.png){kind=icon}
Return
Current best practices in single-cell RNA-seq analysis: a tutorial
.png){kind=icon}
OA
AI
DOI：10.15252/msb.20188746.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up-to-date workflow to analyse one's data. Here, we detail the steps of a typical single-cell RNA-seq analysis, including pre-processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell- and gene-level downstream analysis. We formulate current best-practice recommendations for these steps based on independent comparison studies. We have integrated these best-practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at . This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.
Keywords:
analysis pipeline development
computational biology
data analysis tutorial
single-cell RNA-seq
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.7
Papers:
1.6K
Citations:
1.0W
Organization
Cited Papers
ShareSaveBioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists.png)
NUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSaveSensitive detection of rare disease-associated cell subsets via representation learningNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveSlingshot: cell lineage and pseudotime inference for single-cell transcriptomicsBMC GENOMICSOAAI
BMC GENOMICSIF3.7
OAAIShareSaveSingle-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 DiabetesCELL METABOLISMOAAI
CELL METABOLISMIF30.9
OAAIShareSaveShareSaveThe dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cellsNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveMultiplexed droplet single-cell RNA-sequencing using natural genetic variationNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSave
View more