Exploration of Normalization Methods on Bulk RNA-seq Data and Single-cell RNA-seq Data Open Access

Wang, Yawei (Spring 2021)

Permanent URL: https://etd.library.emory.edu/concern/etds/9s161740d?locale=en


Background: RNA-seq and single-cell RNA-seq are powerful new technologies in biomedical research. To eliminate the inherent technical errors associated with factors like sequencing depth and gene length, RNA-seq data from different samples need to be normalized so that they are comparable. However, the presence of abundant zeros in the data, especially in single-cell RNA-seq data, makes the normalization effect extremely challenging. 


Method and Materials: In the bulk RNA-seq normalization section, I used a novel normalization method, named Group method, and compared its performance with other bulk RNA-seq data normalization methods, Upper Quantile, Quantile, Median, TMM, and DESeq, by calculating Spearman correlation between normalized RNA-seq data and TaqMan qRT-PCR data. We also compared their effectiveness on simulated data and differential expression analysis respectively. For the single-cell RNA-seq part, I merge genes based on the KEGG pathway and use the Quantile method to normalize pathway-cell data, which was named the Pathway-Quantile method. I compared this method with log normalization method, scran, and Linnorm on 3k PBMC data (without spike-in genes) and human pancreas data (with spike-in genes) by using the results after UMAP reducing dimension and Seurat package, version 4.0.1 visualizing. 


Results: For simulated and real bulk RNA-Seq data, all normalization methods performed similarly in terms of the Spearman correlation between normalized real RNA-Seq data and MAQC TaqMan qRT-PCR data. And Group method does not perform better compared to other methods. For differential expression analysis, all methods showed similar performance. For single-cell RNA-seq data, Pathway-Quantile is better than pathway-level data, but its performance was inferior to other methods when test on 3k PBMC data.


Conclusion: We found the group method is competitive for normalizing bulk RNA-seq data. However, more studies are needed for normalizing single-cell RNA-seq data using the Group-Quantile method.

Table of Contents

1.    Introduction 1

2.    Data Source 3

2.1  Bulk RNA-seq data 3

2.1.1 Real data 3

2.1.2 MAQC TaqMan qRT-PCR data 4

2.1.3 simulated data 5

2.2 Single-cell RNA-seq data 5

2.2.1 Peripheral Blood Mononuclear Cell (PBMC) data 5

2.2.2 Single-cell RNA-seq data with spike-ins 5

2.2.3 KEGG pathway gene sets 6

3.    Methods 6

3.1 Bulk RNA-seq data normalization methods 6

3.1.1 Traditional normalization methods with real RNA-Seq data 7

3.1.2 Group method 8

3.1.3 Normalization with simulated data 9

3.1.4 Differential expression analysis  9

3.2 Sing-cell RNA-seq normalization methods10

3.2.1 Log Normalization 10

3.2.2 Linear Model and Normality Based Normalizing Transformation Method (Linnorm) 11

3.2.3 Scran method 12

3.2.4 Group-Quantile method 12

4.    Results12

4.1 Results of bulk RNA-seq data12

4.2 Results of single-cell RNA-seq data16

4.2.1 PBMC data visualization  16

4.2.2 Spike-in single-cell RNA-seq data visualization  17

5.    Conclusion and Discussion18


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