Transcrition-based doublet detection softwares use the transcriptomic profiles in each cell to predict whether that cell is a singlet or doublet. Most methods simulate doublets by adding the transcritional profiles of two droplets in your pool together. Therefore, these approaches assume that only a small percentage of the droplets in your dataset are doublets. The table bellow provides a comparison of the different methods.
| Doublet Detecting Software | .. centered:: QC Filtering Required | .. centered:: Requires Pre-clustering | Doublet Detecting Method |
|---|---|---|---|
DoubletDecon <DoubletDecon-docs> |
Deconvolution based on clusters provided. | ||
DoubletDetection <DoubletDetection-docs> |
Iterative boost classifier to classify doublets. | ||
DoubletFinder <DoubletFinder-docs> |
Identify ideal cluster size and call expected number of droplets with highest number of simulated doublet neighbors as doublets. | ||
scDblFinder <scDblFinder-docs> |
Gradient boosted trees trained with number neighboring doublets and QC metrics to classify doublets | ||
Scds <Scds-docs> |
cxds: Uses genes pairs that are typically not expressed in the same droplet to rank droplets based on coexpression of all pairs. bcds: Uses highly variable genes and simulated doublets to train a binary classificaiton algorithm and return probability of droplet being a doublet. | ||
Scrublet <Scrublet-docs> |
Identifies the number of neighboring simulated doublets for each droplet and uses bimodal distribution of scores to classify singlets and doublets. | ||
Solo <Solo-docs> |
Simulates doublets and fits a two-layer neural network. |
If you don't know which demultiplexing software(s) to run, take a look at our Software Selection Recommendations <SoftwareSelection-docs> based on your dataset or use our add widget link here