Single-cell RNA-seq

Why single-cell RNA-seq

Across human tissues there is an incredible diversity of cell types, states, and interactions. To better understand these tissues and the cell types present, single-cell RNA-seq (scRNA-seq) offers a glimpse into what genes are being expressed at the level of individual cells.

Image credit: courtesy of Dr. Ayshwarya Subramanian

This exciting and cutting-edge method can be used to:

Popular methods to address some of the more common investigations include:

Challenges of scRNA-seq analysis

Prior to scRNA-seq, transcriptome analysis was performed using bulk RNA-seq, which is a straight-forward method for comparing the averages of cellular expression. This can be the best choice of method if looking for disease biomarkers or not expecting or not concerned with a lot of cellular heterogeneity in the sample.

While bulk RNA-seq can explore differences in gene expression between conditions (e.g. treatment or disease), the differences at the cellular level are not adequately captured. For instance, in the images below, if analyzed in bulk (left) we would not detect the correct association between the expression of gene A and gene B. However, if we properly group the cells by cell type or cell state, we can see the correct correlation between the genes.

Image credit: Trapnell, C. Defining cell types and states with single-cell genomics, Genome Research 2015 (doi:

Despite scRNA-seq being able to capture expression at the cellular level, sample generation and library preparation is more expensive and the analysis is much more complicated and more difficult to interpret. The complexity of analysis of scRNA-seq data involves:

Large volume of data

Expression data from scRNA-seq experiments represent ten or hundreds of thousands of reads for thousands of cells. The data output is much larger, requiring higher amounts of memory to analyze, larger storage requirements, and more time to run the analyses.

Low depth of sequencing per cell

For the droplet-based methods of scRNA-seq, the depth of sequencing is shallow, often detecting only 10-50% of the transcriptome per cell. This results in cells showing zero counts for many of the genes. However, in a particular cell, a zero count for a gene could either mean that the gene was not being expressed or the transcripts were just not detected. Across cells, genes with higher levels of expression tend to have fewer zeros. Due to this feature, many genes will not be detected in any cell and gene expression will be highly variable between cells.

Zero-inflated? scRNA-seq data is often referred to as zero-inflated; however, recent analyses suggest that it does not contain more zeros than what would be expected given the sequencing depth [Valentine Svensson’s blog post].

Biological variability across cells/samples

Uninteresting sources of biological variation can result in gene expression between cells being more similar/different than the actual biological cell types/states, which can obscure the cell type identities. Uninteresting sources of biological variation (unless part of the experiment’s study) include:

Image credit: Wagner, A, et al. Revealing the vectors of cellular identity with single-cell genomics, Nat Biotechnol. 2016 (doi:

Technical variability across cells/samples

Technical sources of variation can result in gene expression between cells being more similar/different based on technical sources instead of biological cell types/states, which can obscure the cell type identities. Technical sources of variation include:


While scRNA-seq is a powerful and insightful method for the analysis of gene expression with single-cell resolution, there are many challenges and sources of variation that can make the analysis of the data complex or limited.

Overall, we recommend the following:

This lesson has been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.