Video_1_CALISTA: Clustering and LINEAGE Inference in Single-Cell Transcriptional Analysis.MP4
We present Clustering and Lineage Inference in Single-Cell Transcriptional Analysis (CALISTA), a numerically efficient and highly scalable toolbox for an end-to-end analysis of single-cell transcriptomic profiles. CALISTA includes four essential single-cell analyses for cell differentiation studies, including single-cell clustering, reconstruction of cell lineage specification, transition gene identification, and cell pseudotime ordering, which can be applied individually or in a pipeline. In these analyses, we employ a likelihood-based approach where single-cell mRNA counts are described by a probabilistic distribution function associated with stochastic gene transcriptional bursts and random technical dropout events. We illustrate the efficacy of CALISTA using single-cell gene expression datasets from different single-cell transcriptional profiling technologies and from a few hundreds to tens of thousands of cells. CALISTA is freely available on https://www.cabselab.com/calista.
History
Usage metrics
Categories
- Bioprocessing, Bioproduction and Bioproducts
- Industrial Biotechnology Diagnostics (incl. Biosensors)
- Industrial Microbiology (incl. Biofeedstocks)
- Industrial Molecular Engineering of Nucleic Acids and Proteins
- Industrial Biotechnology not elsewhere classified
- Medical Biotechnology Diagnostics (incl. Biosensors)
- Biological Engineering
- Regenerative Medicine (incl. Stem Cells and Tissue Engineering)
- Medical Biotechnology not elsewhere classified
- Agricultural Marine Biotechnology
- Biomaterials
- Biomechanical Engineering
- Biotechnology
- Biomarkers
- Biomedical Engineering not elsewhere classified
- Genetic Engineering
- Synthetic Biology
- Bioremediation
- Medical Molecular Engineering of Nucleic Acids and Proteins