• Automatic identification of relevant genes from low-dimensional embeddings of single cell RNAseq data

    12 days ago - By Oxford Journals

    AbstractDimensionality reduction is a key step in the analysis of single-cell RNA sequencing data. It produces a low-dimensional embedding for visualization and as a calculation base for downstream analysis. Nonlinear techniques are most suitable to handle the intrinsic complexity of large, heterogeneous single cell data. However, with no linear relation between gene and embedding coordinate, there is no way to extract the identity of genes driving any cell's position in the low-dimensional embedding, making it more difficult to characterize the underlying biological processes.In this...
    Read more ...