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NDRindex: a method for the quality assessment of single-cell RNA-Seq preprocessing data
Ruiyu Xiao1, Guoshan Lu1, Wanqian Guo2
1School of Computer Science and Technology, Harbin Institute of Technology, Zhejiang, China.
BMC Bioinformatics
|December 16, 2020
Summary
A new index, NDRindex, evaluates single-cell RNA sequencing data quality after normalization and dimensionality reduction. This method ensures accurate cell type identification, crucial for medical research like COVID-19 studies.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Single-cell RNA sequencing (scRNA-Seq) is vital for cell type determination, with applications in COVID-19 research.
- scRNA-Seq data analysis involves normalization, dimensionality reduction, and clustering.
- Preprocessing choices significantly impact clustering and cell type enrichment analysis accuracy.
Purpose of the Study:
- To develop a method for evaluating the quality of scRNA-Seq data preprocessing.
- To address the critical need for standardized preprocessing path selection in scRNA-Seq data mining.
Main Methods:
- Introduction of the Normalization and Dimensionality Reduction index (NDRindex).
- Inclusion of a function to quantify data aggregation, a key metric for pre-clustering data quality assessment.
Main Results:
- The NDRindex was tested on five scRNA-Seq datasets.
- Results demonstrated the efficacy and accuracy of the NDRindex in evaluating data quality.
Conclusions:
- The NDRindex aids in selecting optimal preprocessing paths for scRNA-Seq data.
- This method provides valuable indicators for RNA-Seq data quality assessment, enhancing downstream analysis.
