Single cell RNA-seq gene expression counts are zero inflated due to inefficient mRNA capture. This normalization task is based on MAGIC[1]–MArkov Affinity-based Graph Imputation of Cells), to recover gene expression lost due to drop-out. The limitation on using this method is up to 50K cells in the projectinput data node.

To invoke this task, click on a normalized data node which has less than 50K cells, andit will first compute PCA to use the number of PCs specified to impute.

First select the computation is based on samples/cells or features, and click Finish to replace missing values. Some functions will generate the same results no matter which transform option is selected, e.g. constant value. Others will generate different results:

• Constant values: specify a value to replace the missing data
• Maximum: use maximum value of samples/cells or features to replace missing data depends transform option
• Mean: use mean value of samples/cells or features to replace missing data depends transform option
• Median: use median value of samples/cells or features to replace missing data depends transform option
• Minimum: use minimum value of samples/cells or features to replace missing data depends transform option
• K-nearest neighbor (mean): specify number of neighbors (N), Euclidean metric is used to compute neighbors, use mean of (N) neighbors to replace missing data
• K-nearest neighbor (median): specify number of neighbors (N), Euclidean metric is used to compute neighbors, use median of (N) neighbors to replace missing data

References

1. Dijk D et al. MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data

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