Overview of Doublet Detecting Softwares

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	QC Filtering Required	Requires Pre-clustering	Doublet Detecting Method
DoubletDecon	✖️	✔️	Deconvolution based on clusters provided.
DoubletDetection	✖️	✖️	Iterative boost classifier to classify doublets.
DoubletFinder	✔️	✖️	Identify ideal cluster size and call expected number of droplets with highest number of simulated doublet neighbors as doublets.
scDblFinder	✖️	✖️	Gradient boosted trees trained with number neighboring doublets and QC metrics to classify doublets
Scds	✖️	✖️	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	✖️	✖️	Identifies the number of neighboring simulated doublets for each droplet and uses bimodal distribution of scores to classify singlets and doublets.
Solo	✖️	✖️	Simulates doublets and fits a two-layer neural network.

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