Tool: Venice: a fast and accurate approach to find marker genes in single-cell RNA-seq data
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12 weeks ago by
sonpham570
sonpham570 wrote:

Dear Colleagues,

I would like to introduce Venice, a non-parametric approach for finding marker genes in single-cell RNA-seq data, developed by BioTuring team. Using a widely adopted benchmarking approach (Wang et al. 2019), Venice obtains the best accuracy compared with 14 popular methods, while keeping a modest running time.

For preliminary benchmark, please visit here: https://blog.bioturing.com/2019/06/24/venice-a-non-parametric-test-for-finding-marker-genes-in-single-cell-rna-seq-data/

Venice is open-source, and freely available for academic entities. The method is now incorporated in Signac, a single-cell analytics package developed by BioTuring, available at https://github.com/bioturing/signac.

Some benchmark results:

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Type I error control across 87 instances from eight real single-cell null data sets. The black line indicates the target FPR = 0.05 and the y-axis is square-root transformed for increased visibility. Centerline, median; hinges, first and third quartiles; whiskers, most extreme values within 1.5 interquartile range (IQR) from the box. The benchmark was adopted from Sonesson et al. (2018).

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Accuracy on the simulated data. We use scDD package to simulate a dataset contains 2000 differential expressed genes (DEG) that span across four groups: DE, DB, DM, DP. The dataset also has 18000 non-DEG of types EE or EP.

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Running time (in seconds) of all methods.

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