determining doublets in single-cell RNA-seq Doublets are a known problem with scRNA-seq experiments, where 2 or more cells are sometimes captured instead. To determine their presence, there are studies that mix multiple species (such as human and mouse) or distinct cell types and then count how many cells contain genes from the very different transcriptomes. In those experiments, you can pick any cell and determine if it's a doublet based on a few key genes. In a typical single-cell experiment on one sample, you don't have the luxury of knowing what cells to expect and if a particular cell looks abnormal.

If you have a large fraction of doublets, you should be able to see a double peak if you plot the number of genes/transcripts per cell. However, if you have something like 1% doublets, you probably wouldn't notice anything. It seems that most people simply filter out top X% cells based on the number of genes/transcripts. How do you know what X should be? Can you actually estimate the fraction of doublets in a given experiment?

Update:

I found a nice figure from Stoeckius et al. to illustrate the issue:

Although the number of transcripts/UMIs seems to be roughly double for doublets/multiplets (median of ~1000 vs ~500), there is still high overlap.
Expected rates of doublets / duplets / multiplets


Fluidigm C1 doublet rate: around 1-5% depending on chip type used.
More information: Fluidigm white paper: Redesign of C1 Medium-Cell 96 IFCs Improves Single-Cell Capture Efficiency (PN 101-3328 B1)
10x Genomics Chromium 3'mRNA-Seq doublet rate: around 1% per 1000 cells captured


Rates in the 'Chromium Single Cell 3’ Reagent Kits v2 User Guide':

Multiplet Rate (%)  # of Cells Loaded   # of Cells Recovered
0.40%   870 500
0.80%   1700    1000
1.60%   3500    2000
2.30%   5300    3000
3.10%   7000    4000
3.90%   8700    5000
4.60%   10500   6000
5.40%   12200   7000
6.10%   14000   8000
6.90%   15700   9000
7.60%   17400   10000




Detection and identification of doublets and multiplets

Experimental


Visual inspection (e.g. Fluidigm C1)
FACS index sorting data can be used to identify doublets.
Mixed species experiment.


Illumina | BioRad White paper Single-Cell RNA Data Analysis Workflow  
Zheng et al., 2017: Massively parallel digital transcriptional profiling of single cells

Cell "hashing" with oligo-tagged antibodies (Stoeckius et al., 2017): cells (barcodes) positive for two or more oligos are multiplets.


Computational


Mixed gene expression (e.g. lymphoid and myeloid, or T and B cell)
Ilicic et al. describe an approach for detecting low quality cells (incl. multiplets). They train a support vector machine (SVM) model on a dataset annotated by microscopy inspection. They find, among others, that


"wells containing multiple cells (multiples) show similar expression and noise patterns to broken cells", suggesting "that multiple cells contain a mixture of broken and high quality cells."
difference in proportion of duplicated reads
higher ERCC spike-in ratios

demuxlet (Kang et al., 2017, Nature Biotechnology)
DoubletDetection