NetSmooth: Network-smoothing Based Imputation for Single Cell RNA-seq

Overview

Journal F1000Res

Specialties Biomedical Engineering
Science

Date 2018 Jul 20

PMID 29511531

Citations 32

Authors

Jonathan Ronen

Altuna Akalin

Affiliations

Soon will be listed here.

Abstract

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

Citing Articles

The combined use of scRNA-seq and network propagation highlights key features of pan-cancer Tumor-Infiltrating T cells.

Mangelinck A, Molitor E, Marchiq I, Alaoui L, Bouaziz M, Andrade-Pereira R PLoS One. 2024; 19(12):e0315980.

PMID: 39729479 PMC: 11676858. DOI: 10.1371/journal.pone.0315980.

Single-cell gene set scoring with nearest neighbor graph smoothed data (gssnng).

Gibbs D, Strasser M, Huang S Bioinform Adv. 2023; 3(1):vbad150.

PMID: 37886712 PMC: 10599965. DOI: 10.1093/bioadv/vbad150.

Single cell and bulk transcriptome analysis identified oxidative stress response-related features of Hepatocellular Carcinoma.

Zhang S, Li X, Zheng Y, Liu J, Hu H, Zhang S Front Cell Dev Biol. 2023; 11:1191074.

PMID: 37842089 PMC: 10568628. DOI: 10.3389/fcell.2023.1191074.

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

Yang W, Wang P, Luo M, Cai Y, Xu C, Xue G Bioinformatics. 2023; 39(10).

PMID: 37740953 PMC: 10558043. DOI: 10.1093/bioinformatics/btad596.

Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.

Brendel M, Su C, Bai Z, Zhang H, Elemento O, Wang F Genomics Proteomics Bioinformatics. 2022; 20(5):814-835.

PMID: 36528240 PMC: 10025684. DOI: 10.1016/j.gpb.2022.11.011.

References

Hofree M, Shen J, Carter H, Gross A, Ideker T . Network-based stratification of tumor mutations. Nat Methods. 2013; 10(11):1108-15. PMC: 3866081. DOI: 10.1038/nmeth.2651. View

Edgar R, Domrachev M, Lash A . Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2001; 30(1):207-10. PMC: 99122. DOI: 10.1093/nar/30.1.207. View

Kharchenko P, Silberstein L, Scadden D . Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014; 11(7):740-2. PMC: 4112276. DOI: 10.1038/nmeth.2967. View

Patel A, Tirosh I, Trombetta J, Shalek A, Gillespie S, Wakimoto H . Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014; 344(6190):1396-401. PMC: 4123637. DOI: 10.1126/science.1254257. View

Soneson C, Robinson M . Bias, robustness and scalability in single-cell differential expression analysis. Nat Methods. 2018; 15(4):255-261. DOI: 10.1038/nmeth.4612. View

Fraser H, Hirsh A, Wall D, Eisen M . Coevolution of gene expression among interacting proteins. Proc Natl Acad Sci U S A. 2004; 101(24):9033-8. PMC: 439012. DOI: 10.1073/pnas.0402591101. View

McCarthy D, Campbell K, Lun A, Wills Q . Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics. 2017; 33(8):1179-1186. PMC: 5408845. DOI: 10.1093/bioinformatics/btw777. View

Vandin F, Upfal E, Raphael B . Algorithms for detecting significantly mutated pathways in cancer. J Comput Biol. 2011; 18(3):507-22. DOI: 10.1089/cmb.2010.0265. View

Petryszak R, Keays M, Tang Y, Fonseca N, Barrera E, Burdett T . Expression Atlas update--an integrated database of gene and protein expression in humans, animals and plants. Nucleic Acids Res. 2015; 44(D1):D746-52. PMC: 4702781. DOI: 10.1093/nar/gkv1045. View

10.

Deng Q, Ramskold D, Reinius B, Sandberg R . Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science. 2014; 343(6167):193-6. DOI: 10.1126/science.1245316. View

11.

Nestorowa S, Hamey F, Pijuan Sala B, Diamanti E, Shepherd M, Laurenti E . A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood. 2016; 128(8):e20-31. PMC: 5305050. DOI: 10.1182/blood-2016-05-716480. View

12.

Dorum G, Snipen L, Solheim M, Saebo S . Smoothing gene expression data with network information improves consistency of regulated genes. Stat Appl Genet Mol Biol. 2012; 10(1). DOI: 10.2202/1544-6115.1618. View

13.

Wu A, Neff N, Kalisky T, Dalerba P, Treutlein B, Rothenberg M . Quantitative assessment of single-cell RNA-sequencing methods. Nat Methods. 2013; 11(1):41-6. PMC: 4022966. DOI: 10.1038/nmeth.2694. View

14.

Wagner A, Regev A, Yosef N . Revealing the vectors of cellular identity with single-cell genomics. Nat Biotechnol. 2016; 34(11):1145-1160. PMC: 5465644. DOI: 10.1038/nbt.3711. View

15.

Li W, Li J . An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat Commun. 2018; 9(1):997. PMC: 5843666. DOI: 10.1038/s41467-018-03405-7. View

16.

Robinson M, McCarthy D, Smyth G . edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2009; 26(1):139-40. PMC: 2796818. DOI: 10.1093/bioinformatics/btp616. View

17.

Pierson E, Yau C . ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol. 2015; 16:241. PMC: 4630968. DOI: 10.1186/s13059-015-0805-z. View

18.

Lee I, Blom U, Wang P, Shim J, Marcotte E . Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 2011; 21(7):1109-21. PMC: 3129253. DOI: 10.1101/gr.118992.110. View

19.

Szklarczyk D, Morris J, Cook H, Kuhn M, Wyder S, Simonovic M . The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2016; 45(D1):D362-D368. PMC: 5210637. DOI: 10.1093/nar/gkw937. View

20.

Bhardwaj N, Lu H . Correlation between gene expression profiles and protein-protein interactions within and across genomes. Bioinformatics. 2005; 21(11):2730-8. DOI: 10.1093/bioinformatics/bti398. View