singleCellHaystack: Finding surprising genes in 2-dimensional representations of single cell transcriptome data

Vandenbon, A.; Diez, D.

doi:10.1101/557967

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bioRxiv · 10.1101/557967

singleCellHaystack: Finding surprising genes in 2-dimensional representations of single cell transcriptome data

Vandenbon, A.·Diez, D.

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Abstract

SummarySingle-cell sequencing data is often visualized in 2-dimensional plots, including t-SNE plots. However, it is not straightforward to extract biological knowledge, such as differentially expressed genes, from these plots. Here we introduce singleCellHaystack, a methodology that addresses this problem. singleCellHaystack uses Kullback-Leibler Divergence to find genes that are expressed in subsets of cells that are non-randomly positioned on a 2D plot. We illustrate the usage of singleCellHaystack through applications on several single-cell datasets. singleCellHaystack is implemented as an R package, and includes additional functions for clustering and visualization of genes with interesting expression patterns. Availability and implementationhttps://github.com/alexisvdb/singleCellHaystack Contactalexisvdb@infront.kyoto-u.ac.jp

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Vandenbon, A., Diez, D.. 2019-02-22. singleCellHaystack: Finding surprising genes in 2-dimensional representations of single cell transcriptome data. https://doi.org/10.1101/557967

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