CellMarkerPipe: Cell Marker Identification and Evaluation Pipeline in Single Cell Transcriptomes

Overview

Journal Res Sq

Date 2024 Feb 5

PMID 38313296

Authors

Qiuming Yao

Yinglu Jia

Pengchong Ma

Affiliations

Soon will be listed here.

Abstract

Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe (https://github.com/yao-laboratory/cellMarkerPipe), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker's overall reliable performance in single marker gene selection, with COSG showing commendable speed and comparable efficacy. Furthermore, we demonstrate the pivotal role of our approach in real-world medical datasets. This general and opensource pipeline stands as a significant advancement in streamlining cell marker gene identification and evaluation, fitting broad applications in the field of cellular biology and medical research.

References

Delaney C, Schnell A, Cammarata L, Yao-Smith A, Regev A, Kuchroo V . Combinatorial prediction of marker panels from single-cell transcriptomic data. Mol Syst Biol. 2019; 15(10):e9005. PMC: 6811728. DOI: 10.15252/msb.20199005. View

Melms J, Biermann J, Huang H, Wang Y, Nair A, Tagore S . A molecular single-cell lung atlas of lethal COVID-19. Nature. 2021; 595(7865):114-119. PMC: 8814825. DOI: 10.1038/s41586-021-03569-1. View

Yip S, Sham P, Wang J . Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data. Brief Bioinform. 2018; 20(4):1583-1589. PMC: 6781572. DOI: 10.1093/bib/bby011. View

Ahlmann-Eltze C, Huber W . Comparison of transformations for single-cell RNA-seq data. Nat Methods. 2023; 20(5):665-672. PMC: 10172138. DOI: 10.1038/s41592-023-01814-1. View

Haber A, Biton M, Rogel N, Herbst R, Shekhar K, Smillie C . A single-cell survey of the small intestinal epithelium. Nature. 2017; 551(7680):333-339. PMC: 6022292. DOI: 10.1038/nature24489. View

Zheng G, Terry J, Belgrader P, Ryvkin P, Bent Z, Wilson R . Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017; 8:14049. PMC: 5241818. DOI: 10.1038/ncomms14049. View

Zeisel A, Munoz-Manchado A, Codeluppi S, Lonnerberg P, La Manno G, Jureus A . Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science. 2015; 347(6226):1138-42. DOI: 10.1126/science.aaa1934. View

Wang F, Liang S, Kumar T, Navin N, Chen K . SCMarker: Ab initio marker selection for single cell transcriptome profiling. PLoS Comput Biol. 2019; 15(10):e1007445. PMC: 6837541. DOI: 10.1371/journal.pcbi.1007445. View

Grun D, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N . Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature. 2015; 525(7568):251-5. DOI: 10.1038/nature14966. View

10.

Ianevski A, Giri A, Aittokallio T . Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat Commun. 2022; 13(1):1246. PMC: 8913782. DOI: 10.1038/s41467-022-28803-w. View

11.

Rozenblatt-Rosen O, Shin J, Rood J, Hupalowska A, Regev A, Heyn H . Building a high-quality Human Cell Atlas. Nat Biotechnol. 2021; 39(2):149-153. DOI: 10.1038/s41587-020-00812-4. View

12.

Xiang R, Wang W, Yang L, Wang S, Xu C, Chen X . A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data. Front Genet. 2021; 12:646936. PMC: 8021860. DOI: 10.3389/fgene.2021.646936. View

13.

Franzen O, Gan L, Bjorkegren J . PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database (Oxford). 2019; 2019. PMC: 6450036. DOI: 10.1093/database/baz046. View

14.

Kiselev V, Kirschner K, Schaub M, Andrews T, Yiu A, Chandra T . SC3: consensus clustering of single-cell RNA-seq data. Nat Methods. 2017; 14(5):483-486. PMC: 5410170. DOI: 10.1038/nmeth.4236. View

15.

Wang Y, Song W, Wang J, Wang T, Xiong X, Qi Z . Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. 2019; 217(2). PMC: 7041720. DOI: 10.1084/jem.20191130. View

16.

Pullin J, McCarthy D . A comparison of marker gene selection methods for single-cell RNA sequencing data. Genome Biol. 2024; 25(1):56. PMC: 10895860. DOI: 10.1186/s13059-024-03183-0. View

17.

Wolf F, Angerer P, Theis F . SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018; 19(1):15. PMC: 5802054. DOI: 10.1186/s13059-017-1382-0. View

18.

Hao Y, Hao S, Andersen-Nissen E, Mauck 3rd W, Zheng S, Butler A . Integrated analysis of multimodal single-cell data. Cell. 2021; 184(13):3573-3587.e29. PMC: 8238499. DOI: 10.1016/j.cell.2021.04.048. View

19.

Wu Z, Wu H . Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering. Genome Biol. 2020; 21(1):123. PMC: 7249323. DOI: 10.1186/s13059-020-02027-x. View

20.

Fawkner-Corbett D, Antanaviciute A, Parikh K, Jagielowicz M, Geros A, Gupta T . Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell. 2021; 184(3):810-826.e23. PMC: 7864098. DOI: 10.1016/j.cell.2020.12.016. View