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[期刊论文]

scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data

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author:

Ye, Wenbin (Ye, Wenbin.) [1] | Ji, Guoli (Ji, Guoli.) [2] | Ye, Pengchao (Ye, Pengchao.) [3] | Unfold

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Scopus SCIE

Abstract:

BackgroundSingle-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful tool for profiling genome-scale transcriptomes of individual cells and capturing transcriptome-wide cell-to-cell variability. However, scRNA-seq technologies suffer from high levels of technical noise and variability, hindering reliable quantification of lowly and moderately expressed genes. Since most downstream analyses on scRNA-seq, such as cell type clustering and differential expression analysis, rely on the gene-cell expression matrix, preprocessing of scRNA-seq data is a critical preliminary step in the analysis of scRNA-seq data.ResultsWe presented scNPF, an integrative scRNA-seq preprocessing framework assisted by network propagation and network fusion, for recovering gene expression loss, correcting gene expression measurements, and learning similarities between cells. scNPF leverages the context-specific topology inherent in the given data and the priori knowledge derived from publicly available molecular gene-gene interaction networks to augment gene-gene relationships in a data driven manner. We have demonstrated the great potential of scNPF in scRNA-seq preprocessing for accurately recovering gene expression values and learning cell similarity networks. Comprehensive evaluation of scNPF across a wide spectrum of scRNA-seq data sets showed that scNPF achieved comparable or higher performance than the competing approaches according to various metrics of internal validation and clustering accuracy. We have made scNPF an easy-to-use R package, which can be used as a versatile preprocessing plug-in for most existing scRNA-seq analysis pipelines or tools.ConclusionsscNPF is a universal tool for preprocessing of scRNA-seq data, which jointly incorporates the global topology of priori interaction networks and the context-specific information encapsulated in the scRNA-seq data to capture both shared and complementary knowledge from diverse data sources. scNPF could be used to recover gene signatures and learn cell-to-cell similarities from emerging scRNA-seq data to facilitate downstream analyses such as dimension reduction, cell type clustering, and visualization.

Keyword:

Cell type clustering Dropout imputation Network propagation Similarity measurement Single cell RNA-sequencing

Community:

  • [ 1 ] [Ye, Wenbin]Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China
  • [ 2 ] [Ji, Guoli]Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China
  • [ 3 ] [Ye, Pengchao]Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China
  • [ 4 ] [Xiao, Xuesong]Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China
  • [ 5 ] [Li, Shuchao]Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China
  • [ 6 ] [Wu, Xiaohui]Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China
  • [ 7 ] [Long, Yuqi]China Elect Prod Reliabil & Environm Testing Res, Software Qual Testing Engn Res Ctr, Guangzhou 510610, Guangdong, Peoples R China
  • [ 8 ] [Su, Yaru]Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350116, Fujian, Peoples R China
  • [ 9 ] [Ye, Wenbin]Natl Ctr Healthcare Big Data, Xiamen Res Inst, Xiamen, Peoples R China
  • [ 10 ] [Ji, Guoli]Natl Ctr Healthcare Big Data, Xiamen Res Inst, Xiamen, Peoples R China
  • [ 11 ] [Ye, Pengchao]Natl Ctr Healthcare Big Data, Xiamen Res Inst, Xiamen, Peoples R China
  • [ 12 ] [Xiao, Xuesong]Natl Ctr Healthcare Big Data, Xiamen Res Inst, Xiamen, Peoples R China
  • [ 13 ] [Li, Shuchao]Natl Ctr Healthcare Big Data, Xiamen Res Inst, Xiamen, Peoples R China
  • [ 14 ] [Wu, Xiaohui]Natl Ctr Healthcare Big Data, Xiamen Res Inst, Xiamen, Peoples R China
  • [ 15 ] [Ji, Guoli]Xiamen Univ, Innovat Ctr Cell Biol, Xiamen 361005, Peoples R China
  • [ 16 ] [Wu, Xiaohui]Xiamen Univ, Innovat Ctr Cell Biol, Xiamen 361005, Peoples R China

Reprint 's Address:

  • [Wu, Xiaohui]Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China;;[Wu, Xiaohui]Natl Ctr Healthcare Big Data, Xiamen Res Inst, Xiamen, Peoples R China;;[Wu, Xiaohui]Xiamen Univ, Innovat Ctr Cell Biol, Xiamen 361005, Peoples R China

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Source :

BMC GENOMICS

ISSN: 1471-2164

Year: 2019

Volume: 20

3 . 5 9 4

JCR@2019

3 . 5 0 0

JCR@2023

ESI Discipline: MOLECULAR BIOLOGY & GENETICS;

ESI HC Threshold:291

JCR Journal Grade:2

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count:

30 Days PV: 0

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