Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data.
Rujia DaiMing ZhangTianyao ChuRichard KoppChunling ZhangKefu LiuYue WangXusheng WangChao ChenYanling LiuPublished in: bioRxiv : the preprint server for biology (2024)
Single-cell/nuclei RNA sequencing (sc/snRNA-Seq) is widely used for profiling cell-type gene expressions in biomedical research. An important but underappreciated issue is the quality of sc/snRNA-Seq data that would impact the reliability of downstream analyses. Here we evaluated the precision and accuracy in 18 sc/snRNA-Seq datasets. The precision was assessed on data from human brain studies with a total of 3,483,905 cells from 297 individuals, by utilizing technical replicates. The accuracy was evaluated with sample-matched scRNA-Seq and pooled-cell RNA-Seq data of cultured mononuclear phagocytes from four species. The results revealed low precision and accuracy at the single-cell level across all evaluated data. Cell number and RNA quality were highlighted as two key factors determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-Seq. This study underscores the necessity of sequencing enough high-quality cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification.
Keyphrases
- single cell
- rna seq
- high throughput
- electronic health record
- big data
- poor prognosis
- stem cells
- machine learning
- induced apoptosis
- data analysis
- air pollution
- cell death
- randomized controlled trial
- deep learning
- cell cycle arrest
- gene expression
- copy number
- long non coding rna
- endoplasmic reticulum stress
- signaling pathway
- dna methylation
- cell therapy
- bone marrow