Doublet Suspicion Score
Compare a cell’s expression profile with simulated doublet and singlet neighbors.
Description
A cell profile can be compared with supplied singlet and simulated-doublet references.
A cell profile can be compared with supplied singlet and simulated-doublet references. Distance differences depend on the profile scaling and reference choice.1
Compare a cell’s expression profile with simulated doublet and singlet neighbors.
When to use Doublet Suspicion Score
Use this tool to compare a cell’s expression profile with simulated doublet and singlet neighbors.
Prepare the upstream counts, expression values, sequences or record lists requested by this tool. Keep corresponding features in the same order and confirm count units, transformations and caller-selected criteria.
How to use Doublet Suspicion Score and understand its results
- Prepare the upstream counts, expression values, sequences or record lists requested by this tool. Keep corresponding features in the same order and confirm count units, transformations and caller-selected criteria.
- Enter cellProfile, simulatedDoubletProfile, nearestSingletProfile, suspicionThreshold using the field table and calculation rules, then run the tool.
- Read doubletDistance, singletDistance, suspicionMargin, suspected. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| cellProfile | Cell profile | list of number | Required |
| simulatedDoubletProfile | Simulated doublet profile | list of number | Required |
| nearestSingletProfile | Nearest singlet profile | list of number | Required |
| suspicionThreshold | Suspicion threshold | number | Required |
Enter three aligned nonempty finite feature lists and a finite suspicion criterion. Compute both Euclidean distances and subtract doublet distance from singlet distance. Return distances, margin and an inclusive margin≥criterion flag; a positive margin means closer to the doublet reference.
margin=EuclideanDistance(cell,singlet)−EuclideanDistance(cell,doublet) Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| doubletDistance | Doublet distance | number |
| singletDistance | Singlet distance | number |
| suspicionMargin | Suspicion margin | number |
| suspected | Suspected | boolean |
{
"input": {
"cellProfile": [
5,
5
],
"simulatedDoubletProfile": [
6,
4
],
"nearestSingletProfile": [
1,
1
],
"suspicionThreshold": 0
},
"output": {
"doubletDistance": 1.4142135623730951,
"singletDistance": 5.656854249492381,
"suspicionMargin": 4.242640687119286,
"suspected": true
}
}Limitations and assumptions
This does not simulate doublets, normalize profiles or reproduce a neighborhood-based doublet detector. The margin is not a calibrated probability or definitive cell classification.
Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Lists are limited to 100,000 entries; the rules above may require smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors. A successfully returned false flag is an evaluated outcome; its meaning is explained above.
Alternative or Complementary methods
Use Empty Droplet Filter: Classify droplets by a caller-supplied minimum UMI count. For a complementary task, use Correlation Distance Matrix: Build a Pearson correlation-distance matrix for hierarchical clustering.
References
-
Doublet Suspicion Score — tool.io
Similar or alternative tools
- Empty Droplet Filter
Classify droplets by a caller-supplied minimum UMI count.
- Correlation Distance Matrix
Build a Pearson correlation-distance matrix for hierarchical clustering.