Scuphr: A probabilistic framework for cell lineage tree reconstruction.
Hazal KoptagelSeong-Hwan JunJoanna HårdJens LagergrenPublished in: PLoS computational biology (2024)
Cell lineage tree reconstruction methods are developed for various tasks, such as investigating the development, differentiation, and cancer progression. Single-cell sequencing technologies enable more thorough analysis with higher resolution. We present Scuphr, a distance-based cell lineage tree reconstruction method using bulk and single-cell DNA sequencing data from healthy tissues. Common challenges of single-cell DNA sequencing, such as allelic dropouts and amplification errors, are included in Scuphr. Scuphr computes the distance between cell pairs and reconstructs the lineage tree using the neighbor-joining algorithm. With its embarrassingly parallel design, Scuphr can do faster analysis than the state-of-the-art methods while obtaining better accuracy. The method's robustness is investigated using various synthetic datasets and a biological dataset of 18 cells.
Keyphrases
- single cell
- rna seq
- high throughput
- gene expression
- machine learning
- induced apoptosis
- mesenchymal stem cells
- cell death
- mass spectrometry
- deep learning
- nucleic acid
- oxidative stress
- patient safety
- signaling pathway
- young adults
- cell free
- artificial intelligence
- cell therapy
- endoplasmic reticulum stress
- papillary thyroid