Protein Abundance Z-Score Row Normalizer
Normalize a supplied protein abundance row with sample standard deviation (n−1); constant rows are undefined rather than fabricated zeros.
Description
Normalize a supplied protein abundance row with sample standard deviation (n−1); constant rows are undefined rather than fabricated zeros.
Z scores express each observation's distance from a mean in standard-deviation units. Using a sample rather than population deviation changes the scale for a finite vector.
When to use this tool
Normalize a supplied protein abundance row with sample standard deviation (n−1); constant rows are undefined rather than fabricated zeros.
- Prepare the supplied intensities fields for this operation.
- Compare the complete example with the representation used by your own input before carrying the result into another step.
How to use the tool and how it works
- Enter intensities. Use the exact text syntax, units and index conventions explained below.
- Run the tool. If input validation fails, correct the supplied fields before interpreting any result.
- Read result.mean, result.sampleStandardDeviation, result.zScores. Interpret false, null and empty lists using the distinctions below; they can be valid outputs.
Require at least two nonnegative intensities. Compute their arithmetic mean and sample variance with denominator n-1, require positive sample standard deviation, then return (value-mean)/sampleStandardDeviation for each entry.1
| Input field | Meaning and representation | Accepted input |
|---|---|---|
| intensities | intensities. | List of finite numbers; bounded by the operation |
| Output field | Interpretation |
|---|---|
| result | Object containing the named outputs below. |
| result.mean | The mean value under the stated formula or text convention. |
| result.sampleStandardDeviation | The sample standard deviation value under the stated formula or text convention. |
| result.zScores | The z scores list in the ordering and representation described above. |
{
"input": {
"intensities": [
1,
2,
3
]
},
"output": {
"result": {
"mean": 2,
"sampleStandardDeviation": 1,
"zScores": [
-1,
0,
1
]
}
}
}Limitations and assumptions
- Constant vectors are rejected because their deviation is zero. No log transform, missing-value handling or cross-sample normalization is applied, and a z score does not imply a normal distribution.
- Every declared top-level field is required and unexpected fields are rejected. Numeric inputs must be finite; ordinary text fields are bounded to 8,192 UTF-16 code units, with tighter operation-specific limits where stated.
- Ordinary string lists have at most 128 entries of at most 128 code units each; numeric lists and matrices have separate bounded size checks. Nonfinite results and excessively large serialized outputs are rejected.
- Indexes are zero-based and spans have an exclusive end unless explicitly stated otherwise. A valid false, null or empty-list result is distinct from malformed input.
Alternative or Complementary methods
Use compatible mass, charge and sequence conventions when combining these operations. Inspect experimental spectra or quantitative controls separately; sequence features and theoretical mass arithmetic are not peptide identification or biological interpretation.
References
Similar or alternative tools
- empai abundance
emPAI applies an exponential transformation to the observed-versus-observable peptide ratio.
- ibaq abundance
iBAQ-style normalization divides summed peptide intensity by a supplied theoretical peptide count.