TSCAN: Tools for Single-Cell ANalysis

Single-cell high-throughput transcriptomics is a powerful approach to investigate the heterogeneity of gene expression activities on cell level, which is otherwise hard to detect by bulk transcriptomics experiments. Many of the single-cell transcriptomics researches focus on the differentiation of various cell types and hope to find how the expression of genes and key regulatory factors changes over the differentiation process [1,2]. For individual cells during the differentiation process, each cell may experience quite varying schedule of transcriptional changes even though the overall trajectory of changes is highly identical. To depict such variation between individual cells, it is essential to reconstruct the differentiation lineage and order each single cell to the correct cellular states on the lineage. Several computational methods specifically tailored for single-cell experiments have been proposed to reconstruct the trend of cellular transitions during differentiation. For example, SPADE [3] uses an unsupervised spanning-tree progression approach to organize cells in a hierarchy of related phenotypes when applied