08-10-2004MCGH Analyzer1 Hans A. Kestler André Müller.

08-10-2004 MCGH Analyzer 1

MCGH Analyzer

Hans A. KestlerAndré Müller


Data processing steps

• Scanning of the DNA chips (normal and switched)– 2 Channels (Cy 5 and Cy 3)

• Build mean/median over the pixels

• Further processing with MCGH Software


MCGH software

• Background reductioncalculate intensities according to the background

• Quality control of the spotsreject spots not fitting the quality criteria

• Accumulate spots to clones• Check test

reject clones not fitting the visual options

• Select control clones• Reduce control clones• Main calculation loop


Overview


Background reduction

Background reduction to get intensities1. No reduction2. Fixed reduction3. Local reduction4. Global reduction5. Local + Fixed reduction6. Global + Fixed reduction

Compute log Ratios• log( IntCy3 / IntCy5 )• log( IntCy5 / IntCy3 )


Quality control

Reject spots with

• flags marked by the scanning software(bad, not found, absent, normal ...)

• A background intensity brighter than the foreground (new!)

• Min/Max reduction:– Reject the n smallest ratios– Reject the n largest ratios


Spots to clones

Accumulate the non-rejected spot values

• Mean

• Standard deviation

• Median

over

• Intensities (Cy3, Cy5)

• log Ratios

New Feature: Reject clones with less than SpotLowerBound valid spots.


Check test

Reject clones if at least one of these conditions holds:

1. Me(di)an background intensity > Background upper bound

2. Me(di)an Cy3 Intensity < Me(di)an Cy3 background intensity x Intensity lower bound

3. Standard deviation Cy3 Ratio > Ratio SD upper bound


Select control clonesOnly non-rejected clones will be selected as control clones.

• Manual selectionSelect clones with id = ‚91‘ or ‚k‘ or ‚K‘ or ‚?91‘ as control clone

• Automatic selection– No [AutoBand]

[CutoffPercentage] clones from the middle band

– [AutoBand]Select band around the median


Reduce control clonesSome of the control clones will be rejected ...

• [Cutoff Percentage]Reject the n smallest ratios

• Without [Cutoff Band]Reject the n largest ratios

• [Cutoff Band]Reject band around the median


Main calculation loop1. Calculate control means (the mean/median over all control clones/spots)2. Normalize ratios (subtract control mean from the ratio)3. Calculate tolerance value T

s standard deviation of the ratios of the observed clonen the number of valid spots in this clonet value of the t-statistic

significance niveau

4. [ Force T-Test ]Reject clones with T > [ Force T Value ]

5. [ C Check ]Replace tolerance values with possible greater values.

6. Find clone with maximum tolerance and reject it if its tolerance value T is > [ Force T Value ]

7. Perform [ T Test ] and evaluate result value.

Everything has to be recalculated if a control clone will be rejected.

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The C CheckThe clone tolerance values are now recalculated according to the following scheme:

If the new tolerance value is greater than the old T will be replaced by the new value

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The T TestIf [ Force T ] is set, the value will be set to the [ Force T Value ]

otherwise it is the greates tolerance value found in the clones.

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The T Test (2)

• No [ T Test ] : thresholding

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Calculation of the result value R• [ T Test ]

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In this routine the test T > [ Force T Value ] will be performed repeatedly


NCBI Clone Database

• Integration of the NCBI “component” database

• Automatically mapping of clone id’s to accession numbers, genomic clone locations and clone status information according to an up-to-date database

• Direct import of the NCBI file format


Accession-Number

Start-Base End-Base

Clone-State

Database-generated Information


Batch Processing

•One ore more file pairs can be added to a session

•All computations are performed simultaneous on the included datasets


Diagrams functions

• Ratio-profiles of multiple clone sets can be shown in one diagram


Ideogram Browser 1

• Independent portable Java application• Automation from MCGH-Analyzer with JNI• Generation of ideogram drawings from the NCBI

map database• Direct representation of gain and lost markers of

multiple clone sets• Scalable and scrollable graphs


Ideogram Browser 2


Software Structure 1

• Excel as convenient platform with widely known user interface for– Table representation– Diagram drawing– User interaction

• Windows DLL written in C++ for high performance using COM automation

• Platform-independent Java-Application for visualizing ideograms (can be docked to the DLL via JNI)


Software Structure 2


Future Features

• Copy number estimation– Global thresholds– Adaptive (local) thresholds

• Wavelets

• Adaptive weights smoothing

• NCBI database online update

• Interface to the R platform

08-10-2004MCGH Analyzer1 Hans A. Kestler André Müller.

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