MetabolicExprImpution¶

This process imputes the dropout values in scRNA-seq data.

It takes the Seurat object as input and outputs the Seurat object with imputed expression data.

You can turn off the imputation by setting the noimpute option of the process group to True.

Environment Variables¶

• tool (choice): Default: alra.
  Either alra, scimpute or rmagic
  • alra: Use RunALRA() from Seurat
  • scimpute: Use scImpute() from scimpute
  • rmagic: Use magic() from Rmagic
• scimpute_args (ns): The arguments for scimpute
  • drop_thre (type=float): Default: 0.5.
    The dropout threshold
  • kcluster (type=int): Number of clusters to use
  • ncores (type=int): Default: 1.
    Number of cores to use
  • refgene: Default: ~/reference/hg19/hg19-gene.gtf.
    The reference gene file
• rmagic_args (ns): The arguments for rmagic
  • python: Default: python.
    The python path where magic-impute is installed.
    
• alra_args (type=json): Default: {}.
  The arguments for RunALRA()

Reference¶

• Linderman, George C., Jun Zhao, and Yuval Kluger. "Zero-preserving imputation of scRNA-seq data using low-rank approximation." BioRxiv (2018): 397588.
• Li, Wei Vivian, and Jingyi Jessica Li. "An accurate and robust imputation method scImpute for single-cell RNA-seq data." Nature communications 9.1 (2018): 997.
• Dijk, David van, et al. "MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data." BioRxiv (2017): 111591.