scDown: A Pipeline for Single-Cell RNA-Seq Downstream Analysis.
| Title: | scDown: A Pipeline for Single-Cell RNA-Seq Downstream Analysis. |
|---|---|
| Authors: | Sun L; Research Informatics, Department of Information Technology, Boston Children's Hospital, Boston, MA 02115, USA.; Ma Q; Research Informatics, Department of Information Technology, Boston Children's Hospital, Boston, MA 02115, USA.; Cai C; Research Informatics, Department of Information Technology, Boston Children's Hospital, Boston, MA 02115, USA.; Labaf M; Research Informatics, Department of Information Technology, Boston Children's Hospital, Boston, MA 02115, USA.; Jain A; Research Informatics, Department of Information Technology, Boston Children's Hospital, Boston, MA 02115, USA.; Dias C; Department of Pediatrics, Section of Developmental Pediatrics, Section of Genetics and Metabolism, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.; Rockowitz S; Research Informatics, Department of Information Technology, Boston Children's Hospital, Boston, MA 02115, USA.; Children's Rare Disease Collaborative, Boston Children's Hospital, Boston, MA 02115, USA.; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA.; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.; The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA 02115, USA.; Sliz P; Research Informatics, Department of Information Technology, Boston Children's Hospital, Boston, MA 02115, USA.; Children's Rare Disease Collaborative, Boston Children's Hospital, Boston, MA 02115, USA.; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.; The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA 02115, USA.; Division of Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA.; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA. |
| Source: | International journal of molecular sciences [Int J Mol Sci] 2025 May 30; Vol. 26 (11). Date of Electronic Publication: 2025 May 30. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101092791 Publication Model: Electronic Cited Medium: Internet ISSN: 1422-0067 (Electronic) Linking ISSN: 14220067 NLM ISO Abbreviation: Int J Mol Sci Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Basel, Switzerland : MDPI, [2000- |
| MeSH Terms: | Single-Cell Analysis*/methods ; RNA-Seq*/methods ; Sequence Analysis, RNA*/methods ; Software*; Gene Expression Profiling/methods ; Computational Biology/methods ; Cell Differentiation/genetics ; Cell Communication/genetics ; Humans ; Transcriptome ; Animals ; Single-Cell Gene Expression Analysis |
| Abstract: | Single-cell transcriptomics data are analyzed using two popular tools, Seurat and Scanpy. Multiple separate tools are used downstream of Seurat and Scanpy cell annotation to study cell differentiation and communication, including cell proportion difference analysis between conditions, pseudotime and trajectory analyses to study cell transition, and cell-cell communication analysis. To automate the integrative cell differentiation and communication analyses of single-cell RNA-seq data, we developed a single-cell RNA-seq downstream analysis pipeline called "scDown". This R package includes cell proportion difference analysis, cell-cell communication analysis, pseudotime analysis, and RNA velocity analysis. Both Seurat and Scanpy annotated single-cell RNA-seq data are accepted in this pipeline. We applied scDown to a published dataset and identified a unique, previously undiscovered signature of neuronal inflammatory signaling associated with a rare genetic neurodevelopmental disorder. These findings were not identified with a simple implementation of Seurat differential gene expression analysis, illustrating the value of our pipeline in biological discovery. scDown can be broadly utilized in downstream analyses of scRNA-seq data, particularly in rare diseases. |
| References: | Nat Immunol. 2019 Feb;20(2):163-172. (PMID: 30643263); Nat Biotechnol. 2020 Dec;38(12):1408-1414. (PMID: 32747759); Cancer Res. 2021 Jul 15;81(14):3791-3805. (PMID: 34035083); Nat Commun. 2020 Oct 6;11(1):5011. (PMID: 33024107); Nat Protoc. 2025 Jan;20(1):180-219. (PMID: 39289562); Nat Methods. 2017 Oct;14(10):979-982. (PMID: 28825705); Nat Commun. 2017 Jun 01;8:15599. (PMID: 28569836); Cell. 2022 Feb 17;185(4):690-711.e45. (PMID: 35108499); Bioinformatics. 2020 Feb 1;36(3):964-965. (PMID: 31400197); Nat Commun. 2020 Nov 30;11(1):6077. (PMID: 33257685); Nat Protoc. 2020 Apr;15(4):1484-1506. (PMID: 32103204); Nat Commun. 2021 Oct 7;12(1):5890. (PMID: 34620862); BMC Genomics. 2018 Jun 19;19(1):477. (PMID: 29914354); Nat Biotechnol. 2024 May;42(5):803-812. (PMID: 37592035); Genome Biol. 2024 Jan 19;25(1):27. (PMID: 38243313); Nat Biotechnol. 2024 May;42(5):758-767. (PMID: 37414936); Nature. 2018 Aug;560(7719):494-498. (PMID: 30089906); Nat Biotechnol. 2019 Apr;37(4):451-460. (PMID: 30899105); Nat Commun. 2021 Nov 25;12(1):6876. (PMID: 34824236); J Neuroinflammation. 2025 Jan 23;22(1):15. (PMID: 39849490); Nat Methods. 2020 Feb;17(2):159-162. (PMID: 31819264); Nat Methods. 2019 Dec;16(12):1289-1296. (PMID: 31740819); Cell. 2021 Jun 24;184(13):3573-3587.e29. (PMID: 34062119); Mol Syst Biol. 2021 Aug;17(8):e10282. (PMID: 34435732); Oncogene. 2023 Jun;42(27):2155-2165. (PMID: 37277603); Nat Commun. 2021 Feb 17;12(1):1088. (PMID: 33597522); Nat Biotechnol. 2018 Jun;36(5):411-420. (PMID: 29608179); Am J Hum Genet. 2024 Aug 8;111(8):1544-1558. (PMID: 39079538); BMC Genomics. 2024 Apr 24;25(1):402. (PMID: 38658838); Nat Biotechnol. 2014 Apr;32(4):381-386. (PMID: 24658644); Front Immunol. 2021 Mar 30;12:595811. (PMID: 33859634); Nature. 2019 Feb;566(7745):496-502. (PMID: 30787437); Genome Biol. 2019 Mar 19;20(1):59. (PMID: 30890159); Mol Autism. 2020 May 11;11(1):32. (PMID: 32393347); Nucleic Acids Res. 2020 Jun 4;48(10):e55. (PMID: 32196115); Genes Brain Behav. 2003 Oct;2(5):255-67. (PMID: 14606691) |
| Contributed Indexing: | Keywords: cell proportion difference analysis; cell–cell communication; pseudotime analysis; single-cell transcriptomics; trajectory analysis |
| Entry Date(s): | Date Created: 20250613 Date Completed: 20250613 Latest Revision: 20250615 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC12154318 |
| DOI: | 10.3390/ijms26115297 |
| PMID: | 40508102 |
| Database: | MEDLINE |
Journal Article