NDLI: SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

Please wait, while we are loading the content...

SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

Content Provider	Oxford Academic
Author	Dong, Meichen Thennavan, Aatish Urrutia, Eugene Li, Yun Perou, Charles M Zou, Fei Jiang, Yuchao
Copyright Year	2021
Abstract	Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.
Related Links	https://academic.oup.com/bib/article-pdf/22/1/416/35934954/bbz166.pdf
Ending Page	427
Starting Page	416
File Format	PDF
ISSN	14675463
e-ISSN	14774054
DOI	10.1093/bib/bbz166
Journal	Briefings in Bioinformatics
Issue Number	1
Volume Number	22
Language	English
Publisher	Oxford Academic
Publisher Date	2021-01-18
Access Restriction	Open
Subject Keyword	Bioinformatics and Computational Biology Biological Sciences Science and Mathematics Single-cell Rna Sequencing Bulk Rna Sequencing Gene Expression Deconvolution Ensemble Batch Effect
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Information Systems

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

SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

Comprehensive evaluation of noise reduction methods for single-cell RNA sequencing data

Tumor immune microenvironment lncRNAs

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes

Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM

Sincast: a computational framework to predict cell identities in single-cell transcriptomes using bulk atlases as references

Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data

A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data

FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets

SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

Similar Documents

SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

Comprehensive evaluation of noise reduction methods for single-cell RNA sequencing data

Tumor immune microenvironment lncRNAs

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes

Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM

Sincast: a computational framework to predict cell identities in single-cell transcriptomes using bulk atlases as references

Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data

A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data

FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets

SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references