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 |
---|---|---|---|
✖️ |
✔️ |
Deconvolution based on clusters provided. |
|
✖️ |
✖️ |
Iterative boost classifier to classify doublets. |
|
✔️ |
✖️ |
Identify ideal cluster size and call expected number of droplets with highest number of simulated doublet neighbors as doublets. |
|
✖️ |
✖️ |
Gradient boosted trees trained with number neighboring doublets and QC metrics to classify doublets |
|
✖️ |
✖️ |
cxds: Uses genes pairs that are typically not expressed in the same droplet to rank droplets based on coexpression of all pairs. |
|
✖️ |
✖️ |
Identifies the number of neighboring simulated doublets for each droplet and uses bimodal distribution of scores to classify singlets and doublets. |
|
✖️ |
✖️ |
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 based on your dataset or use our add widget link here