlights cell-to-cell heterogeneity in read coverage, which is hidden by mean read coverage data. Millefy associates genomic contexts, including bulk NGS data, genomic features, and gene annotations, thus facilitating the interpretation of single-cell read coverage. The parallelograms represent different types of input data. Gray boxes represent displayed tracks in Millefy. White boxes represent computation of a read coverage matrix. b An example plot in Millefy

Availability – Millefy is available as an R package (https://github.com/yuifu/millefy) and as a Docker image for use with Jupyter Notebook (https://hub.docker.com/r/yuifu/datascience-notebook-millefy).

Ozaki H, Hayashi T, Umeda M, Nikaido I. (2020) Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets. BMC Genomics 21, 177. [article]

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