ScreenKNIMEing How HCS-Tools and Scripting Integrations can be used - - PowerPoint PPT Presentation

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ScreenKNIMEing How HCS-Tools and Scripting Integrations can be used - - PowerPoint PPT Presentation

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ScreenKNIMEing How HCS-Tools and Scripting Integrations can be used in a screening environment 1 Friday, February 24, 2012 1 Outline - 1 st half HCS-Tools step-by-step Setup / Preferences Todays task What we are working with

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SLIDE 1

ScreenKNIMEing

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

1

1 Friday, February 24, 2012

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SLIDE 2

Antje Niederlein, niederle@mpi-cbg.de

Outline - 1st half

  • HCS-Tools step-by-step
  • Setup / Preferences
  • Todays task
  • What we are working with
  • The big goal
  • Step by step

2

2 Friday, February 24, 2012

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SLIDE 3

Antje Niederlein, niederle@mpi-cbg.de

Outline - 2nd half

  • Scripting Integrations
  • Setup / Preferences
  • Node Types
  • Configuration dialog elements
  • Script editor
  • Template repository
  • ...
  • Todays task

3

3 Friday, February 24, 2012

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SLIDE 4

HCS-Tools

4

4 Friday, February 24, 2012

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SLIDE 5

Antje Niederlein, niederle@mpi-cbg.de

Setup / Preferences

  • Installation
  • http://tech.knime.org/update/community-contributions/nightly
  • Community Contributions --> KNIME HCS Tools
  • Preferences

5

5 Friday, February 24, 2012

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SLIDE 6

Antje Niederlein, niederle@mpi-cbg.de

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

6

6 Friday, February 24, 2012

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SLIDE 7

Antje Niederlein, niederle@mpi-cbg.de

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]* 7

7 Friday, February 24, 2012

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SLIDE 8

Antje Niederlein, niederle@mpi-cbg.de

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

8

8 Friday, February 24, 2012

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SLIDE 9

Antje Niederlein, niederle@mpi-cbg.de

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

9

9 Friday, February 24, 2012

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SLIDE 10

Antje Niederlein, niederle@mpi-cbg.de

The Layout

  • An Excel-sheet of a certain format provides information
  • n the treatment
  • Positive Transfection Controls (Tox1, Tox, Tox3)
  • Negative Controls (Mock, Untreated)
  • RNAi library

10

10 Friday, February 24, 2012

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SLIDE 11

Antje Niederlein, niederle@mpi-cbg.de

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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11 Friday, February 24, 2012

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SLIDE 12

Antje Niederlein, niederle@mpi-cbg.de

  • 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

12

12 Friday, February 24, 2012

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SLIDE 13

Antje Niederlein, niederle@mpi-cbg.de

  • 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

12

Data Input

12 Friday, February 24, 2012

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SLIDE 14

Antje Niederlein, niederle@mpi-cbg.de

  • 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

12

Data Input Meta data integration

12 Friday, February 24, 2012

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SLIDE 15

Antje Niederlein, niederle@mpi-cbg.de

  • 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

12

Data Input Meta data integration Visualization

12 Friday, February 24, 2012

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SLIDE 16

Antje Niederlein, niederle@mpi-cbg.de

  • 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

12

Data Input Meta data integration Visualization Quality Control

12 Friday, February 24, 2012

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SLIDE 17

Antje Niederlein, niederle@mpi-cbg.de

  • 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

13

13 Friday, February 24, 2012

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SLIDE 18

Antje Niederlein, niederle@mpi-cbg.de

  • 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

13

Normalization

13 Friday, February 24, 2012

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SLIDE 19

Antje Niederlein, niederle@mpi-cbg.de

  • 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

13

Normalization Quality Control

13 Friday, February 24, 2012

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SLIDE 20

Antje Niederlein, niederle@mpi-cbg.de

  • 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

14

14 Friday, February 24, 2012

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SLIDE 21

Antje Niederlein, niederle@mpi-cbg.de

  • 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

14

Normalization

14 Friday, February 24, 2012

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SLIDE 22

Antje Niederlein, niederle@mpi-cbg.de

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)
  • 15

15 Friday, February 24, 2012

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SLIDE 23

Scripting Integrations (R)

16

16 Friday, February 24, 2012

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SLIDE 24

Antje Niederlein, niederle@mpi-cbg.de

Setup / Preferences

  • Installation
  • http://tech.knime.org/update/community-contributions/nightly
  • Community Contributions --> KNIME R Scripting Extension

17

17 Friday, February 24, 2012

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SLIDE 25

Antje Niederlein, niederle@mpi-cbg.de

Preferences

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18 Friday, February 24, 2012

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SLIDE 26

Antje Niederlein, niederle@mpi-cbg.de

Node Types

19

  • Plot
  • Snippet
  • OpenIn...
  • (Generic nodes)

19 Friday, February 24, 2012

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SLIDE 27

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Script Editor tab (script view)

20

Column names Scripting area

20 Friday, February 24, 2012

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SLIDE 28

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Template tab

21

21 Friday, February 24, 2012

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SLIDE 29

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Template tab

21

Template Repository

21 Friday, February 24, 2012

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SLIDE 30

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Template tab

21

Template Repository

21 Friday, February 24, 2012

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SLIDE 31

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Template tab

21

Template Repository

21 Friday, February 24, 2012

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SLIDE 32

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Template tab

21

Template Repository Template Description / Source

21 Friday, February 24, 2012

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SLIDE 33

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Template tab

21

Template Repository Template Description / Source

21 Friday, February 24, 2012

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SLIDE 34

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Template tab

21

Template Repository Template Description / Source

21 Friday, February 24, 2012

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SLIDE 35

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Script Editor tab (template view)

22

22 Friday, February 24, 2012

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SLIDE 36

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Script Editor tab (template view)

22

RGG interface of the selected template

22 Friday, February 24, 2012

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SLIDE 37

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Script Editor tab (template view)

22

RGG interface of the selected template modify final script

22 Friday, February 24, 2012

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SLIDE 38

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Script Editor tab (template view)

22

RGG interface of the selected template modify final script

22 Friday, February 24, 2012

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SLIDE 39

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Script Editor tab (template view)

22

RGG interface of the selected template modify final script

22 Friday, February 24, 2012

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SLIDE 40

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Script Editor tab (template view)

23

23 Friday, February 24, 2012

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SLIDE 41

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Script Editor tab (template view)

23

modify template (dev)

23 Friday, February 24, 2012

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SLIDE 42

Antje Niederlein, niederle@mpi-cbg.de

Plot / Snippet

  • Script Editor tab (template view)

23

modify template (dev) RGG (XML)

23 Friday, February 24, 2012

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SLIDE 43

Antje Niederlein, niederle@mpi-cbg.de

Tips & Tricks for Editing

  • Mouse click = ”Column name”
  • Alt + Mouse click = kIn$”Column name”
  • Ctrl + Mouse click = Displays possible domain values
  • f 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”,...

24

24 Friday, February 24, 2012

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SLIDE 44

Antje Niederlein, niederle@mpi-cbg.de

Plot output

25

25 Friday, February 24, 2012

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SLIDE 45

Antje Niederlein, niederle@mpi-cbg.de

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

26

26 Friday, February 24, 2012

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SLIDE 46

Antje Niederlein, niederle@mpi-cbg.de

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

27

27 Friday, February 24, 2012

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SLIDE 47

Antje Niederlein, niederle@mpi-cbg.de

Get ready

  • Download tutorial
  • Download workflow (including example data)

28

28 Friday, February 24, 2012