NDLI: Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets

Content Provider	Springer Nature : BioMed Central
Author	Cuevas-Diaz Duran, Raquel Wei, Haichao Wu, Jiaqian
Abstract	Background Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data. Main body The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis. Conclusions According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.
Related Links	https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-024-10364-5.pdf
Ending Page	18
Page Count	18
Starting Page	1
File Format	HTM / HTML
ISSN	14712164
DOI	10.1186/s12864-024-10364-5
Journal	BMC Genomics
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2024-05-06
Access Restriction	Open
Subject Keyword	Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Single-cell sequencing scRNA-seq Normalization Technical variability Biological variability
Content Type	Text
Resource Type	Review
Subject	Biotechnology Genetics
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.1/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

Using DenseFly algorithm for cell searching on massive scRNA-seq datasets

ddSeeker: a tool for processing Bio-Rad ddSEQ single cell RNA-seq data

Comparative transcriptomic analyses of thymocytes using 10x Genomics and Parse scRNA-seq technologies

I-Impute: a self-consistent method to impute single cell RNA sequencing data

Choice of pre-processing pipeline influences clustering quality of scRNA-seq datasets

scReClassify: post hoc cell type classification of single-cell rNA-seq data

Single-cell transcriptomic atlas of the chicken cecum reveals cellular responses and state shifts during Eimeria tenella infection

Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets

Similar Documents

scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

Using DenseFly algorithm for cell searching on massive scRNA-seq datasets

ddSeeker: a tool for processing Bio-Rad ddSEQ single cell RNA-seq data

Comparative transcriptomic analyses of thymocytes using 10x Genomics and Parse scRNA-seq technologies

I-Impute: a self-consistent method to impute single cell RNA sequencing data

Choice of pre-processing pipeline influences clustering quality of scRNA-seq datasets

scReClassify: post hoc cell type classification of single-cell rNA-seq data

Single-cell transcriptomic atlas of the chicken cecum reveals cellular responses and state shifts during Eimeria tenella infection

Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets