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Analysis Tools Registry

Markov Affinity-based Graph Imputation of Cells (MAGIC)

David van Dijk, Kevin Moon, Scott Gigante, Daniel Dager, Guy Wolf, Smita Krishnaswamy

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Markov Affinity-based Graph Imputation of Cells (MAGIC) is an algorithm for denoising and imputation of single cells applied to single-cell RNA sequencing data, as described in Van Dijk D et al. (2018), Recovering Gene Interactions from Single-Cell Data Using Data Diffusion, Cell https://www.cell.com/cell/abstract/S0092-8674(18)30724-4.

Use

docker pull scottgigante/magic:release-1.1

Here we download a csv file containing raw scRNA-seq counts, preprocess it by filtering cells with less than 2000 counts, library size normalize and then apply a square root transform before running MAGIC, then save the smoothed data matrix to magic_output.csv in your current working directory.

docker run -v ${PWD}:/data --rm scottgigante/magic:release-1.1 --filename  https://github.com/KrishnaswamyLab/MAGIC/raw/master/data/HMLE_TGFb_day_8_10.csv.gz  --min-library-size 2000 --normalize --transform sqrt --knn 5 --decay 15 --all-genes  --output /data/magic_output.csv

Validate

Run this command to confirm your container produces correct reference output:

docker run --rm scottgigante/magic:release-1.1 --validate

Contact

Scott Gigante (scott.gigante@yale.edu)

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