Analysis of Single-Cell RNA-Sequencing Data: A Step-by-Step Guide
Abstract
:1. Introduction
2. Material and Methods
2.1. Data Description
2.2. Raw Data Download
2.3. Data Preprocessing
2.4. Quality Check of Raw Reads
2.5. Extracting the Cell Barcodes, UMIs and Reads
2.6. Mapping to Reference Genome to Obtain Read Counts
2.7. Assigning Reads to Genes
3. Statistical Modeling and Data Analysis
3.1. Mathematical Models for scRNA Count Data
3.1.1. Negative Binomial Model
3.1.2. Zero-Inflated Negative Binomial Model
3.2. Zero-Inflation Analysis
3.3. Clustering
3.4. K-means Clustering
3.5. Determination of the Optimum Number of Clusters
3.6. Differential Gene Expression Analysis
3.7. DESeq2
3.8. DEsingle
3.9. Performance Evaluation of DE Methods
4. Results and Discussion
4.1. Quality Control
4.2. Extracting UMIs, Cell Barcodes and UMIs
4.3. Mapping
4.4. Quantification
4.5. Determining the Distribution of Zeros in Data
4.6. Distribution of Cell Sequencing Depths
4.7. Clustering Analysis
4.8. Study the Effect of Zero’s Reduction on the Determination of Optimum Cell Clusters
4.9. Case 1: No Reduction
4.10. Case 2: Reduction in the Number of Genes when many Cells have Zero Counts
4.11. Differential Expression Analysis
4.12. Evaluating Performance
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Name | Version | Description | Reference |
---|---|---|---|
FastQC | v0.11.9 | FastQ Quality Check | [33] |
UMI-tools | 1.0.0 | Tools for handling Unique Molecular Identifiers | [34] |
Human genome | Grch38/hg 38 | Human genome reference file | [35] |
GTF | Release 35 | Gene Transfer Format | [36] |
STAR | 2.7 | Spliced Transcripts Alignment to a Reference | [37] |
SAM tools | 1.4 | SAMtools software package | [38] |
Subread package | 2.0.1 | The package used by SAMtools | [39] |
Stats R package | 3.6.1 | Package for k-means clustering | [40,41] |
DESeq2 | 1.28.1 | DE analysis tool for RNA-seq | [26] |
DEsingle | 1.8.2 | DE analysis tool for scRNA-seq | [27] |
Case Type | Percentage Reduction | No. of Optimal Clusters | No. of Genes |
---|---|---|---|
Case 1 | No reduction | 10 | 42,406 |
Case 2 | 80% | 10 | 2415 |
Case 2 | 60% | 10 | 1201 |
Case 2 | 50% | 10 | 879 |
Case 2 | 30% | 10 | 454 |
Level of Significance | DEsingle Genes | DESeq2 Genes | Common Genes |
---|---|---|---|
1% | 634 | 79 | 25 |
0.1% | 401 | 75 | 22 |
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Malhotra, A.; Das, S.; Rai, S.N. Analysis of Single-Cell RNA-Sequencing Data: A Step-by-Step Guide. BioMedInformatics 2022, 2, 43-61. https://doi.org/10.3390/biomedinformatics2010003
Malhotra A, Das S, Rai SN. Analysis of Single-Cell RNA-Sequencing Data: A Step-by-Step Guide. BioMedInformatics. 2022; 2(1):43-61. https://doi.org/10.3390/biomedinformatics2010003
Chicago/Turabian StyleMalhotra, Aanchal, Samarendra Das, and Shesh N. Rai. 2022. "Analysis of Single-Cell RNA-Sequencing Data: A Step-by-Step Guide" BioMedInformatics 2, no. 1: 43-61. https://doi.org/10.3390/biomedinformatics2010003
APA StyleMalhotra, A., Das, S., & Rai, S. N. (2022). Analysis of Single-Cell RNA-Sequencing Data: A Step-by-Step Guide. BioMedInformatics, 2(1), 43-61. https://doi.org/10.3390/biomedinformatics2010003