A brief introduction to our project.
We develop a new method combining lineage clustering and generalized additive model (GAM) to infer cell lineages from single-cell data. We also validate our methods on hematopoietic cell lineage and real hepatocellular carcinoma (HCC) samples. You can obtain more information about our methods in results page.
Single-cell pseudotime inference is a powerful tool to study cell differentiation dynamics, as real time data is hard to acquire at single cell resolution. Current popular pseudotime inference methods mostly need supervised clustering and artificially designated differentiation lineage, which is not so convenient for complex data such as tumor.
Meanwhile, it's hard to perform statistical analysis on an artificially designated differentiation lineage (as there is only one lineage). So we hope to devise a new method based on lineage clustering to automatically identified lineages and preserve more paths for statistical analysis.
We use graph pathfinding metrics to find possible paths, hausdorff distance to measure simillarity between paths, and ohter statictics and machine learning methods to analyze paths. More information is available at Results page.
1. Street K, Risso D, Fletcher RB, Das D, Ngai J, Yosef N, Purdom E, Dudoit S. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics. 2018 Jun 19;19(1):477. doi: 10.1186/s12864-018-4772-0. PMID: 29914354; PMCID: PMC6007078.
2. Haghverdi L, Büttner M, Wolf FA, Buettner F, Theis FJ. Diffusion pseudotime robustly reconstructs lineage branching. Nat Methods. 2016 Oct;13(10):845-8. doi: 10.1038/nmeth.3971. Epub 2016 Aug 29. PMID: 27571553.
3. Wolf FA, Hamey FK, Plass M, Solana J, Dahlin JS, Göttgens B, Rajewsky N, Simon L, Theis FJ. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 2019 Mar 19;20(1):59. doi: 10.1186/s13059-019-1663-x. PMID: 30890159; PMCID: PMC6425583.