CellBIC is a tool to cluster single cell transcriptomic data into top-down hierarchical clusters using bimodality in the gene expression distribution. CellBIC is implemented in MATLAB.
- run CellBIC_step1 with log-transformed count data and parameters. This function will return a top-down hierarchical clustering result.
- run CellBIC_step2 with the returned variables from CellBIC_step1. THis function will return a clustering result with a given number of clusters.
Four example scripts are available with the corresponding single cell RNA sequencing data as follows:
- RunCellBIC_Enge.m [1]
- RunCellBIC_Treutlein.m [2]
- RunCellBIC_Wang.m [3]
- RunCellBIC_Zeisel.m [4]
Manuscript for CellBIC is available from Nucleic Acids Research [5].
- Enge, M. et al. Single-Cell Analysis of Human Pancreas Reveals Transcriptional Signatures of Aging and Somatic Mutation Patterns. Cell 171, 321–330.e14 (2017).
- Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–5 (2014).
- Wang, Y. J. et al. Single cell transcriptomics of the human endocrine pancreas. Diabetes (2016).
- Zeisel, a. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–42 (2015).
- Kim, J. et al. CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type. Nucelic Acids Research 46 (21), e124-e124 (2018).