Updates in the world of single-cell RNA-sequencing data analysis


Stephanie C. Hicks


March 20, 2019

In the last few weeks, there have been quite a few great pre-prints posted on bioRxiv related to the analysis of a popular type of genomics data, called single-cell RNA-sequencing (scRNA-seq). The idea with this data is that you can measure genome-wide features (such as genes or transcripts) in individual cells, in contrast to more traditional bulk RNA-sequencing experiments where for each feature, you get an average of gene expression across all the cells in the sample. One key insight that has been widely talked about in the world of scRNA-seq data analysis is the over inflation of the number of zeros, or sparsity, of the data compared to bulk RNA-sequencing measurements. Therefore, for several years now, lots of work has been focused on developing zero-inflation aware methods. Many early contributions were based on what are commonly referred to as plate-based protocols as opposed to the more recently developed droplet-based protocols that have what are called unique molecular identifiers (UMIs) – little barcodes attached to the ends of the mRNA that remove certain biases related to PCR amplification. Here, I wanted to write up a quick blogpost summarizing one of the key takeaways that have come out from some recent pre-prints.

Droplet-based scRNA-seq data with UMI counts are not zero inflated

There were hints of this in 2017 [Vieth et al. (2017) and a blogpost by Valentine Svensson in 2017], but several recent pre-prints have come out supporting this idea. Townes et al. (2019) showed UMI count data derived from negative control scRNA-seq datasets (i.e. identical RNA was added to droplets and sequenced aka we do not expect any biological variation) are well-described by multinomial distributions, which can be approximated by Poisson and negative binomial distributions. A few days later Hafemeister and Satija (2019) independently published similar results, with a different error distribution. The next day, Svensson et al. (2019) took the analysis from his 2017 blogpost and converted it into a pre-print. Here, he took five negative control droplet scRNA-seq datasets (again, identical RNA was added to droplets and sequenced aka we do not expect any biological variation), and showed how the data – well 4 out of 5 datasets – dropseq was a bit wonky) fit nicely to a negative binomial distribution. This matches what previous authors have found Vieth et al. (2017). Svensson argues this suggests an over-abundance of zeros in biological data is likely real biological variation, as opposed to technical variation, which also been suggested by Andrews and Hemberg (2018) as a way to identify genes that contain biologically meaningful information.

Both Townes and Svensson hypothesize that the zero-inflation in non-UMI data is related to the outliers in PCR duplicated counts, which is corrected by the use of UMIs. Svensson does not perform a similar assessment of plate-based scRNA-seq datasets because no comparable negative control data exists or plate-based methods. However, Svensson goes on to hypothesize that plate-based methods might introduce an additional layer out count noise leading to over dispersion and manifesting as additional zeros.

What does this mean for the field going forward? This means that the choice of methods used for the analysis of scRNA-seq data will likely vary depending on the type of protocol used to generate that data. I guess this is not so suprising, but methods do matter and the assumptions behind those methods are important to think about. However, ultimately I’m just excited to see such great work from multiple groups all converge on a similar idea. Hope this helps to move the field forward.