Isoform-level quantification for single-cell RNA sequencing.

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However, due to the strong 3' bias sequencing protocol, mRNA quantification for high-throughput single-cell RNA sequencing such as Chromium Single Cell 3' 10× Genomics is currently performed at the gene level.\nRESULTS: We have developed an isoform-level quantification method for high-throughput single-cell RNA sequencing by exploiting the concepts of transcription clusters and isoform paralogs. The method, called Scasa, compares well in simulations against competing approaches including Alevin, Cellranger, Kallisto, Salmon, Terminus and STARsolo at both isoform- and gene-level expression. The reanalysis of a CITE-Seq dataset with isoform-based Scasa reveals a subgroup of CD14 monocytes missed by gene-based methods.\nAVAILABILITY AND IMPLEMENTATION: Implementation of Scasa including source code, documentation, tutorials and test data supporting this study is available at Github: https://github.com/eudoraleer/scasa and Zenodo: https://doi.org/10.5281/zenodo.5712503.\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "The joint estimation procedure is called an AEM algorithm, for which the exact formulas are given in Deng et al. (2020).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34864849", "locator": "sections[4] \"2.2 Statistical model and AEM algorithm\" paragraph 5", "quote": "The joint estimation procedure is called an AEM algorithm, for which the exact formulas are given in Deng et al. (2020)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Supplementary data are available at Bioinformatics online.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34864849", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Supplementary data are available at Bioinformatics online."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Scasa compares well in simulations against competing approaches at both isoform- and gene-level expression and reveals a subgroup of CD14 monocytes missed by gene-based methods.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34864849", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The method, called Scasa, compares well in simulations against competing approaches including Alevin, Cellranger, Kallisto, Salmon, Terminus and STARsolo at both isoform- and gene-level expression."}, {"asset": "source:pmid:34864849", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The reanalysis of a CITE-Seq dataset with isoform-based Scasa reveals a subgroup of CD14 monocytes missed by gene-based methods."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Scasa compares well in simulations against competing approaches at both isoform- and gene-level expression and reveals a subgroup of CD14 monocytes missed by gene-based methods.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34864849"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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