Cell Mixture Deconvolution
Estimate a two-cell mixture fraction by constrained least squares against caller references.
Description
A two-component mixture estimate projects two marker observations onto supplied reference profiles.
A two-component mixture estimate projects two marker observations onto supplied reference profiles.1
Estimate a two-cell mixture fraction by constrained least squares against caller references.
When to use Cell Mixture Deconvolution
Use this tool to estimate a two-cell mixture fraction by constrained least squares against caller references.
Prepare the already measured signals, counts, coordinates, fitted coefficients or sequence strings requested. Keep assay definitions, units, aligned lists and caller-selected thresholds consistent.
How to use Cell Mixture Deconvolution and understand its results
- Prepare the already measured signals, counts, coordinates, fitted coefficients or sequence strings requested. Keep assay definitions, units, aligned lists and caller-selected thresholds consistent.
- Enter observedMarkerOne, observedMarkerTwo, cellAMarkerOne, cellAMarkerTwo, cellBMarkerOne, cellBMarkerTwo using the field table and calculation rules, then run the tool.
- Read cellAFraction, cellBFraction, residualSquared. 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 |
|---|---|---|---|
| observedMarkerOne | Observed marker one | number | Required |
| observedMarkerTwo | Observed marker two | number | Required |
| cellAMarkerOne | Cell a marker one | number | Required |
| cellAMarkerTwo | Cell a marker two | number | Required |
| cellBMarkerOne | Cell b marker one | number | Required |
| cellBMarkerTwo | Cell b marker two | number | Required |
Enter finite observations/distinct profiles; return fractions and residual squared error, with residual<1e−24 set to zero.
A=clip(((observed−B)·(A−B))/abs(A−B)²,0,1); B=1−A Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| cellAFraction | Cell a fraction | number |
| cellBFraction | Cell b fraction | number |
| residualSquared | Residual squared | number |
{
"input": {
"observedMarkerOne": 0.7,
"observedMarkerTwo": 0.3,
"cellAMarkerOne": 1,
"cellAMarkerTwo": 0,
"cellBMarkerOne": 0,
"cellBMarkerTwo": 1
},
"output": {
"cellAFraction": 0.7,
"cellBFraction": 0.30000000000000004,
"residualSquared": 0
}
}Limitations and assumptions
Only two markers/components are fitted. Signed marker inputs are accepted; references and purity are not validated.
Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use MOFA Factor Rank: Rank a MOFA factor by explained variance share. For a complementary task, use Matrix Row Balance: Measure matrix row-sum balance by coefficient of variation.
References
-
Cell Mixture Deconvolution — tool.io
Similar or alternative tools
- MOFA Factor Rank
Rank a MOFA factor by explained variance share.
- Matrix Row Balance
Measure matrix row-sum balance by coefficient of variation.