screen knime ing - mpi-cbg · antje niederlein, [email protected] outline - 1st half ‣...

ScreenKNIMEingHow HCS-Tools and Scripting Integrations can be used in a screening environment

11Friday, February 24, 2012

Antje Niederlein, [email protected]

Outline - 1st half‣ HCS-Tools step-by-step

‣ Setup / Preferences

‣ Todays task

‣ What we are working with

‣ The big goal

‣ Step by step

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mailto:[email protected]



Outline - 2nd half‣ Scripting Integrations

‣ Setup / Preferences

‣ Node Types

‣ Configuration dialog elements

‣ Script editor

‣ Template repository

‣ ...

‣ Todays task

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HCS-Tools4



Setup / Preferences ‣ Installation

‣ http://tech.knime.org/update/community-contributions/nightly

‣ Community Contributions --> KNIME HCS Tools

‣ Preferences

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http://tech.knime.org/update/community-contributions/nightly



Preferences‣ Minimal samples required to calculate mean / median

‣ Minimal samples required to calculate variance / MAD

‣ ... at least x samples per group should be present to provide a descent estimate of the statistic

‣ important for normalization and QC nodes

‣ less samples will result in a warning

‣ Scaling factor for MAD

‣ set to the factor proposed for normal distributed data

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Preferences‣ Barcode patterns

‣ regular expressions (separated by ‘;’) which describes a barcode standard

‣ used for ‘Expand Barcode’ node to automatically retrieve meta data from the barcode, and ‘Plate Viewer’ node

‣ possible placeholders

‣ projectcode, libcode, libnumber, date, replicate, assay, description, concentration, concunit, timepoint, customa, customb, customc, customd

‣ (?<libplatenumber>[0-‐9]{3})(?<projectcode>[A-‐z]{2})(?<date>[0-‐9]{6})(?<replicate>[A-‐z]{1})-‐(?<libcode>[_A-‐z\d]{3})(?<assay>[-‐_\s\w\d]*

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Todays task‣ Where do we start?

‣ RNAi screen has been performed with a stable cell line. Cells were fixed and stained

‣ Nuclei and cytoplasm staining

‣ Marker 1 and Marker 2

‣ 39 x 384-well plates

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Todays task‣ What are we working with?

‣ Results from an image analysis (Acapella) of images taken by the Opera - an automated confocal microscope from PerkinElmer

‣ Each RES-file (XML-format) contains the results of one 384 well plate

‣ Measurements

‣ Number of cells in the well

‣ several quality control measurements (intensities of different channels)

‣ The Layout is given as an Excel-sheet

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The Layout‣ An Excel-sheet of a certain format provides information

on the treatment

‣ Positive Transfection Controls (Tox1, Tox, Tox3)

‣ Negative Controls (Mock, Untreated)

‣ RNAi library

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Big goal‣ Extract RNAi’s which are not toxic but show a

significant increase of the signals in both marker channels

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‣ Load the raw data

‣ Add meta data from both barcode and layout

‣ Inspect data visually

‣ Strength of cell number reduction for transfection controls seems to be different, has to be quantified

‣ Readouts show a plate to plate variation

‣ Did the transfection work well?

‣ ‘well’ = at least 80% transfection efficiency

Step by step

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Step by step

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Data Input












Step by step

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Data Input

Meta data integration












Step by step

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Data Input


Visualization












Step by step

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Data Input


Visualization

Quality Control





‣ Plate wise normalization / Percent of control (POC)

‣ plate data has to be normalized on the Mock wells

‣ two effects:

‣ Mock wells will be centered around 100% (eliminates plate wise variation)

‣ Transfection controls are represented as percentage which makes it easy to judge about transfection efficiency

‣ Quality control - SSMD

‣ Measure of how well positive control and negative control are separated from each other. It’s better interpretable than z prime factor

Step by step

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‣ two effects:





Step by step

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Normalization







‣ two effects:





Step by step

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Normalization

Quality Control





‣ Remove all screening plate where none of the transfection controls shows an efficiency < 80 %

‣ keep, if at least on transfection control has less then 20% cells

‣ Actual hit selection

‣ consider only wells with a cell number comparable to Mock(‘comparable’ = +- 0.5 standard deviation away from the median)

‣ consider wells which show more than 2 standard deviation increase of both marker channel signals

‣ Z-score normalization of the whole screen based on Mock

‣ centralize Mock values around 0 with standard deviation 1

Step by step

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‣ Remove all screening plate where none of the transfection controls shows an efficiency < 80 %

‣ keep, if at least on transfection control has less then 20% cells

‣ Actual hit selection

‣ consider only wells with a cell number comparable to Mock(‘comparable’ = +- 0.5 standard deviation away from the median)

‣ consider wells which show more than 2 standard deviation increase of both marker channel signals

‣ Z-score normalization of the whole screen based on Mock

‣ centralize Mock values around 0 with standard deviation 1

Step by step

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Normalization





Get ready‣ Install the HCS-Tools (and Scripting Integrations)

‣ Download tutorial

‣ (Purpose)

‣ Where to find the node?

‣ How to configure the node?

‣ How it works?

‣ (What does the node view show?)

‣ What does the output table contain?

‣ Download workflow (including example data)

‣

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Scripting Integrations (R)



Setup / Preferences‣ Installation

‣ http://tech.knime.org/update/community-contributions/nightly

‣ Community Contributions --> KNIME R Scripting Extension

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Preferences

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Node Types

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‣ Plot

‣ Snippet

‣ OpenIn...

‣ (Generic nodes)





Plot / Snippet‣ Script Editor tab (script view)

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Column namesScripting area





Plot / Snippet‣ Template tab

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Template Repository






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Template RepositoryTemplate Description / Source





Plot / Snippet‣ Script Editor tab (template view)

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22

RGG interface of the selected template






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RGG interface of the selected template

modify final script






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23modify template (dev)






23modify template (dev)

RGG (XML)





Tips & Tricks for Editing ‣ Mouse click = ”Column name”

‣ Alt + Mouse click = kIn$”Column name”

‣ Ctrl + Mouse click = Displays possible domain values of the column and offers to insert a selection (comma separated

‣ Press Apple/Windows key and select multiple = as soon as you release the key, the selected column names will be inserted “column 1”,”column 2”,...

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Plot output

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Todays task‣ Are the readouts normal distributed?

‣ Use QQ-Plot template and Shapiro Wilk test template

‣ Create density distribution plots for each readout

‣ save the plots to disk

‣ put the readout name into the title

‣ collect the plots as images in the loop

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Todays task‣ Write your own little plot template which returns the

histogram of a user defined number of random normal distributed values

‣ The mean and standard deviation should be taken from the estimates of a chosen numeric column of the input table

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Get ready‣ Download tutorial

‣ Download workflow (including example data)

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screen knime ing - mpi-cbg · antje niederlein, [email protected] outline - 1st half ‣...

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