PT  - JOURNAL ARTICLE
AU  - Eduardo Torre
AU  - Hannah Dueck
AU  - Sydney Shaffer
AU  - Janko Gospocic
AU  - Rohit Gupte
AU  - Roberto Bonasio
AU  - Junhyong Kim
AU  - John Murray
AU  - Arjun Raj
TI  - A comparison between single cell RNA sequencing and single molecule RNA FISH for rare cell analysis
AID  - 10.1101/138289
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 138289
4099  - http://biorxiv.org/content/early/2017/05/18/138289.short
4100  - http://biorxiv.org/content/early/2017/05/18/138289.full
AB  - The development of single cell RNA sequencing technologies has emerged as a powerful means of profiling the transcriptional behavior of single cells, leveraging the breadth of sequencing measurements to make inferences about cell type. However, there is still little understanding of how well these methods perform at measuring single cell variability for small sets of genes and what “transcriptome coverage” (e.g. genes detected per cell) is needed for accurate measurements. Here, we use single molecule RNA FISH measurements of 26 genes in thousands of melanoma cells to provide an independent reference dataset to assess the performance of the DropSeq and Fluidigm single cell RNA sequencing platforms. We quantified the Gini coefficient, a measure of rare-cell expression variability, and find that the correspondence between RNA FISH and single cell RNA sequencing for Gini, unlike for mean, increases markedly with per-cell library complexity up to a threshold of ∼2000 genes detected. A similar complexity threshold also allows for robust assignment of multi-genic cell states such as cell cycle phase. Our results provide guidelines for selecting sequencing depth and complexity thresholds for single cell RNA sequencing. More generally, our results suggest that if the number of genes whose expression levels are required to answer any given biological question is small, then greater transcriptome complexity per cell is likely more important than obtaining very large numbers of cells.