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03_​Normalization_​of_​screening_​data

Normalization of screening data with HCS-Tools

The workflow shows how to use the nodes from the HCS-Tools plugin to normalize screening data with different well known methods.

Normalization of screening data with HCS-ToolsThe workflow shows how to use the nodes from the HCS-Tools plugin to normalizescreening data with different well known methods.REQUIRES: Installation of HCS-Tools Plugin (part of the community contribution) Normalization:- see help for detailed description of the the math applied- 'Group data by' is usually the plate barcode- 'Reference label' is usually the column containing some well annotation- 'subset by' is usually annotation for negative contols (DMSO, untreated, mock...)- hint: use String Manipulator or Combine Columns by Header nodes to merge well annotation columns Outlier Removal can be used to clean the data set for outliersSEE ALSO: Normalize Plates (B-Score) Annotated data fromimage analsys withCellProfiler per platebased on negative control(~ 100%) total data setbased on negative control(mean 0 / sd 1) per platebased onnegative (100%) and positive (0%)controlcombinemeta datacalculatewell means exclude outliersnegative control combinemeta dataSEE: Split Columnsby Header extractmeta dataSEE: Combine Columnsby Headermeansper conditionsort permeta data no hitvs. hit(at leastone parameter with z-score < -3 orzscore > 3) Table Reader NormalizePlates (POC) Normalize Plates(Z-Score) NormalizePlates (NPI) String Manipulation GroupBy Outlier Removal Nominal ValueRow Filter Combine Columnsby Header Split Columnsby Header GroupBy Sorter Range Splitter Normalization of screening data with HCS-ToolsThe workflow shows how to use the nodes from the HCS-Tools plugin to normalizescreening data with different well known methods.REQUIRES: Installation of HCS-Tools Plugin (part of the community contribution) Normalization:- see help for detailed description of the the math applied- 'Group data by' is usually the plate barcode- 'Reference label' is usually the column containing some well annotation- 'subset by' is usually annotation for negative contols (DMSO, untreated, mock...)- hint: use String Manipulator or Combine Columns by Header nodes to merge well annotation columns Outlier Removal can be used to clean the data set for outliersSEE ALSO: Normalize Plates (B-Score) Annotated data fromimage analsys withCellProfiler per platebased on negative control(~ 100%) total data setbased on negative control(mean 0 / sd 1) per platebased onnegative (100%) and positive (0%)controlcombinemeta datacalculatewell means exclude outliersnegative control combinemeta dataSEE: Split Columnsby Header extractmeta dataSEE: Combine Columnsby Headermeansper conditionsort permeta data no hitvs. hit(at leastone parameter with z-score < -3 orzscore > 3) Table Reader NormalizePlates (POC) Normalize Plates(Z-Score) NormalizePlates (NPI) String Manipulation GroupBy Outlier Removal Nominal ValueRow Filter Combine Columnsby Header Split Columnsby Header GroupBy Sorter Range Splitter

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: tdsuser
Created at: 2016-07-13
On NodePit since: 2019-07-12
Last update: 2024-04-18
Created with KNIME version: v3.2.0
Tags: High-content screening

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