Branch Divergence
Measure trajectory branch divergence from aligned expression profiles.
Description
Aligned branch profiles can be summarized by their absolute expression differences.
Aligned branch profiles can be summarized by their absolute expression differences. The mean measures average separation while the maximum highlights the largest coordinate difference.1
Measure trajectory branch divergence from aligned expression profiles.
When to use Branch Divergence
Use this tool to measure trajectory branch divergence from aligned expression profiles.
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 Branch Divergence 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 branchOneValues, branchTwoValues, minimumDivergence using the field table and calculation rules, then run the tool.
- Read meanAbsoluteDivergence, maxDivergence, divergent. 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 |
|---|---|---|---|
| branchOneValues | Branch one values | list of number | Required |
| branchTwoValues | Branch two values | list of number | Required |
| minimumDivergence | Minimum divergence | number | Required |
Enter aligned nonempty finite profiles and a finite minimum divergence criterion. Return mean absolute divergence, maximum difference and an inclusive mean≥criterion flag.
difference_i=|branchOne_i−branchTwo_i|; mean=Σdifference/n Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| meanAbsoluteDivergence | Mean absolute divergence | number |
| maxDivergence | Max divergence | number |
| divergent | Divergent | boolean |
{
"input": {
"branchOneValues": [
1,
2
],
"branchTwoValues": [
3,
4
],
"minimumDivergence": 2
},
"output": {
"meanAbsoluteDivergence": 2,
"maxDivergence": 2,
"divergent": true
}
}Limitations and assumptions
Branch alignment, pseudotime and expression scaling must already be established. This does not infer branches, calculate a statistical test or identify which coordinate caused the maximum.
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 Pseudotime Root Selector: Select the earliest cell as the pseudotime trajectory root. For a complementary task, use Correlation Distance Matrix: Build a Pearson correlation-distance matrix for hierarchical clustering.
References
-
Branch Divergence — tool.io
Similar or alternative tools
- Pseudotime Root Selector
Select the earliest cell as the pseudotime trajectory root.
- Correlation Distance Matrix
Build a Pearson correlation-distance matrix for hierarchical clustering.