Feature Extraction Software Training Insert AE Name and Date.

Feature Extraction Software Training

Insert AE Name and Date

Page 2Feature Extraction 7.5 Customer TrainingUpdated 08/18/04

Agenda

• Product structure and pricing

• Downloading and shipping of software and manual

• Installing Feature Extraction 7.5.1

• License types and redemption

• Activating FE software

• Data Workflow

• Features and Benefits of Feature Extraction software

• User Interface

• Grid Mode for Agilent and 3rd party microarrays

• Feature Extraction algorithms

• Feature Extraction results

• Known issues fixed in FE 7.5.1


Product Structure

Product Type Product # Option Description PricePreexisting G2565BA ScannerPreexisting G2566AA Agilent Scanner Software Kit

Includes: Feature Extraction, Analysis Viewer, and Scanner Control Softwares. Includes three licenses each for FE and viewer and one for Scanner Control.

New G2567AA Feature Extraction Initial License. 5,000$ Includes: Feature Extraction software on CDROM, perpetual license to use on 1workstation, manual.

Description Change G2568AA Feature Extraction Additional License. 3,000$ Includes: Perpetual license to useFeature Extraction software on 1workstation. Does not include media.Must have current initial license

New G4146AA Scanner Control Software Upgrade. 500$ Includes: CDROM, manual, perpetual license to use current Scanner Controlsoftware on 1 workstation

New G4147AA Feature Extraction Software Upgrade -$ Enables upgrade of existing FeatureExtraction software licenses. Allowsuse of current software version onspecified number of workstations

001 Initial and 2 Additional licenses 1,000$ Includes: Feature Extraction software onCDROM, manual, license to use on 1 initial and 2 additional workstations

002 Upgrade for 1 additional license only. 300$ Includes: License to use current versionof Feature Extraction software on 1additional workstation. Must have aninitial license for current version


Product Structure

Product Type Product # Option Description PricePreexisting G2565BA ScannerPreexisting G2566AA Agilent Scanner Software Kit

Includes: Feature Extraction, Analysis Viewer, and Scanner Control Softwares. Includes three licenses each for FE and viewer and one for Scanner Control.

New G2567AA Feature Extraction Initial License. 5,000$ Includes: Feature Extraction software on CDROM, perpetual license to use on 1workstation, manual.

Description Change G2568AA Feature Extraction Additional License. 3,000$ Includes: Perpetual license to useFeature Extraction software on 1workstation. Does not include media.Must have current initial license

New G4146AA Scanner Control Software Upgrade. 500$ Includes: CDROM, manual, perpetual license to use current Scanner Controlsoftware on 1 workstation

New G4147AA Feature Extraction Software Upgrade -$ Enables upgrade of existing FeatureExtraction software licenses. Allowsuse of current software version onspecified number of workstations

001 Initial and 2 Additional licenses 1,000$ Includes: Feature Extraction software onCDROM, manual, license to use on 1 initial and 2 additional workstations

002 Upgrade for 1 additional license only. 300$ Includes: License to use current versionof Feature Extraction software on 1additional workstation. Must have aninitial license for current version


Downloading FE 7.5.1 Software and Manual• FE 7.5.1 software and manual are available for

download from:

• Agilent website

http://www.chem.agilent.com/Scripts/PDS.asp?lPage=2547

• eRoom - Gene Expression Informatics Software > Fe_cd7.5.1

https://teamspace.agilent.com/eRoom/CAG2/ScannerSupport/0_52b2b

• EPI Warehouse

http://whadmin.cos.agilent.com/

• LSM website

http://lsbu.marketing.agilent.com/sb/sbshow.asp?nav_id=389
























Shipping of FE 7.5.1 Software and Manual• FE 7.5.1 software and hard-copy manual will be

shipped to:

• New scanner orders originating on or after June 16

• Upgrade service contracts originating on or after June 16

• Existing FE 7.1.1 customers will NOT receive FE 7.5.1 installation CD or hard-copy manual in the mail; Must download from Agilent website


Installing Feature Extraction 7.5.1

• Before installing FE 7.5.1, make sure that previous version of FE is uninstalled completely

• Delete all associated .dll files

• Un-installation of FE 7.1.1 will NOT delete the existing license file

• After FE 7.5.1 is installed, software will recognize and use the FE 7.1.1 license

• Compatibility and known issues with FE 7.5.1

• Internal tool version 1.7.5.1 is compatible with FE 7.5.1

• Concurrent (multiple) sessions of FE 7.5.1 is NOT supported on same PC


License Types

• Two types of licenses available:

• Node-locked licenses

• User must install Feature Extraction software on specific PC

• User must provide the host ID of PC that FE s/w is installed on

• 30-day demo licenses

• Software is fully functional but expires 30 days after date of issue

• Run on any PC

• FE 7.5.1 uses the SAME license file that FE 7.1.1 used

• This means free upgrade for existing FE 7.1.1 customers!!!

• Customers using beta versions of FE 7.4.x will need to upgrade to FE 7.5.1 by July 15 when the beta software self-inactivate


License Redemption

• License key is redeemed at Agilent website https://software.business.agilent.com

• Also, from FE 7.5 menu, click Help > Agilent License

• User must provide Agilent with the following:

• Order Number (available on Software Entitlement Certificate)

• Certificate Number (available on Software Entitlement Certificate)

• Host ID (available in FE 7.5, Help > About Analysis)

• Email address where the license will be sent to

https://software.business.agilent.com/


Access Host ID in Feature Extraction Software• Click Help > About

Analysis

• About Analysis dialog displays

• Software version

• Host ID (MAC/Ethernet address)


Where to Save the License Key

• License file name ends with “.lic“

• License file should be saved in this directory:

Program Files\Agilent\MicroArray


Activating Feature Extraction Software

• Software needs to be activated after it is installed and the license file is saved in directory


• To activate Feature Extraction software, open an image file

• FLEXlm License Finder dialog pops up asking for license file

• Select “Specify the License File” and browse to the directory where you have saved the license file



TIFF Image Analysis

Feature Extraction

JPEG

Text

GEML

QC File(print file)

PatternFile

Scanner software

Rosetta Resolver™ or Luminator™

Shape

MAGE-ML

Data FlowFeature Extraction

Result Files:


Features and Benefits ofFeature Extraction Software


What You Can Do with Image Analysis Tool• Visualize spots on microarray

• Change color and scale of image

• Flip and rotate image from landscape to portrait mode and vice versa

• Interactively position grids to find spots on microarrays

• Compare nominal spot centroid laid down by grid with centroid position for the spot

• Move centroid position to where you want it on the spot

• Select spots to ignore – these won’t be used in Feature Extraction

• Create histogram and line plots

• View visual results and outlier flags for features & backgrounds


What You Can Do with Feature Extraction Algorithms

• Find Spots – positions a grid and finds centroid positions of spots

• Spot Analyzer – removes outlier pixels and defines pixels for features & local backgrounds

• Poly Outlier Flagger – flags features and backgrounds that are non-uniformity outliers and population outliers

• Background Subtraction – corrects for the background and determines if background-adjusted signal is positive and significant from background

• Deletion Control (25mer in-situ) – corrects for cross-hybridization

• Dye Normalization – selects features for dye bias evaluation and corrects for dye bias

• Ratio – calculates log (rProcessedSignal/gProcessedSignal), log ratio error, and p-value of log ratio for each feature


Feature Extraction User Interface


User Interface

• Display panels

• Image Info (available when image is loaded)

• Single channel display

• Grid Definition (available when grid mode is on)

• Maximun Fit Movements for subgrids and spots

• Grid Adjustment (available when grid mode is on)

• Spot location (col, row)

• Spot center

• Ignore spot – The user can select spots that are to be ignored from analysis. No data including feature number, row, column information will be displayed in feature extraction output file.


Toolbar Buttons for Grid Mode

• Crop Mode On/Off

• On = Crop

• Off = Zoom

• Grid Mode On/Off

• Adjust Main Grid

• Adjust Subgrid

• Skew Subgrid

• Preview Spot Centroids

• Undo and Redo

• The Edit menu also has these options

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Zooming In and Out

• Cropping button in the OFF mode is used for zooming in on any boxed area

• Toolbar buttons

• Click View > Zoom, then select a magnification

• Mouse shortcuts

• To zoom in - Ctrl + left double click on the image

• To zoom out - Ctrl + right double click on the image

Zoom in Zoom out 100 percent


Tools Menu

• Tools > Flip Upper Left to Lower Right (Landscape/Portrait)

• Tools > Preferences (to set default options)

• Image View tab – Set initial window size, option to start with crop mode, image color, data range of image display, and more

• Grid Mode tab – Start grid mode with a gene list type, default view zoom setting, and maximum fit movements for grid adjustment

• Feature Extraction tab – Search for grid file or design file first when analyzing Agilent microarray, default save directory for result files, option to save log file, and FTP settings to send result files to Resolver and Luminator

• Graph View tab – Histogram bin size and bin number

• General tab – Hyperlink to Agilent web site


Demo User Interface


Using Grid Mode to AnalyzeAgilent and Non-Agilent

Microarrays


Grid Mode Analysis

• Ability to grid and feature extract Agilent and non-Agilent microarrays scanned on Agilent scanner

• New spot finding tool allows the user to interactively position and find spots on microarray

• Accepts annotation and array layout information via the following gene lists

• Agilent grid files

• Agilent design files

• GAL files (GenePix Array Layout)

• Tab-delimited text files


Setting Up an Initial Grid

• Grid file is required to feature extract non-Agilent microarrays

• Agilent microarrays can be feature extracted with grid file, if desired

• To create a grid, click on Grid Mode On/Off icon to select a gene list type to grid

• _grid.csv, gal, xml, tab text

• no gene list

• Grids are saved as two files (_grid.csv, _feat.csv) and can be used to grid and analyze other arrays of same layout

• Recommend to save grid file in same directory as image – this is where the software looks at when it needs a grid file


Benefits to using Grid Mode

• If no gene list is available, a grid can be created de novo

• If a gene list is selected, Feature Extraction uses the layout information and annotation from the gene list to grid the microarray

• Users can interactively adjust the main grid, subgrids, spots, and preview spot centroids

• Users can select spots to ignore from analysis

• Grid files can feature extract images that are too rotated and therefore cannot be used with Agilent design files (*.xml)

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Demo How to Grid a Microarray


Set Design File Search Path (Agilent Microarrays Only)

• Feature Extraction checks the design file search path to find the microarray design file

• Click Tools > Preferences > Feature Extraction > Design File Search Path

• Click Browse in the “Configure Directory Path for finding Design Files” dialog box

• Locate the directory containing the design file in the “Browse for Folder” dialog box

• Click Add in the “Configure Directory Path for finding Design Files” dialog box


How to Manually Specify a Grid/Design File• If Feature Extractor can not find a

grid file or design file, then the “Load Grid/Design File” dialog box appears

• Browse for grid or design file and then click the “Load” button

• To avoid having to manually specify a grid or design file, do the following:

• Select the preference of search order for Agilent grid or design file

• If a design file is to be used, make sure the design file search path has been properly set

• If a grid file is to be used, make sure the grid file is saved in same path as TIFF image file


Feature Extraction Input Files

• Input Files - Required

• TIFF image of Agilent and non-Agilent microarray scanned on Agilent scanner

• Array design file – describes the layout of probes and probe annotation

• Design file (.xml) – for Agilent microarrays

• Grid file (_grid.csv, feat.csv) – for Agilent and non-Agilent microarrays

• Input Files - Optional

• Printing File (cDNA microarrays only) – Contains cDNA clones that failed printing QC and are to be ignored from analysis.

• Location of printing file needs to be set in Design File Search Path


Feature Extraction Output Files

• GEML – Expression data in the GEML 1.0 format

• Can be exported to Rosetta Resolver and Luminator software

• MAGE – Expression data in the MAGE-ML format

• Can be exported to Rosetta Resolver 4.0 and future version of Luminator

• JPEG – Compressed version of image file in JPEG format

• Tab-delimited text – Expression data in tab-delimited text format

• Visual Result – “Shapes” annotation generated by and viewed in Feature Extraction

• Shows the feature size, local background region, raw signals, log ratio, gene name, non-uniformity and population outlier flags

• Allows for subsequent viewing of the “shapes” annotation without having to re-extract the scan image


MAGE-ML Result Files

• Feature Extraction result files can be saved in MAGE-ML format

• Microarray Gene Expression Markup Language (MAGE-ML) is a language designed to describe and communicate information about microarray based experiments

• MAGE-ML is based on XML and can describe microarray designs, microarray manufacturing information, microarray experiment setup and execution information, gene expression data and data analysis results.

• A format accepted by major public microarray databases such as ArrayExpress (EBI)and GEO (NIH)

http://www.ebi.ac.uk/arrayexpress/

http://www.ncbi.nlm.nih.gov/geo/


Exporting Files to Resolver/Luminator (Intranet)

• FTP transfer of files

• GEML or MAGE-ML results

• TIFF or JPEG images

• FTP settings

• Destination: enter name where Resolver or Luminator resides

• FTP port: enter FTP port #

• User name: enter user’s name

• Password: enter password


Demo Feature Extraction and the Algorithm Modules


Barcode, design ID, filename

Array dimensions, array pattern, feature size,feature layout, probe names, etc.

QC information, flagged features

Running Feature Extraction


Default Parameters in Feature Extraction Modules• Default parameters are loaded based on type of microarray,

design or grid file, and settings changes saved during a run

• Default check boxes are marked (on) or (off)

• Default radio buttons are marked (*)

• Default numbers are displayed in parentheses

• Default parameters are only recommended when Agilent’s complete system is used (i.e. Agilent labeling and hybridization protocols, Agilent microarrays, and Agilent scanner)

• If there is any deviation from Agilent’s complete system, users need to carry out experiments to fine tune the parameters

• If parameter numbers appear in red, it means that they are different from the values optimized for the Agilent microarray system

• Users need to carry out experiments to optimize these values for their microarrays and protocols


Where can you find the default parameters


Feature Extraction Algorithms


FindSpots Algorithm – Grid Initialization• Locates all spots on microarray

• Finds corner spots for grid placement

• Places initial or nominal grid based on location of corner spots, spot size and inter-spot distances obtained from the design file

• Finds bright spots (based on high intensity)

• Adjusts grid according to location of bright spots

• Finds dim spots by interpolating location from adjusted grid


FindSpots – Deviation Limit

• Dev Limit restricts how far a spot can deviate from the nominal grid position and still be called “found”

• Default deviation limit is automatically loaded

• User can change default deviation limit between 0-70 microns (Agilent arrays)

• Setting deviation limit too low can cause spots to be missed

• Setting deviation limit too high can cause spots in adjacent rows and columns to be swapped.


SpotAnalyzer Algorithm

• Determines which pixels represent the spot and the local background

• Spot size is determined by CookieCutter or WholeSpot method

• Optional: calculate spot size

• Local background area is determined by the radius distance

• Rejects outlier pixels in spot and local background based on

• Standard Deviation or Inter Quartile Range method (Default; more robust)

• Flags feature as saturated if > 50% of pixels remaining after outlier rejection have intensities above 65502


SpotAnalyzer – Spot Size and Spot Analysis Methods• Spot size is calculated when enabled in the UI

• Spot size determines the number of pixels that are chosen to represent a feature

• The spot size is reported with the final results as "SpotRadiusX" and "SpotRadiusY"

• Spot analysis methods use the spot size to define features

• For CookieCutter method

• Spot size is obtained from the XML design file or the calculation that user selects from SpotAnalyzer tab

• For WholeSpot method

• Spot size is obtained from spot size calculation that user selects from the SpotAnalyzer tab


SpotAnalyzer – What Defines Spot Size and Local Background

CookieCutter WholeSpot


SpotAnalyzer – Determination of Local Background Radius• Minimum local background radius (Default)

2 2x yScan Resolution .6 (Interspot spacing ) (Interspot spacing )n

• Adjusted local background radius (Max of n = 4)

x y0.6 Scan Resolution Max Interspot spacing , Interspot spacing

• Where n is minimum of 1 to maximum of 4 sets of closest neighbors

• n = 1 has at least 8 nearest neighbors




24 nearest neighbors (n =

2)

2

Self

• Maximum radius

2 2x yCEILING Scan Resolution 4.7 (Interspot spacing ) (Interspot spacing )

Example: CEILING [3.2] = 4


SpotAnalyzer – Pixel Rejection Based on Standard Deviation

• Pixel outlier rejection for features and backgrounds in both colors

• +/- 2 SD, encompasses ~ 95% distribution

• Feature intensity is mean signal of inlier pixels


SpotAnalyzer – Pixel Rejection Based on Inter Quartile Range• Interquartile Range (IQR) is

range of intensities under Gaussian distribution between the 25th and 75th percentile

• Pixels of feature and background are rejected if

Pixel Intensity > UpperRBPixel Intensity < LowerRB

75Upper percentile PixelOutlierRB I Cutoff 25Lower percentile PixelOutlierRB I Cutoff

75 25

1.42

where PixelOutlier

th th

Cutoff IQR

IQR Intensity Intensity

• ~ 99 % of the distribution encompassed between the lower and upper rejection boundaries, when using

1.42*IQR


PolyOutlierFlagger Algorithm

• Flags features and backgrounds as non-uniformity outliers based on statistical deviations from Agilent noise model:

• Polynomial Variance Model – expected variances from array manufacturing, wet lab chemistry, and scanner noise

• Flags feature and background as population outlier using:

• IQR Method – using intra-array replicate features and the associated background areas


PolyOutlierFlagger – NonUniformity OutlierExpected Variance Measured Variance

• x is mean signal of feature or background minus minimum signal feature or background on array

• A (Gaussian) – variance estimated from labeling and feature synthesis

• B (Poisson) – variance estimated from scanning measurement or counting error

• C (Constant) – variance expected from electronic scanner noise and glass background noise

2 2E Ax Bx C

2

2

Array

PixSDevA CV

MeanSignal MinSig

12 2

0

1( )

1

n

M ii

X Xn

• n = # inlier pixels in feature or background

• X = raw pixel intensity in feature or background

• X bar = raw mean signal of feature or background

2 2M E CI

Feature or background is flagged as non-uniformity outlier if:

where CI is confidence interval calculated from chi square

distribution


PolyOutlierFlagger – Population Outlier• Performs population

statistics on features and background areas if microarray has the minimum number of replicate features

• Feature or background is flagged as population outlier if:MeanSignal > UpperRBMeanSignal < LowerRB

75Upper percentile PopOutlierRB I Cutoff 25Lower percentile PopOutlierRB I Cutoff

75 25

1.42

where PixelOutlier

th th

Cutoff IQR

IQR Intensity Intensity

• ~ 99 % of the distribution encompassed between the lower and upper rejection boundaries, when using

1.42*IQR


PolyOutlierFlaggerPink triangles are features flagged as NonUnifOL


PolyOutlierFlaggerNon-Uniformity Outliers indicated by :Colored inner ring (Feature) or colored outer ring (Local_BG)

Feature appears “uniform”…

Try changing color scales,or, looking at single-channelwindow


BGSub Algorithm

• Estimates and corrects for systematic biases in data arising from:

• Substrate fluorescence

• Non-specific binding to substrate

• Possible biases introduced during scanning

• Artifacts from hyb and wash

• Determines if feature signal is significant compared to background

• Spatial detrend to correct for

• Adjusts background globally (to a user-defined value) to correct for under or over estimation of the background


BGSub - Background Subtraction Methods• No background subtraction

• This method does NOT subtract the background signal from the feature signal

• Feature raw signal (MeanSignal) is passed on to spatial detrend (if turned on)

• If “no background subtraction” method is selected, then by default, the background is not adjusted globally

• Local Method

• Local background (Radius method)

• Global Methods:

• Average of all background areas

• Average of negative control features

• Minimum signal (feature or background)

• Minimum signal (feature) on array ~ simulated negative control


BGSub - Order of Background Correction• Analysis flow for background correction is in this

order:

• Background subtraction method

• Spatial detrend, if it is turned on

• Feature significance test

• Adjust background globally, if it is turned on

• Feature signal is passed on to and processed by next method that is available


BGSub - Spatial Detrend Algorithm

• Decreases the contribution of any systematic signal gradient on the array to the “foreground” signal

• Estimates the surface of the “foreground” signal by picking dimmest 1-2% of the array feature intensities

• “Foreground” signal is the portion of feature signal that is not related to intended signal from dye-labeled target complementary to the probes on the feature

• SpatialDetrendSurfaceValue is determined for each feature per channel


The Problem – Differential Expression GradientFE 7.1.1 – Default Parameters

Down Regulated - 408

Col0 25 50 75 100 125 150 175 200

100

80

60

40

20

0

Up Regulated - 408

Col0 25 50 75 100 125 150 175 200

100

80

60

40

20

0

Up-regulated

Down-regulated


The Cause – Differential Expression Gradient

• Regional variations in the “foreground” are present on the microarrays

• In previous version of FE (v.7.1.1), the background subtraction method did not adequately measure these variations

• Background was underestimated in some regions of the microarray

• Consequently, log ratios and differential expression calls were inaccurate


The Approach – New Background Estimation Method• FE 7.1.1 default is local

background subtraction

• Estimates non-specific signal on feature based on intensity of area between features

• FE 7.5.1 default is no background subtraction with spatial detrend

• Estimates non-specific signal based on the dimmest 1% of feature intensities. This baseline is estimated regionally to account for variation.


Spatial Detrend – Estimating Foreground Intensity• A “FilteredSet” of features are identified in process known as Low Pass

Filter

• Features with dimmest 1% of feature intensity per window are selected

• If “no background subtraction” option is selected, then feature intensity is raw mean signal. If a background subtraction option is selected, then feature intensity is background subtracted signal.

• Window size is 10 columns x 10 rows of features

• Window is moving horizontally and vertically on array by increment of 5

• Foreground surface is estimated from the “FilteredSet” of features (i.e. features with dimmest 1% feature intensity per window)

• 2-D Loess algorithm fits a smooth surface through the “FilteredSet” of feature intensities using 20% nearest neighborhood filtered points

• For features NOT in the “FilteredSet”, a 2-D Loess algorithm with similar neighborhood size of filtered points is used to predict surface value for each feature

• Lastly, SpatialDetrendSurfaceValue is subtracted from MeanSignal (or BGSubSignal, if BG subtraction is selected) for each feature


Identify Features in FilteredSet by Low Pass Filter

Default: Window = 10, Increment = 5, Percentage = 1


Low Pass Filter Schematic – Effect of Moving Window on Sampling

Scatter Plot

Col0 25 50 75 100 125 150 175 200

100

80

60

40

20

0

Scatter Plot

Col0 25 50 75 100 125 150 175 200

100

80

60

40

20

0

Moving Window

No Moving Window


Low Pass Filter Schematic

Scatter Plot

Col0 25 50 75 100 125 150 175 200

100

80

60

40

20

0

Features from Low Pass Filter – Raw Green Intensities gMeanSignal


2D Loess Fit – Estimate Foreground Surface to All Features

gMeanSignal gSpatialDetrendSurfaceValue


The Solution – No Differential Expression GradientFE 7.5.1 Default Parameters

Down Regulated - 408

Col0 25 50 75 100 125 150 175 200

100

80

60

40

20

0

Up Regulated - 408

Col0 25 50 75 100 125 150 175 200

100

80

60

40

20

0

Up-regulated

Down-regulated


BGSub – Feature Significance and Well Above BGFeature Significance

Test

• Calculates significance of feature signal vs background signal (local or global) using:

• 2-sided Student’s t-test (implemented as an incomplete Beta Function approximation)

• Feature gets Boolean flag of 1 under the IsPositiveAndSignif column (in FE result file) if the calculated p-value is less than the user-defined max p-value

Well Above Background Test

• If background-subtracted signal is “well above” background as calculated by the equation below:

• And the feature passes the IsPositiveAndSignif test, then feature gets Boolean flag of 1 under the IsWellAboveBG column in Feature Extraction result file

Calculated MaxpValue pValue

BGSubSignal WellAboveSDMulti BGSDUsed


BGSub – Global Background Adjustment

• Background subtraction errors arise from inaccuracies in background estimation

• Basic ideas behind global background adjustment

• Adjusts for over or under-estimation of background in one channel over the other channel

• Corrects “hook” effect at the low-end intensity scale

• Applies background correction using curve fitting method to adjust the initial background-subtracted intensities


Adjust Background Globally to User-Specified Value• Global Background Adjust algorithm is same as in FE7.1.1:

• Evaluates background-subtracted signal and finds a rank consistent set of features with low signal

• Finds a constant in both channels that moved the median of these signals to zero

• In FE 7.5.1, user can enter a constant value between 0 to 500 to “pad” all feature signals to that value

• This will have the effect of compressing log ratios, but will decrease the variability (SD) in the log ratio between inter- and intra-array replicates.

• Note: The “pad” is an exploratory tool for variance stabilization. Customers are advised NOT to use the “pad” for production purposes.

• Reference: “Transformations…What For…Which One” by W. Huber

• http://www.ima.umn.edu/talks/workshops/9-29-10-3.2003/huber/whuber-vsn-ima-oct2003.htm

http://www.ima.umn.edu/talks/workshops/9-29-10-3.2003/huber/whuber-vsn-ima-oct2003.htm

















How is Adjust Background Globally Value Used• If red signal vs green signal plot has a slope of rank

consistent features > 1, then “pad” value chosen by user is assigned to green channel

• Pad value = 50 and slope = 1.2

• Value of 50 is added to the green background-subtracted signal all features

• Value of (50*1.2) = 60 is added to the red background-subtracted signal of all features

• Pad value = 50 and slope = 0.5

• Value of 50 is added to the red background-subtracted signal all features

• Value of (50/0.5) = 100 is added to the green background-subtracted signal of all features


BGSub – Features Used for Global BG Adjustment• Select a suitable subset of the entire dataset (probes) for applying the

global adjustment algorithm Select Features w/ no or negligible differential expression (i.e. Rank Consistency Filter - Features along the central tendency line (red) of the distribution)

Basic filters for feature selection:

1. Control type = 0

2. Non Population Outlier

3. Non Non-Uniformity Outlier

4. Pass Rank Consistency Filter

r(M

ean

Sig

nal

)

g(MeanSignal)


Feature Number

Intensity_RIR

Rank_R

R

Intensity_G

IG

Rank_GG

1 30 1 560 5

2 170 5 390 4

3 99 4 146 1

4 360 6 452 6

5 43 2 300 3

6 45 3 149 2

7 423 7 700 7

R

G

Identify Features along the Central Tendency Line – Rank Consistency Filter

• Compute a correlation strength per feature

• Transform(Intensity) RankCorrelation Strength per feature = |R - G |/(Features)

where : threshold percentile

• If you compare the rank of a given feature in R & G channels the ranks should be within percentile.

• Example: A feature should be correlated in R & G channels within 5%ile =0.05 A feature should be correlated in R & G channels within 15%ile =0.15


R

G0,0

1. Identify features that pass Rank Consistency Filter

2. Below X%ile cutoff

5. Y%ile cutoff

6. Compute IRMedian & IGMedian

M’ = IRMedian/IGMedian

M’Projected

M’ projected to median fit line

IGMedian_proj = GBGOffset = gBGAdjust

IRMedian_proj = RBGOffset = rBGAdjust

3. Median fit to distribution(orange)

4. Add negative controls

BGSub – Calculation of Global BG Adjust Values• Algorithm determines

offset in red and green channels using the features near the central tendency of the data, especially in the lower intensity range


BGSubSignal Calculation

• BGSubSignal = MeanSignal – BGUsed

where BGSubSignal and BGUsed depend on type of background method and settings for spatial detrend and global background adjust


BGSub – Before Global Background Adjustment

• After background subtraction, a green or red bias may exist at low signal intensity

• If this bias is uncorrected, the log ratio vs. signal plot of a “self” array will not be symmetric about the log ratio axis


BGSub – After Global Background Adjustment• The background

adjustment algorithm corrects the bias in both the red and green channels

• The resulting log ratio vs. signal plot is symmetrical around the log ratio axis for a “self” array


New Default Parameters

BGSub Tab Ratio Tab

Array Type

Background Subtraction

Method*

Spatial Detrend

Adjust Background Globally

Auto-estimate Additive Errors**

InSitu No background subtraction On Off On

cDNA No background subtraction On Off On

8x Format

Local background subtraction

Off Off On

Grid Files

Minimum signal from

featureOff On (to 0) Off

Agilent


DyeNorm Algorithm

• Estimates and corrects for dye bias arising from systematic variation like:

• Differences in labeling efficiency between two dyes

• Differences in power settings of two lasers

• Selects features used as normalization set to evaluate the dye bias

• Optional – Omit feature with background PopOL from the normalization set

• Computes dye normalization factors and corrects dye bias using:

• Linear (Global Method)

• Linear&LOWESS (LOWESS method preceded by linear method)

• LOWESS (Local or Non-Parametric Method)


DyeNorm – Methods for Normalization Feature Selection

• Selects a set of normalization features to evaluate the dye bias

• Rank Consistency Filter (Default)

• Use features falling within central tendency of the data, having consistent trends between the red and green channels

• “Real-time house keeping genes”

• Use all significant, non-control, and non-outlier features

• IsPosAndSignif = 1 for each channel

• ControlType = 0 for each channel

• IsFeatNonUnifOL, IsFeatNonPopnOL, and IsSaturated = 0 for each channel

• Use a list of normalization genes

• House keeping genes or genes that should not be differentially expressed


Feature Number

Intensity_RIR

Rank_R

R

Intensity_G

IG

Rank_GG

1 30 1 560 5

2 170 5 390 4

3 99 4 146 1

4 360 6 452 6

5 43 2 300 3

6 45 3 149 2

7 423 7 700 7

R

G

Identify Features along the Central Tendency Line – Rank Consistency Filter

• Compute a correlation strength per feature

• Transform(Intensity) RankCorrelation Strength per feature = |R - G |/(Features)

where : threshold percentile

• If you compare the rank of a given feature in R & G channels the ranks should be within percentile.

• Example: A feature should be correlated in R & G channels within 5%ile =0.05 A feature should be correlated in R & G channels within 15%ile =0.15


Features selected using the Rank Consistency Filter


DyeNorm – Linear Normalization Method

• Assumes dye bias is NOT intensity-dependent

• A global approach to dye normalization – forces the average log ratio to zero

• Problem with this approach – not adequate for cases where bias is intensity-dependent

• A global constant is determined separately for red and green channels

• LinearDyeNormFactor is calculated such that geometric mean of the normalization features equals 1000

• For example, geometric mean of the normalization features is 250, then the LinearDyeNormFactor is 4


DyeNorm – LOWESS Normalization Method

• LOWESS is locally weighted linear regression

• Handles data that has intensity-dependent dye bias

• Fits the locally weighted linear regression curve to the normalization features (chosen from selection method)

• Determines the amount of dye bias from the curve for each feature’s intensity

• Each feature gets different LOWESS dye normalization factor for each channel


DyeNorm – Calculation of DyeNormFactor (DNF)• For Linear dye normalization method:

• For Linear&LOWESS dye normalization method:

• For LOWESS dye normalization method:

&DyeNormSignal

Linear LOWESSDyeNormFactorBGSubSignal LinearDyeNormFactor

DyeNormSignalLOWESSDyeNormFactor

BGSubSignal

101

1log

1000

10

n

ii

BGSubSignaln

LinearDyeNormFactor

where n is # features in the normalization set (i.e. features with IsNormalization = 1)


X

Y

Linear Fit: y = Slope*x + Intercept + scatter

y = m*x + c + e

Assumptions in Linear Fit:1. Scatter is Gaussian about a Mean = 02. Standard Deviation of scatter about a point on the curve is independent of the x-variable.

DyeNorm – Linear vs Non-Linear Fit


X

Y

DyeNorm – LOWESSLocally Weighted Linear Regression


DyeNorm – LOWESSChanging the Granularity of the Fit

X

YX

Y


DyeNorm – Calculating Dye Norm Signal• Dye normalized signal is calculated per feature per

channel

if Linear method is used

& if Linear&LOWESS method is used

if LOWESS method is used

where

DNF LinearDyeNormFactor

DNF Linear LOWESSDyeNormFactor

DNF LOWESSDyeNormFactor

DyeNormSignal BGSubSignal DNF


Ratio Algorithm

• Calculates the log ratio of red signal over green signal

Log(rProcessedSignal/gProcessedSignal)

• Calculates significance of log ratio

• Log ratio error

• p-value

• Determines if feature is differentially expressed according to the error model used

• Auto-estimate additive error values

• Applies surrogate values to dye normalized signals for more accurate and reproducible log ratio


Ratio – Error Models

• Three error models available to estimate random error on log ratio

• Agilent’s propagated error method based on pixel-level statistics

• Rosetta’s Universal Error Model (UEM)

• More conservative error estimate between propagated error and UEM (Default)

• p-value calculated is based upon the probability of log ratio = 0

• Recommend using the more conservative error estimate between propagated error and UEM


Ratio – Propagated Error vs Universal ErrorPropagated Error Model

• Measures the error on the log ratio by propagating the pixel-level error from calculations made in the analysis (e.g. raw signal and background subtraction)

• Good at capturing the error at the low intensity level

• Underestimates error at the mid to high intensity level

Universal Error Model

• Measures the expected error between the red and green channels using the additive and multiplicative errors

• Additive - constant noise term that dominates at low intensity level

• Multiplicative – intensity scaled term that dominates at high intensity level

• Good at capturing the error at mid to high intensity level

• Underestimates error in noisy features, especially at low signal ranges

Most conservative estimate of Propagated Error Model and Universal Error Model

(Recommended)

Evaluates both error models and reports the higher (more conservative estimate) p-value of two error models


Auto-estimate Additive Error Values• In FE 7.1.1, a default additive error constant (25) is used for Agilent

in-situ arrays processed using Agilent protocols and scanner.

• In FE 7.5.1, the additive error value is auto-estimated for each array per channel by looking at:

• Standard deviation of negative control features

• Spatial variability of spatial detrend surface. This is RMS difference between each point on the surface and mean of surface.

• Note: Arrays with less than 500 features will use only negative control features to calculate auto-estimate additive error because the surface cannot be fitted through small number of data points.

• Auto-estimate of additive error should be used with spatial detrend option turned on

• Note: Selection of spatial detrend option is independent of selection of auto-estimate of additive error option. Spatial detrend surface will be determined for use of auto-estimate but it won’t be subtracted from data if “spatial detrend” option is NOT selected.


Ratio – Use of Surrogates

• Log ratios are calculated from red and green dye normalized signals

• Dye normalized signals cannot be used to calculate log ratio if:

• BGSubSignal fails the IsPosAndSignif test

• BGSubSignal is less than its background standard deviation (i.e. BGSDUsed)

• If the above cases occur, a surrogate value is used instead of DyeNormSignal

• Surrogate value is calculated as 1 SD of BG intensities x DyeNormFactor

• For local background method, SD of BG is at pixel-level of local background

• For global background method, SD of BG is at background population level on array

• If surrogate is used, then a non-zero value is displayed in SurrogateUsed column and ProcessedSignal = SurrogateUsed

• If surrogate is not used, then a zero value is displayed in SurrogateUsed column and ProcessedSignal = DyeNormSignal


Surrogates: If signal is around the background signal, use Background_SD * DyeNormFactorCase 1: R/G

Both channels use DyeNormSignals;

• p-value and log ratio are calculated as usual. Log ratio error is calculated according to error model chosen by the user.

Case 2: r/G

r = rSurrogateUsed

G = gDyeNormSignal;

• p-value and log ratio are calculated as usual.

• If r/G > 1, then FE software automatically sets LogRatio = 0 and pValueLogRatio = 1

Case 3: R/g

R = rDyeNormSignal

g = gSurrogateUsed;

• p-value and log ratio are calculated as usual.

• If R/g < 1, then FE software automatically sets LogRatio = 0 and pValueLogRatio = 1

Case 4: r/g

Both channels use surrogates;

FE software automatically sets

LogRatio = 0 and pValueLogRatio = 1

• For signals using surrogates, the g(r)ProcessedSignal is equal to g(r)SurrogateUsed value, used to calculate log ratio.


Ratio – pValue and Log Ratio Error Calculations

12 2

xDev xDevpvalue Erf Erfc

Equation 1

LogRatioxDev

LogRatioError

Equation 2

xdev is deviation of LogRatio from 0. This is analogous to a signal to noise metric. xDev is displayed in the FEATURES table in FE result file.


Feature Extraction Results


Feature Extraction Visual Results

• Click View > Extraction Results

• View Results

• View Outlier Only

• Hide Outer Local BG Ring

• Use Simple Colors

• Click Help > Feature Extraction Output Quick Reference

• Shape visual results can be viewed only with Feature Extraction

• .shp files from v.7.1 cannot be opened with v.6.1.1 and earlier


Feature Extraction Text Results


Know Issues Fixed in FE 7.5.1

• Know issues fixed in FE 7.5 are available in the Release Note

• Release Note 7.5 is on the installation CD

• Downloadable from eRoom and EPI Warehouse


Known Typographical Errors in FE 7.5.1 Manual• The following equations are correct and will amended

in version 1.1 of FE 7.5.1 manual (p. 216), which will be available on Agilent website

• [r,g]SpatialDetrendRMSFit

• [r,g]SpatialDetrendRMSFilteredMinusFit

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1

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Visit our website for current info on Feature Extraction Softwarehttp://www.chem.agilent.com/Scripts/PDS.asp?lPage=2547• Download latest:

• Software

• 30-day License

• Example Images

• User Manual

• Technical Notes

• View software showcase

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