Groups of data (e.g. wells of a plates) are normalized relative to a subset (e.g. some wells within that plate, e.g. negative control). For each group mean and standard deviation (or median and mad) are calculated for the selected subset. Based on these estimates, all data points of a group are normalized by applying this formula:
x.zscore = (x – mean(x[subset]))/sd(x[subset])
or
x.zscore = (x – median(x[subset]))/mad(x[subset])
Literature: Malo et al., Nat. Biotechnol. 24, 167-179 (2006)
Wikipedia: Z-score / Standard score
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- Normalize Plates (B-Score)5 %
Number To String5 %File Reader4 %- Outlier Removal4 %
Row Filter (deprecated)3 %Excel Reader (XLS)3 %- Plate Heatmap Viewer3 %
Column Rename3 %- Math Formula2 %
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String To Number2 %- Rule-based Row Filter2 %
Excel Reader (XLS)2 %- SSMD (PC x NC)2 %
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Joiner2 %- Number to String2 %
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- Partitioning1 %
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- Z-Primes (PC x NC)1 %
- Column Combiner1 %
- Category to Number1 %
- Nominal Value Row Filter1 %
- Value Counter1 %
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Table Writer1 %- Group Loop Start< 1 %
- Column Resorter< 1 %
- Correlation Filter< 1 %
- String Manipulation< 1 %
- BinningAnalysis< 1 %
- Normalize Plates (Z-Score)< 1 %
CSV Reader< 1 %- Logistic Regression Learner< 1 %
- Concatenate< 1 %
- Auto-Binner< 1 %
Row Splitter (deprecated)< 1 %- Box Plot (legacy)< 1 %
- Row Filter (Labs)< 1 %
Expand Well Position< 1 %- Join Layout< 1 %
- R Snippet (2:1)< 1 %
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Loop End< 1 %- Missing Value Column Filter< 1 %
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XLS Reader< 1 %- Image Properties< 1 %
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Table Reader< 1 %- MultiLayerPerceptron Predictor< 1 %
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String Replace (Dictionary)< 1 %- Unpivot< 1 %
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Python Script (2⇒1)< 1 %- String to Date&Time< 1 %
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- Variable to Table Column< 1 %
- Constant Value Column Filter< 1 %
- Low Variance Filter< 1 %
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