Chapter 1 Why Single-Cell Analysis?

Single-cell sequencing was selected as the method of the year in 2013 by Nature. (2014) Ever since then, besides of the methodology and the algorithms, the popularity of the single-cell sequencing improved. However, why do we need to use a sequencing method focused on a single cell when we can have bulk sequencing data?

FIGURE 1.1: 10X Genomics Molecular Profiling of Single-Cell vs Bulk Sequencing (2019)

The short answer is the limitations of bulk sequencing. If you want to analyze the immune response profiles of different patients using blood samples, using bulk sequencing data may not be beneficial. Blood is a complex mixture of various cell types. The bulk sequencing averages all cells within the sample. As a result, the data you have will be the average expression profile of all cells in the sample. Single-cell sequencing technology allows profiling different cells in samples. In Table 1, the comparison between bulk and single-cell sequencing was given.

TABLE 1.1: Properties of Bulk vs Single-Cell Sequencing

Bulk Sequencing	Single-Cell Sequencing
Average gene expression from all cells	Each cell acts as a unique sample
Average might not be representative	Rare cell types can be lost
Cellular heterogeneity is masked	Resolves cellular heterogeneity
Data is dense	Data can be sparse and noisy
Is a more robost technology	Increases statistical power
Cell aggregates	Can be costly

References

10xGenomics. 2019. “Single-Cell RNA-Seq: An Introductory Overview and Tools for Getting Started. Retrieved 06-08-2019. https://community.10xgenomics.com/t5/10x-Blog/Single-Cell-RNA-Seq-An-Introductory-Overview-and-Tools-for/ba-p/547.” 10xGenomics, 5–9. https://www.10xgenomics.com/blog/single-cell-rna-seq-an-introductory-overview-and-tools-for-getting-started.

“Method of the Year 2013.” 2014. Nature Publishing Group. https://doi.org/10.1038/nmeth.2801.