A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples.
Nat Biotechnol
; 39(9): 1103-1114, 2021 09.
Article
in En
| MEDLINE
| ID: mdl-33349700
ABSTRACT
Comparing diverse single-cell RNA sequencing (scRNA-seq) datasets generated by different technologies and in different laboratories remains a major challenge. Here we address the need for guidance in choosing algorithms leading to accurate biological interpretations of varied data types acquired with different platforms. Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, we compared different scRNA-seq platforms and several preprocessing, normalization and batch-effect correction methods at multiple centers. Although preprocessing and normalization contributed to variability in gene detection and cell classification, batch-effect correction was by far the most important factor in correctly classifying the cells. Moreover, scRNA-seq dataset characteristics (for example, sample and cellular heterogeneity and platform used) were critical in determining the optimal bioinformatic method. However, reproducibility across centers and platforms was high when appropriate bioinformatic methods were applied. Our findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Sequence Analysis, RNA
/
Benchmarking
/
Single-Cell Analysis
Type of study:
Clinical_trials
/
Guideline
Limits:
Female
/
Humans
Language:
En
Journal:
Nat Biotechnol
Journal subject:
BIOTECNOLOGIA
Year:
2021
Document type:
Article
Affiliation country:
United States