ArticleFrontiers in bioengineering and biotechnology2020
CALISTA: Clustering and LINEAGE Inference in Single-Cell Transcriptional Analysis.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Review
- SCREAM: Single-cell Clustering using Representation Autoencoder of Multiomics.bioRxiv : the preprint server for biology · 2025Article
- Single-cell transcriptional uncertainty landscape of cell differentiation.F1000Research · 2023Article
- Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells.PLoS biology · 2022Article
- Current progress and potential opportunities to infer single-cell developmental trajectory and cell fate.Current opinion in systems biology · 2021Article
- Identifying cell types from single-cell data based on similarities and dissimilarities between cells.BMC bioinformatics · 2021Article
- DUETT quantitatively identifies known and novel events in nascent RNA structural dynamics from chemical probing data.Bioinformatics (Oxford, England) · 2019Article
- Integrating image analysis with single cell RNA-seq data to study podocyte-specific changes in diabetic kidney disease.Proceedings of SPIE--the International Society for Optical EngineeringArticle
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4 authors.
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Abstract
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.
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