Universal Preprocessing of Single-cell Genomics Data

Overview

Journal bioRxiv

Date 2023 Sep 25

PMID 37745572

Authors

A Sina Booeshaghi

Delaney K Sullivan

Lior Pachter

Affiliations

Soon will be listed here.

Abstract

We describe a workflow for preprocessing a wide variety of single-cell genomics data types. The approach is based on parsing of machine-readable assay specifications to customize inputs for , which uses and to catalog reads, error correct barcodes, and count reads. The universal preprocessing method is implemented in the Python package that is available for download at: https://github.com/cellatlas/cellatlas/.

References

Svensson V, Beltrame E, Pachter L . A curated database reveals trends in single-cell transcriptomics. Database (Oxford). 2020; 2020. PMC: 7698659. DOI: 10.1093/database/baaa073. View

Macosko E, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M . Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015; 161(5):1202-1214. PMC: 4481139. DOI: 10.1016/j.cell.2015.05.002. View

Ma S, Zhang B, LaFave L, Earl A, Chiang Z, Hu Y . Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin. Cell. 2020; 183(4):1103-1116.e20. PMC: 7669735. DOI: 10.1016/j.cell.2020.09.056. View

Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay P, Swerdlow H . Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017; 14(9):865-868. PMC: 5669064. DOI: 10.1038/nmeth.4380. View

Battenberg K, Thomas Kelly S, Ras R, Hetherington N, Hayashi M, Minoda A . A flexible cross-platform single-cell data processing pipeline. Nat Commun. 2022; 13(1):6847. PMC: 9652453. DOI: 10.1038/s41467-022-34681-z. View

Xu W, Yang W, Zhang Y, Chen Y, Hong N, Zhang Q . ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. Nat Methods. 2022; 19(10):1243-1249. DOI: 10.1038/s41592-022-01601-4. View

Davis C, Hitz B, Sloan C, Chan E, Davidson J, Gabdank I . The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 2017; 46(D1):D794-D801. PMC: 5753278. DOI: 10.1093/nar/gkx1081. View

He D, Zakeri M, Sarkar H, Soneson C, Srivastava A, Patro R . Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data. Nat Methods. 2022; 19(3):316-322. PMC: 8933848. DOI: 10.1038/s41592-022-01408-3. View

Cao J, Packer J, Ramani V, Cusanovich D, Huynh C, Daza R . Comprehensive single-cell transcriptional profiling of a multicellular organism. Science. 2017; 357(6352):661-667. PMC: 5894354. DOI: 10.1126/science.aam8940. View

10.

Cusanovich D, Daza R, Adey A, Pliner H, Christiansen L, Gunderson K . Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. 2015; 348(6237):910-4. PMC: 4836442. DOI: 10.1126/science.aab1601. View

11.

Shen W, Le S, Li Y, Hu F . SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation. PLoS One. 2016; 11(10):e0163962. PMC: 5051824. DOI: 10.1371/journal.pone.0163962. View

12.

Melsted P, Booeshaghi A, Liu L, Gao F, Lu L, Min K . Modular, efficient and constant-memory single-cell RNA-seq preprocessing. Nat Biotechnol. 2021; 39(7):813-818. DOI: 10.1038/s41587-021-00870-2. View

13.

Tian L, Su S, Dong X, Amann-Zalcenstein D, Biben C, Seidi A . scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data. PLoS Comput Biol. 2018; 14(8):e1006361. PMC: 6105007. DOI: 10.1371/journal.pcbi.1006361. View

14.

Parekh S, Ziegenhain C, Vieth B, Enard W, Hellmann I . zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs. Gigascience. 2018; 7(6). PMC: 6007394. DOI: 10.1093/gigascience/giy059. View

15.

Picelli S, Faridani O, Bjorklund A, Winberg G, Sagasser S, Sandberg R . Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc. 2014; 9(1):171-81. DOI: 10.1038/nprot.2014.006. View

16.

Xu Z, Heidrich-OHare E, Chen W, Duerr R . Comprehensive benchmarking of CITE-seq versus DOGMA-seq single cell multimodal omics. Genome Biol. 2022; 23(1):135. PMC: 9219143. DOI: 10.1186/s13059-022-02698-8. View

17.

Bray N, Pimentel H, Melsted P, Pachter L . Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016; 34(5):525-7. DOI: 10.1038/nbt.3519. View

18.

Rosenberg A, Roco C, Muscat R, Kuchina A, Sample P, Yao Z . Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science. 2018; 360(6385):176-182. PMC: 7643870. DOI: 10.1126/science.aam8999. View

19.

Gehring J, Park J, Chen S, Thomson M, Pachter L . Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins. Nat Biotechnol. 2019; 38(1):35-38. DOI: 10.1038/s41587-019-0372-z. View

20.

McGinnis C, Patterson D, Winkler J, Conrad D, Hein M, Srivastava V . MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. Nat Methods. 2019; 16(7):619-626. PMC: 6837808. DOI: 10.1038/s41592-019-0433-8. View