{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Choi J"],"funding":["National Research Foundation of Korea"],"pagination":["157"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9063264"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(1)"],"pubmed_abstract":["<h4>Background</h4>Although single-cell RNA sequencing of xenograft samples has been widely used, no comprehensive bioinformatics pipeline is available for human and mouse mixed single-cell analyses. Considering the numerous homologous genes across the human and mouse genomes, misalignment errors should be evaluated, and a new algorithm is required. We assessed the extents and effects of misalignment errors and exonic multi-mapping events when using human and mouse combined reference data and developed a new bioinformatics pipeline with expression-based species deconvolution to minimize errors. We also evaluated false-positive signals presumed to originate from ambient RNA of the other species and address the importance to computationally remove them.<h4>Result</h4>Error when using combined reference account for an average of 0.78% of total reads, but such reads were concentrated to few genes that were greatly affected. Human and mouse mixed single-cell data, analyzed using our pipeline, clustered well with unmixed data and showed higher k-nearest-neighbor batch effect test and Local Inverse Simpson's Index scores than those derived from Cell Ranger (10 × Genomics). We also applied our pipeline to multispecies multisample single-cell library containing breast cancer xenograft tissue and successfully identified all samples using genomic array and expression. Moreover, diverse cell types in the tumor microenvironment were well captured.<h4>Conclusion</h4>We present our bioinformatics pipeline for mixed human and mouse single-cell data, which can also be applied to pooled libraries to obtain cost-effective single-cell data. We also address misalignment, multi-mapping error, and ambient RNA as a major consideration points when analyzing multispecies single-cell data."],"journal":["BMC bioinformatics"],"pubmed_title":["Expression-based species deconvolution and realignment removes misalignment error in multispecies single-cell data."],"pmcid":["PMC9063264"],"funding_grant_id":["2020R1A2C3012524"],"pubmed_authors":["Lee CH","Kim JI","Moon HG","Choi J","Cho S","Yang HK","Lee W","Choi SM","Chung JH","Yoon JK"],"additional_accession":[]},"is_claimable":false,"name":"Expression-based species deconvolution and realignment removes misalignment error in multispecies single-cell data.","description":"<h4>Background</h4>Although single-cell RNA sequencing of xenograft samples has been widely used, no comprehensive bioinformatics pipeline is available for human and mouse mixed single-cell analyses. Considering the numerous homologous genes across the human and mouse genomes, misalignment errors should be evaluated, and a new algorithm is required. We assessed the extents and effects of misalignment errors and exonic multi-mapping events when using human and mouse combined reference data and developed a new bioinformatics pipeline with expression-based species deconvolution to minimize errors. We also evaluated false-positive signals presumed to originate from ambient RNA of the other species and address the importance to computationally remove them.<h4>Result</h4>Error when using combined reference account for an average of 0.78% of total reads, but such reads were concentrated to few genes that were greatly affected. Human and mouse mixed single-cell data, analyzed using our pipeline, clustered well with unmixed data and showed higher k-nearest-neighbor batch effect test and Local Inverse Simpson's Index scores than those derived from Cell Ranger (10 × Genomics). We also applied our pipeline to multispecies multisample single-cell library containing breast cancer xenograft tissue and successfully identified all samples using genomic array and expression. Moreover, diverse cell types in the tumor microenvironment were well captured.<h4>Conclusion</h4>We present our bioinformatics pipeline for mixed human and mouse single-cell data, which can also be applied to pooled libraries to obtain cost-effective single-cell data. We also address misalignment, multi-mapping error, and ambient RNA as a major consideration points when analyzing multispecies single-cell data.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 May","modification":"2025-05-18T12:44:10.539Z","creation":"2025-05-18T12:44:10.539Z"},"accession":"S-EPMC9063264","cross_references":{"pubmed":["35501695"],"doi":["10.1186/s12859-022-04676-0"]}}