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统计学还是生物学：关于 scRNA-seq 数据的零膨胀争议。

Statistics or biology: the zero-inflation controversy about scRNA-seq data.

机构信息

Department of Statistics, University of California, Los Angeles, 90095-1554, CA, USA.

Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, 90095-7246, CA, USA.

出版信息

Genome Biol. 2022 Jan 21;23(1):31. doi: 10.1186/s13059-022-02601-5.

DOI:10.1186/s13059-022-02601-5
PMID:35063006
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8783472/
Abstract

Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.

摘要

研究人员对单细胞 RNA-seq 数据中的大量零值有不同的看法：一些人将零值视为代表无或低基因表达的生物学信号，而另一些人则将零值视为缺失数据进行校正。为帮助解决争议，我们在此讨论了生物学和非生物学零值的来源；介绍了在计算基准测试中添加非生物学零值的五种机制；评估了非生物学零值对数据分析的影响；基准测试了三种输入数据类型：观测计数、推断计数和二值化计数；讨论了关于非生物学零值的悬而未决的问题；并倡导透明分析的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/228cfc490834/13059_2022_2601_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/a17a2e81bed5/13059_2022_2601_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/066facd315fe/13059_2022_2601_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/569657b91ab2/13059_2022_2601_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/f6284db5ac87/13059_2022_2601_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/3bac0f91fefa/13059_2022_2601_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/a8c1e9bc0ad3/13059_2022_2601_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/228cfc490834/13059_2022_2601_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/a17a2e81bed5/13059_2022_2601_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/066facd315fe/13059_2022_2601_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/569657b91ab2/13059_2022_2601_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/f6284db5ac87/13059_2022_2601_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/3bac0f91fefa/13059_2022_2601_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/a8c1e9bc0ad3/13059_2022_2601_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93a/8783472/228cfc490834/13059_2022_2601_Fig7_HTML.jpg

相似文献

[1]
Statistics or biology: the zero-inflation controversy about scRNA-seq data.

Genome Biol. 2022-1-21

[2]
Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics.

Genome Biol. 2020-7-27

[3]
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J Chem Inf Model. 2025-3-10

[4]
Missing data and technical variability in single-cell RNA-sequencing experiments.

Biostatistics. 2018-10-1

[5]
Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation.

BMC Bioinformatics. 2022-6-17

[6]
FRMC: a fast and robust method for the imputation of scRNA-seq data.

RNA Biol. 2021-10-15

[7]
scRecover: Discriminating True and False Zeros in Single-Cell RNA-Seq Data for Imputation.

Stat Med. 2025-2-28

[8]
DEsingle for detecting three types of differential expression in single-cell RNA-seq data.

Bioinformatics. 2018-9-15

[9]
CPARI: a novel approach combining cell partitioning with absolute and relative imputation to address dropout in single-cell RNA-seq data.

Brief Bioinform. 2024-11-22

[10]
Are dropout imputation methods for scRNA-seq effective for scHi-C data?

Brief Bioinform. 2021-7-20

引用本文的文献

[1]
BAYESIAN DIFFERENTIAL CAUSAL DIRECTED ACYCLIC GRAPHS FOR OBSERVATIONAL ZERO-INFLATED COUNTS WITH AN APPLICATION TO TWO-SAMPLE SINGLE-CELL DATA.

Ann Appl Stat. 2025-9

[2]
DropDAE: Denosing Autoencoder with Contrastive Learning for Addressing Dropout Events in scRNA-seq Data.

Bioengineering (Basel). 2025-7-31

[3]
scCOSMIX: A Mixed-Effects Framework for Differential Coexpression and Transcriptional Interactions Modeling in Single-Cell RNA-Seq.

Stat Med. 2025-8

[4]
Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data.

BMC Bioinformatics. 2025-7-29

[5]
Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data.

Brief Bioinform. 2025-5-1

[6]
Single-cell eQTL analysis identifies genetic variation underlying metabolic dysfunction-associated steatohepatitis.

Nat Genet. 2025-6-25

[7]
QuadST identifies cell-cell interaction-changed genes in spatially resolved transcriptomics data.

Genome Res. 2025-8-1

[8]
Fusion of spatiotemporal and network models to prioritize multiscale effects in single-cell perturbations.

Brief Bioinform. 2025-5-1

[9]
Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.

Genomics Inform. 2025-5-17

[10]
scPRINT: pre-training on 50 million cells allows robust gene network predictions.

Nat Commun. 2025-4-16

本文引用的文献

[1]
Clipper: p-value-free FDR control on high-throughput data from two conditions.

Genome Biol. 2021-10-11

[2]
scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured.

Genome Biol. 2021-5-25

[3]
Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis.

Nat Genet. 2021-6

[4]
A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples.

Nat Biotechnol. 2021-9

[5]
Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data.

Nat Protoc. 2021-1

[6]
Naught all zeros in sequence count data are the same.

Comput Struct Biotechnol J. 2020-9-28

[7]
SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks.

Cell Syst. 2020-9-23

[8]
A systematic evaluation of single-cell RNA-sequencing imputation methods.

Genome Biol. 2020-8-27

[9]
Demystifying "drop-outs" in single-cell UMI data.

Genome Biol. 2020-8-6

[10]
DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning.

Genome Biol. 2020-7-10

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