RT Journal Article
SR Electronic
T1 MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 111591
DO 10.1101/111591
A1 David van Dijk
A1 Juozas Nainys
A1 Roshan Sharma
A1 Pooja Kaithail
A1 Ambrose J. Carr
A1 Kevin R. Moon
A1 Linas Mazutis
A1 Guy Wolf
A1 Smita Krishnaswamy
A1 Dana Pe'er
YR 2017
UL http://biorxiv.org/content/early/2017/02/25/111591.abstract
AB Single-cell RNA-sequencing is fast becoming a major technology that is revolutionizing biological discovery in fields such as development, immunology and cancer. The ability to simultaneously measure thousands of genes at single cell resolution allows, among other prospects, for the possibility of learning gene regulatory networks at large scales. However, scRNA-seq technologies suffer from many sources of significant technical noise, the most prominent of which is ‘dropout’ due to inefficient mRNA capture. This results in data that has a high degree of sparsity, with typically only ~10% non-zero values. To address this, we developed MAGIC (Markov Affinity-based Graph Imputation of Cells), a method for imputing missing values, and restoring the structure of the data. After MAGIC, we find that two- and three-dimensional gene interactions are restored and that MAGIC is able to impute complex and non-linear shapes of interactions. MAGIC also retains cluster structure, enhances cluster-specific gene interactions and restores trajectories, as demonstrated in mouse retinal bipolar cells, hematopoiesis, and our newly generated epithelial-to-mesenchymal transition dataset.