microarray image processing and quality control

Journal of VLSI Signal Processing 38, 211–226, 2004c© 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.

Microarray Image Processing and Quality Control

ANTON PETROV AND SOHEIL SHAMSBioDiscovery Inc., 100 N. Sepulveda Blvd., Suite 1230, El Segundo, CA 90245, USA

Received April 11, 2003; Revised August 15, 2003; Accepted September 5, 2003

Abstract. Image processing is an important stage of every microarray experiment. Reliability of this stage stronglyinfluences the results of data analysis performed on extracted gene expressions. Multiple methods related to arrayrecognition, spot segmentation and measurement extraction have emerged in this area over past several years.Currently there are various commercial and freeware packages available, which perform microarray image analysis.This paper attempts to review microarray image analysis as a whole and to make some experimental comparisonof several computational schemes for signal segmentation and measurement extraction. Also we provide a detaileddiscussion of automated image quality control for use with microarray images.

Keywords: microarray image processing, image segmentation, microarray quality control, microarray batchautomation, cDNA microarrays

1. Introduction

DNA and protein array experiments involve a rathercomplex multi-step process, which generates largequantities of data associated with the experiment aswell as the related experimental conditions. Microar-rays, also known as biochips, have found many ap-plications in the life sciences. Although initially usedfor measuring gene expression values, they have nowbeen applied to a number of related experiments to takeadvantage of the highly parallel measurement featureoffered by array technology. Some examples includeprotein arrays, antibody arrays, CGH arrays, SNP ar-rays, and so forth. Since the majority of applications ofmicroarrays continue to be for measuring gene expres-sion values, in this paper we will assume the arrays areused for this purpose. However, the image processingapproaches described here are applicable to any afore-mentioned utilization of microarrays (e.g., protein ar-rays).

There are three distinct areas of array data man-agement [1]. The first involves keeping track of thedata generated at the stages of chip production, sam-ple preparation, and array hybridization [2]. The sec-ond is associated with microarray image capturing and

analysis leading to array quantification. The third is toanalyze and mine the quantified image data in order toobtain biologically relevant information [3]. Figure 1demonstrates an overview of microarray experimentalprocess. In this paper we will primarily concentrate onthe issues related to array image analysis step of thisprocess.

Microarray images consist of arrays of spots ar-ranged in rectangular groups called sub-grids. All thesub-grids usually, but not always, have the same num-bers of rows and columns of spots. These “sub-grids”,called also “quadrants”, are arranged in relatively equalspacing relative to each other, forming a “meta-array”.The goal of array image processing is to measure theintensity levels of these spots, quantifying gene expres-sion values based on these intensities, assessing the re-liability of the data, identifying defects or unreliablemeasurements and generating appropriate warnings.

Ideally, a simple computer program could easily ac-complish the basic image-processing task by superim-posing a uniformly spaced array of circles with defineddimensions and spacing on the images. In theory, thepixels falling inside and outside these circles would beconsidered signal and background, respectively. In re-ality, however, the exact location of each sub-grid, and

212 Petrov and Shams

Figure 1. Microarray experiment process flow.

each individual spot within each sub-grid, may varyfrom slide to slide. These variations can be attributedto a number of factors, including, the reproducibilityand accuracy of the mechanical spotting process, hy-bridization inconsistencies, surface chemistry and soforth. The problem of inconsistent spotting necessitatesa reliable approach for automatic spot finding.

Contamination is another major issue for microarrayimage processing. Contaminants, such as dust, impuri-ties in and on the slide, dirt, splashes and drips of DNAsolution from pins, and alike, often result in very brightareas in the scanned image of a microarray. Figure 3shows an example of a small contaminant in a microar-ray spot image. Small contaminants distributed in thebackground as well as inside and adjacent to the mi-croarray spots, are well noticed in many array images.The best solution to the contamination problem is touse replicate spots and/or arrays. This way if one of thereplicated spots is contaminated the other values can beused to detect an outlier for removal. However, this ap-proach might not be possible in many cases due to extracost or limitation on the amount of source RNA. There-fore, image-processing methods can be used to providethe best estimate of the true signal level by identify-ing the contamination and removing it before applyingmeasurements. Various segmentation algorithms canbe used to identify correct signal, background and con-tamination regions for every spot. We will discuss someof them in this paper.

Choice of a specific measurement in order to charac-terize intensity level of a spot is also considered a veryimportant step in microarray analysis. One can choosebetween mean, median, mode, or total intensity valueof all pixels associated with a segmented spot image torepresent the gene’s expression level. Moreover, when amicroarray experiment involves two channels, a control(reference) channel as well as the experimental chan-nel, ratios between the channels for the above measurescan also be used to represent the gene’s expression

“fold-change”. Additionally pixel-by-pixel ratios canbe used as a relative regulation measure. In this paperwe will analyze several different methods of absoluteand relative measurement extraction.

Additionally every researcher tends to be concernedwith one or another kind of quality assessment of themeasurements that are generated by image processing.A minimum requirement for an ideal image processingsystem would be to be able to exclude “low-quality”spots from further analysis. Even at this stage there is noagreement on how to define a “low-quality” spot. Thisproblem is further compounded for assessing spot’squality quantitatively for use of this information in theexpression analysis step. Important results regardingour approach in producing a reliable and consistentquality control procedure for microarrays will be dis-cussed later in the paper.

Finally, a microarray experiment is a typical exam-ple of a high-throughput research demanding a reliableautomated image processing to speed-up the discoveryworkflow and to reduce human error. Although severalattempts in providing such systems have been made incommercial and academic software tools, the majorityhave had limited success in handling the wide range ofmicroarray images. We will discuss why this is a partic-ularly challenging problem and our successful attemptat meeting this challenge.

The microarray image processing involves a num-ber of well-established steps. It begins with a capturedmicroarray image as input and finishes with the outputof quantified gene expression values as well as poten-tial quality measurements and flags. Figure 2 belowdemonstrates this process. In the following sections ofthe paper we will discuss each step of this process indetail.

2. Image Alignment, Grid Placement,and Spot Finding

Image Alignment

A very common approach in microarray experimentsinvolves the use of a two-channel system. In such ex-periments, a microarray is hybridized using a mixturecontaining two differently labeled samples. One labelis used for the control and the other is used for la-beling the RNA from the experimental condition. Forexample, healthy tissue RNA can be labeled with theCy3 dye and the corresponding diseased tissue RNA la-beled with the Cy5 dye. After the hybridization of this

Microarray Image Processing and Quality Control 213

Figure 2. Microarray image analysis process. The dashed boxes refer to optional operations.

combined pool of labeled RNA, the scanner will gen-erate two images from the array, one for each labeledchannel. It is not usually necessary to process theseimages simultaneously. However, one might need tooverlay these images for visual inspection or for pixel-by-pixel ratio quantification method. For this reason,an initial image alignment stage might be necessarywhen dealing with two channel images. Both manualand automatic image alignment can be performed. Inthe manual mode the user will need tools for two ba-sic image manipulation operations: image rotation andtranslation. In automated mode both image rotation andtranslation operations will be performed without userintervention. Various algorithms based on matched fil-tering and feature extraction can be utilized for the au-tomated approach. The results of such algorithms areusually very stable and reliable. This is explained bythe fact that aligned images represent screenshots of thesame array and therefore are relatively easy to match.

Grid Placement and Spot Finding

Once the microarray images are loaded and potentiallyaligned, the next step is to create a grid having a layoutdefined by the user (a set number of rows, columns,spacing, etc.). Although it is possible to detect the gridlayout without any input from the user, this is not agood general approach as in many cases with arrayshaving few expressed genes or incomplete sub-grids ormultiple fields, automatic grid creation will fail. Sincethe user always has information about the grid design,we assume that this information is readily available forinput to the image processing software. Once the gridis created and laid on the array image, each individualspot must be found.

The goal of a spot finding operation is to locate thesignal spots in images and estimate size of each spot.There are two different levels of sophistication in the

algorithms for spot finding, corresponding to the de-gree of human intervention in the process. These aredescribed below in order most to least amount of man-ual intervention.

Manual Spot Finding

This method is essentially a computer-aided image pro-cessing approach. The computer provides tools to allowthe users to identify the signal spots in the image. Typi-cally, a grid frame is provided which the user can manu-ally place on the image and manipulate to fit the spatialextent of the spots in the image. Because the spots inthe image may not be evenly spaced, the user may needto adjust the grid lines individually to align with the ar-rayed spots. The user may also have to adjust some orall grid points to land onto the spots in the image. Thesize of each circle may also need manual adjustmentto fit to the size of each particular spot. To conductan accurate measurement, this method is prohibitivelytime-consuming and labor intensive. Thus considerableinaccuracy of the data may be introduced due to humanerror, historically, this was the first method available formicroarray image analysis.

Automatic Spot Finding

The ultimate goal of array image processing is to buildan automatic system to find the spots without the needfor any human intervention. This method would greatlyreduce the human effort, minimize the potential forhuman error, and offer a great deal of consistency in thequality of data. Such a processing system would requirethe user to only specify the expected configuration ofthe array (e.g., number of rows and columns of spots).The system would automatically search the image forthe grid position. Most of the methods used for this


purpose are based on some type of matched filtering.Matched filtering can be applied row- or column-wiseor for the whole grid.

Having found the approximate grid position, whichspecifies the center of each spot, the neighborhood canbe examined to detect signal and background. Knowl-edge about the image characteristics should be incor-porated to account for variability in microarray im-ages. Spot location, size, and shape should be ad-justed to accommodate for noise, contamination anduneven distribution. The quality of data is high becausethis approach is unbiased. Human intervention andpossibilities for errors are minimized.

3. Spatial Segmentation of Signaland Background Pixels

After the spot location is determined in the image, asmall patch around that location (target region) can beused to quantify the spot expression level. The nextstep is to determine which pixels in the target regionare due to the actual spot signal, which are background,and which are neither (e.g., contaminations). This op-eration is called signal or image segmentation in com-puter vision terminology. A number of methods havebeen developed with different levels of sophistication.We will discuss some of these below.

Pure Spatial Image Segmentation

Methods of this class use purely spatial informationfrom the result of spot finding to segment out signalpixels. After the spot finding operation has been com-pleted, the location and size of the spot is determined.A circular mask of the computed size is placed in the

Figure 3. Circular segmentation results. The red pixels denote signal area and the green pixels denote background. Black colored pixels in thepanel labeled “segmented” indicate ignored pixels. Note that the spot is not perfectly circular and part of the spot falls outside the signal area.

image at the determined position to separate the signalfrom the background. It is assumed that the pixels insideof the circle are due to the true signal and those outsideare background. The measurements are then performedon these segmented pixels. Pure spatial segmentationis optimal when the spot finding operation is effective,i.e., spots have been correctly located and sized, thespot shapes are close to perfect circles, and no contam-ination is present [4]. Knowing the configuration of thearray and the spacing between spots, the user can spec-ify the number of pixels around the spot that can be usedto compute the background value. However, irregular-ity of the spot shape and sizes are more like rules thanexception in many microarray images. Whenever theseconditions happen, the accuracy of the measurementsis largely compromised. In addition, spot contamina-tion is still a large issue in many microarray images. Inreal life, such a procedure, being computationally in-expensive on one hand, becomes critically inaccurateon the other hand (Fig. 3).

Pure Intensity Based Signal Segmentation

Methods of this class use intensity informationexclusively to segment out signal pixels from the back-ground. They assume that the signal pixels are statis-tically brighter than the background pixels. A set per-centage of pixels with the highest intensity values areconsidered the signal. Although this method was em-ployed with early image analysis systems, currently itis not used by most systems due to high error rates.An improvement over the simple intensity-only seg-mentation is to use various clustering procedures togroup pixels into background and signal groups [5].Such method seems very natural at the first glance. Onecan use k-means or k-medians clustering [6] to extractsignal and background regions. This operation will find


an optimal arrangement of pixel intensities to maxi-mize difference between cluster levels and minimizewithin-cluster variance at the same time. Obviously,this approach highly depends on a choice of distancemetric in intensity space.

Such extraction will be robust with respect to thespot’s location within considered region and with re-spect to the spot’s shape. However, since this methodcompletely ignores spatial information about pixel ar-rangement, outcome can be completely unpredictablein a case of low SNR. In this case decision on whereto separate signal and background will rely mainly onmetric properties. In addition one has to make a deci-sion regarding number of clusters used, basically ana-lyzing the image on contamination presence. Such de-cision can be very difficult since contamination can takevery complicated configurations and may need severalclusters to accommodate.

Mann-Whitney Segmentation

Based on the result of the spot finding operation, a circleis placed in the target region to include the spatial regionof the spot. Because the pixels outside of the circle areassumed to be the background, the statistical propertiesof these background pixels can be used to determinewhich pixels inside the circle are signal pixels. Mann-Whitney test is used to obtain a threshold intensity level[7, 8].

Briefly Mann-Whitney segmentation can be done asfollows. Randomly select some number of pixels fromthe background (say, 10 pixels), their intensity valueswill form the first sample. Select same number of sig-nal pixels (again, 10 pixels), but choose those that havethe lowest intensity values within the signal mask. Testtwo samples on having the same means using Mann-Whitney test. This test is selected because of its ro-bustness with respect to distribution assumptions. If thesamples pass the test, replace some pre-defined num-ber of pixels from the signal sample with those not yetin the sample and having lowest intensity values. Test-ing will continue until two samples fail the test. Pixelsinside of the circle having a higher intensity than thelowest of those in the signal sample are identified assignal.

This method works very well when the spot loca-tion is correctly found and there is no contamination inthe image. However, when contamination pixels existinside of the circle, they will be determined as signalpixels. This is due to the fact that typical contamina-

tion will be seen as specular reflection and has intensityhigher than the background. If there are contaminationpixels outside of the circle, or the spot location is notfound correctly, such that some of the signal pixels areoutside of the circle, these high intensity pixels willraise the intensity threshold level. Consequently, thesignal pixels with their intensity lower than the thresh-old will be misclassified as background. This methodalso has its limitations when dealing with weak signaland noisy images. This scenario is similar to what hasbeen discussed in the pure intensity based segmentationmethod.

The Method of Trimmed Measurements

This approach is another method that combines bothspatial and intensity information in segmenting thesignal pixels from the background. The logic of thismethod proceeds as follow. After the spot is localizedand a target circle is placed in the target region, most ofthe pixels inside of the circle are signal pixels and mostof the background pixels are outside of the circle. Dueto the shape irregularity, some signal pixels may leakout of the circle and some background pixels may getinto the circle. These pixels may be considered as out-liers in the samples of the signal and background pixels.Similarly, contamination pixels may also be consideredas outliers in the intensity domain. These outliers willseverely change the measurement of the mean and to-tal signal intensity. To remove the effect of outliers onthese measurements, one may simply “trim-off” a fixedpercentage of pixels from the intensity distribution ofthe pixels for both signal and background regions (seeFig. 4). However, one never knows a priori what outlierpercentage would be “right” for a spot and again, suchan approach can produce inaccurate results.

Integrating Spatial and Intensity Informationfor Signal Segmentation

The two methods discussed above use minimal amountof spatial information, i.e. the target circle obtainedfrom spot localization is not used to improve the detec-tion of signal pixels. Their design priority is to makethe measurements of the intensity of the spots withminimal computation. These methods are useful insemi-automatic image processing because the speedhas strong priority and the user can visually inspect thequality of data.


Figure 4. Results of “trimmed measurements” segmentation. Note that the small contamination in the signal area has been successfully removedalong with other true signal pixels.

In a fully automated image processing system, theaccuracy of the signal pixel classification becomes acentral concern. Not only does the correct segmen-tation of signal pixels offer accurate measurement ofthe signal intensity, but it also permits multiple qualitymeasurements based on the geometric properties of thespots. These quality measures can be used to draw theattention of a human inspector to spots having question-able quality values after the completion of an automatedanalysis. The correct classification of the signal pixelscan be realized by algorithms that use information fromboth spatial and intensity domain (hybrid methods) [2,4, 5, 8]. Spatial information should be used to not onlyinitialize signal/background mask, but also to optimizerelative spatial arrangement of pixels from same class.This is why one of the most popular approaches utilizedfor hybrid procedures is region-growing [9–11]. Youcan see a sample output of such procedure on Fig. 5.

An experimental comparison between the hybridand pure spatial segmentation methods is presented inSection 7.

Figure 5. Segmentation results of a “hybrid” method. Note that the pixels associated with the small contaminant have been successfullyremoved from the signal area and the signal area has matched the non-circular spot shape to include all signal pixels.

4. Data Quantification

The key information that needs to be recorded frommicroarrays is the expression strength of each target.In gene expression studies, one is typically interestedin the difference in expression levels between the testand reference mRNA populations. This translates todifferences in the function of intensities on the twoimages. Under idealized conditions, the total florescentintensity from a spot is proportional to the expressionstrength. However, those idealized conditions almostnever exist. Usually spots differ in their shapes andsizes even throughout the same array.

When contamination is on the spot, the signal in-tensity covered by the contaminated region is notmeasurable. The image processing may not correctlyidentify all the signal pixels, thus, the quantificationmethods should be designed to address these prob-lems. The common values computed after segmenta-tion are total, mean, median, mode, volume, intensityratio, and the correlation ratio across two channels. The


underlying principle for judging which one is the bestmethod is based on how well each of these measure-ments correlates to the amount of the DNA probepresent at each spot location.

Total. The total signal intensity is the sum of the in-tensity values of all the pixels in the signal region. Asit has been indicated above, total intensity is sensitiveto the variation of the amount of DNA deposited on thespot, the existence of contamination, and the anomaliesin the image processing operation. Because these prob-lems occur frequently, the total may not be an accuratemeasurement.

Mean. The mean signal intensity is the average in-tensity of the signal pixels. This method has certainadvantages over the total. Very often the spot size cor-relates to the DNA concentration in the wells during thespotting processing. Measuring the mean will reducethe error caused by the variation of the amount of DNAdeposited on the spot. With advanced image processingallowing for accurate segmentation of contaminationpixels from the signal pixels, the mean should be oneof the choice measurement methods.

Median. The median of the signal intensity is theintensity value that splits the distribution of the sig-nal pixels in half. The number of pixels above themedian intensity is the same as those below. Thus,this value is a landmark in the intensity distributionprofile. The advantage of choosing this landmark asthe measurement is due to its robustness to outliers.As has been discussed in the last section, contami-nation and problems in the image processing oper-ation introduce outliers in the sample of identifiedsignal pixels. The mean measurement is very vulner-able to these outliers. Thus, if the image processingoperation is not sophisticated enough to ensure thecorrect identification of signal, background, and con-tamination pixels, median is a better choice than themean.

Mode. The mode of the signal intensity is the “most-likely” intensity value and can be measured as the in-tensity level corresponding to the peak of the intensityhistogram. It is also a landmark in the intensity dis-tribution. Thus, it enjoys the same robustness againstoutliers offered by the median. The trade-off is that themode becomes a biased estimate when the distributionis multi-modal. This is because the mode value will be

equal to one of the modes in the distribution dependingon which one is the highest.

Volume. The volume of signal intensity is the sum ofthe signal intensity above the background intensity. Itmay be computed as (mean of signal–mean of back-ground) ∗ area of the signal. This method provides anaccurate measure of the amount of labeled material inthe sample given that the spotted material, the probe,concentration is higher than the labeled material, thetarget, and all of the target material are attached to theprobes at the end of hybridization. However, the aboveconditions might not hold in general.

Intensity Ratio. If the hybridization experiments aredone in two channels, then the intensity ratio betweenthe channels might be the only quantified value of in-terest. This value will be insensitive to variations inthe exact amount of DNA spotted since the ratio be-tween the two channels is being measured. This ratiocan be obtained from the mean, median, or mode of theintensity measurement for each channel.

Correlation Ratio. Another way of computing theintensity ratio is to perform correlation analysis acrossthe corresponding pixels in two channels of the sameslide. It computes the ratio between the pixels in twochannels by fitting a straight line through a scatter plotof intensities of individual pixels. This line must passthrough the origin and the slope of it is the intensity ratiobetween the two channels. This is also known as regres-sion ratio. Estimation of the slope can be done usingvarious techniques (mean ratio, median ratio, weighedratio etc.). This method may be effective when signalintensity is much higher than the background inten-sity. The motivation behind using this method is to by-pass the signal pixel identification process. However,for spots of moderate to low intensities, the backgroundpixels may severely bias the ratio estimation of the sig-nal towards the ratio of the background intensity. Thenthe advantage of applying this method becomes un-clear and the procedure suffers the same complicationsencountered in the signal pixel identification methodsdiscussed above. Thus its advantage over intensity ra-tio method may not be warranted. One remedy to thisproblem is to identify the signal pixels first before per-forming correlation analysis. In this case it becomesunclear why use correlation ratio instead of intensityratio. However, some specialists prefer this approachover previously described intensity ratio arguing thatcorrelation ratio has better distribution properties [12].


In estimating the true signal value, it is necessaryto reduce the effect of nonspecific fluorescence, suchas the auto-fluorescence of the glass slides. For mostanalysis calculations, the background intensity shouldbe subtracted from the signal intensity before any ratiocalculations are performed. The method for determin-ing the background intensity can vary depending on thequality of the arrays and the spacing between individualspots.

The same measurements discussed above, such asmean, median, mode, and total, can be used to computethe background. Further, one can also compute the stan-dard deviation of the background intensities that can beused to determine the reliability of the measurement.

Result of a comparative study of these various quan-tification approaches is presented in Section 7 of thispaper.

5. Quality Control

In a fully automated image processing system, the ac-curacy of the signal pixel classification becomes a cen-tral concern. Not only does the correct segmentation ofsignal pixels offer accurate measurement of the signalintensity, but it also permits multiple quality measure-ments based on the morphological properties of thespots. These quality measures can be used to draw theattention of a human inspector to spots having ques-tionable quality values after the completion of an auto-mated analysis. Source of measurement abnormalitiesmay lay in both image imperfections and spot imageextraction procedure itself.

There are currently several general approaches to ex-pression quality measurement [4]. Two different groupsof methods can be noted in the literature.

• Image based quality assessment• Replicate based quality assessment

Our current discussion will be mainly devoted to thefirst category, but it is important to note here that spotreplicates can be successfully utilized for flagging ofsuspicious spots as well as being a valuable source ofinformation for significance and confidence analysis ofdifferentially expressed genes [13]. The most commonapproach to quality control in this area is based on thereplicate outlier removal [8, 14].

Different quality measures can be used for imagebased quality assessment. The choice of those mainlydepends on a particular microarray design, equipment

sophistication and measurement extraction procedures.The most widely used are the ratio of signal standarddeviation within the spot and its mean expression, off-set of the spot from its expected position in the gridand spot circularity measures (for example the ratioof a squared perimeter and a spot area). These mea-sures used separately, or combined into some kind ofa decision-making technique can be used to flag a spotas of low quality.

Background Contamination

Background defects may appear in arbitrary part of animage due to various reasons. Such an effect can influ-ence expression level of a large number of spots locatedin the contaminated area. As we mentioned earlier, weassume that the output of segmentation procedure foreach spot region delivers the signal (spot) pixels, back-ground pixels around the spot and ignored pixels. Thelast ones are usually isolated from the rest of the imageto avoid local contaminations (like dust particles) influ-encing the expression measurement. Our current taskis to identify possible abnormality in the background.Let us take the mean of background intensity around thespot as the local background estimate. In an ideal situ-ation, when no contamination occurs across the image,background means will be approximately distributedwith normal distribution (application of Central LimitTheorem).

A number of simple statistical tests can be appliedto identify spots with significantly high or low back-ground means. To illustrate our speculations we useda standard t-test technique. Quantitative comparisonof background mean for a current spot to the normaldistribution with two moments estimated from overalldistribution of image background means will producea p-value. This p-value multiplied by 2 will form ourconfidence in this spot being “clean” from any back-ground anomaly. Low (close to 0) confidence valuewill alert a researcher. Setting threshold for such aconfidence value at some pre-selected level will pro-vide automated flagging of suspicious spots. Of course,such an approach will produce certain false alarm ratecontrolled by selection of the threshold.

Signal Contamination

Another possible source of signal disturbance can bea non-homogeneous distribution of material within the


spot. To assess this quality we suggest analysis of asignal volatility within the spot. High signal variationwill lower our confidence in the expression value. Wesuggest taking spot intensity variance as an estimateof signal volatility. Spot variance distribution can alsobe approximated by normal distribution, however ex-periments show that the parameters of such a probabil-ity will strongly depend on the expression level of thespot. Thus, to retrieve more accurate information aboutsignal variance distribution we collect all the spots onthe image into the groups by their expression levels.Sort spots by the mean expression X j split them intothe bins with equal number of spots. And finally weperform significance analysis similar to that describedin the previous paragraph, but for each bin separately.Also a flagging procedure, similar to the one describedin previous paragraph can be applied.

Position Offset

Combination of spot finding and image segmentationprocedures give us estimates for each spot’s center lo-cation (for example, the center of mass of a spot region).To be able to test results produced by such scheme wealso should obtain a co-called “expected position” ofevery spot in the grid. Obviously for that purpose wecould use locations of the nodes in a pre-defined rigidgrid corresponding to idealistic microarray structure.However, in real life many different factors can intro-duce systematic irregularities into the grid structure.Such irregularities as varying inter-row or inter-columndistance or slight curvature in rows or columns shouldnot influence our decision on spot quality. In such sit-uation the best solution would be a least square fit ofstraight lines to the spot centers row- and column-wise.As a result, we can utilize intersection points of theselines as our expected spot locations. Once we obtainthese coordinates, the testing procedure becomes rela-tively simple. We can compare the offset from expectedposition of every particular spot to an average expectedinter-spot distance. Spots with offset higher than somefraction of average expected inter-spot distance wouldbe flagged.

Percentage of Ignored Pixels

Signal contamination assessment we described previ-ously does not take into the account any informationabout how many pixels were ignored during the seg-

mentation procedure. Such information may give usadditional understanding of a spot quality and may beextremely important if ignored contamination region isattached to the signal area.

Let us compute the total number of pixels in theignored regions directly neighboring the signal regionfor every spot. For each spot compute the ratio

R j = No. of ignored pixels neighboring the signal

No. of signal pixels + No. of ignored pixels neighboring the signal× 100%.

For different microarray types different values of sucha ratio might be acceptable. Thus set the threshold forflagging at some level R0. Flag all the spots with theratio higher than this threshold. Usually R j below 10–15% is acceptable.

Percentage of Open Perimeter. Sometimes signalarea produced by segmentation appears to be close tothe boundary of the patch dedicated to current spot.This may happen because of spot’s dramatic shiftfrom its expected location or because of its unusualshape.

In this case we can compute the perimeter of spot sig-nal Ptotal and the length of that part of signal boundarythat coincides with the spot’s region bounding box (thebox around the spot, in which the segmentation proce-dure was performed) Popen. The ratio R = Popen

Ptotal×100%

will be a measure of the percentage of “open” perimeterof the spot signal. Set the threshold for flagging of thespot at some level R0. Flag all the spots with the ratiohigher than this threshold. Usually R j below 10% isacceptable, because it may be due to high spot densityon a slide. But again, this threshold should be a sub-ject to adjustment according to current experimentalconditions.

Shape Regularity. As we mentioned before, severalmeasures can be used for estimating the goodness ofspot’s shape. One of them will be the scaled ratio of aspot’s area and its squared perimeter. Such a measureseems natural since it can be scaled to 1 for an idealcircle and any non-circular shape will have lowervalue. Different measures can be used for similarpurpose as the “shape regularity”.

Experiments with ImaGene 5.0 (BioDiscovery Inc.)proved that the outlined criteria arranged into an ap-propriate logic provide a reliable detection of differ-ent types of defects, see Fig. 6. Also our experimentsshowed a performance most of the time superior to the


Figure 6. Various defects flagged by ImaGene 5.0.

manual flagging made by human operator. This effectcomes from ability of aforementioned statistics to de-tect slight abnormalities in spot measurements. Takinginto the account further sophisticated statistical analy-sis that is usually done on the obtained data, eliminationof such abnormalities may be of a significant value.

6. Batch Automation

As we mentioned in introduction, microarray experi-ment becomes a classic example of a high-throughputdiscovery process. Full automation of at least imageprocessing stage would be of a great value in this in-dustry. Multiple attempts have been made in this di-rection in both freeware and commercial software sec-tors. However, most of them have obvious drawbacksand restrictions making their full-scale utilization pro-hibitive.

There are several obstacles on the way of fully auto-mated microarray image operation.

1. Grid finding and spot adjustment procedures shouldbe robust and flexible enough to accommodate pos-sible variations in spot locations and shapes.

2. Image segmentation procedure should take into theaccount both spatial and distributional informationwithin each spot’s neighborhood to be able to ex-tract signal, background and contamination areas asaccurately as possible.

3. A reliable automated quality control procedureshould be performed to issue an alarm wheneverproblem is encountered on either spot, subgrid orimage level. Statistical errors of both types (Types Iand II) should be minimized to insure optimal uti-lization of microarray data.

Most of the existing software tools lack at least oneof aforementioned characteristics. Especially difficult


to implement appears to be the last requirement. Withrich variety of microarray types present on the marketit has become inreasingly difficult to build a unifiedsystem of criteria that would give acceptable resultsfor at least a significant majority of arrays. Addition-ally, many research labs have their own concepts of asatisfactory quality for a spot. Thus the only way toconstruct an acceptable QC algorithm would be to doso with an involvement of widely accepted statisticaltests capable of picking up data abnormalities invisiblefor human eye.

7. Experimental Results

We are going to use results of several microarray ex-periments to illustrate concepts and questions outlinedthroughout the paper. The biggest difficulty in deter-mining the optimum segmentation and quantificationmethod is due to the lack of a ground truth in theseexperiments. Since it is almost impossible to know ex-actly the expression value of a particular gene as well asthe difficulty in removing non-image processing noisedue to fabrication, hybridization and scanning condi-tions, we cannot easily compare the results of vari-ous approaches. In our study here, we utilized a set ofreplicated experiments. In this case we made measure-ments from a series of five microarrays produced byAmersham’s CodeLink system. Each image was cre-ated using the same scanning channel (florescent dyeand wavelength) and contained 10752 spots arrangedinto two subgrids, 112 × 48 spots each. Pixel intensi-ties ranged from 0 to 65535. Average background levelwas measured at 85. Several spots were saturated at65535. About 20% of the spots were blank controlsand were not taken into account in our analysis. Theresults described below were obtained using BioDis-covery’s ImaGene 5.0 software.

Segmentation Methods

First set of experiments was designed to analyze thedifferences in performance of various spot segmenta-tion algorithms. For this purpose we compared mea-surements from the replicated arrays described earlier.Since each image is measuring essentially the samecondition, we are looking for a method that producesthe most consistent values across each of the replicatedarrays. Therefore, we measured the Standard Devia-tion of the measurements across replicates and looked

for a method which minimizes the overall standard de-viations. For this experiment, all the non-blank spotswere grouped into eight bins according to their averageexpression throughout the five replicate slides. Back-ground mean was subtracted from signal mean for ev-ery physical spot on every slide. Therefore five suchvalues were obtained for every gene; replicate Stan-dard Deviation of such values for every gene was com-puted. Resulting Standard Deviations were averagedthroughout all the mentioned eight bins. Average repli-cate Standard Deviations were plotted against the binnumber. This binning was done to account for well-known relation between replicate signal variance andaverage expression level for every particular gene [15].

Figure 7 shows replicate Standard Deviation (SD)plots for two methods: purely spatial (circular) segmen-tation with background buffer width equal to 3 pixelsand hybrid intensity/space-based segmentation.

As we can see from the graph, the hybrid segmenta-tion produced uniformly lower variation of replicatemeasurements. This indicates higher reproducibilitypotential of the hybrid method. To further investigatea source of unreliability of the circular segmentationlet us take a closer look at the genes with the mostdifference in SDs for the two methods.

Figure 8 represents one of the causes for the gapbetween the two curves on Fig. 7. In presence of acontamination inside the signal area, hybrid segmenta-tion is usually capable of isolating the contaminationfrom both signal and background. Pure circular seg-mentation cannot extract the contamination unless itfalls into the buffer zone. As the result, correspondingCV (SD over average) for the circular segmentation ap-pears to be 0.9, while the hybrid segmentation brings it

Figure 7. Replicate Standard Deviation plot for 8 intensity binsusing (a) circular segmentation (dotted line), (b) ImaGene 5.0 hybridsegmentation (solid line).


Figure 8. Hybrid segmentation (upper), circular segmentation (lower).

down to 0.1. Of course, one could use trimmed circularsegmentation; however, since nobody knows how largethe contamination is going to be, the operation becomesbasically a guess. If the user wishes to trim significantpercentage of every spot’s intensity, the measurementsthemselves stop making sense. Another reason for thereplicate SDs being so different for two methods is thehybrid method’s ability to correct for imperfection ofspot finding results. Spot location and size serves onlyas a starting point for this algorithm, while circular seg-mentation is not able to deviate from neither initial loca-tion nor circular shape. Thus we would recommend us-ing hybrid approach to signal segmentation over purelyspatial whenever computational limits allow that.

Quantification Method

As we discussed it earlier, the choice of expressionmeasurement method is also an important detail ofany reliable microarray technique. We used the fivereplicate slides from the previous analysis to showpossible difference in microarray results when usingdifferent measurement approaches. We incorporate thebinning approach from previous analysis, but instead ofplotting replicate variances for different segmentationmethods we plotted replicate variance for the hybridsegmentation only, but using three different measures:mean, median, mode. Figure 9 shows the correspond-

Figure 9. Replicate Standard Deviation plot for 8 intensity binsusing (a) mean (solid line), (b) median (dotted line), (c) mode (dashedline).

ing plots. As we can see, mean and median producevery similar reproducibility rate, at the same time modeis much less stable. Circular segmentation will showsimilar relation between the measures. Using differenttypes of arrays for our experiments did not change therelation.

Evidently using mode as a measurement for geneexpression can introduce more noise into the system,therefore we recommend using mean or median for thehybrid segmentation and median for the circular one (asit is more tolerant to a moderate level of contamination).Mode can also be very sensitive to partial saturation ofspot image, making such measurement less reliable.


Figure 10. Channel-to-channel scatter plot of signal means.

One can argue that lower variance of resulting mea-surement between the replicates is not necessary anindicator of superiority of a method used. Keeping thisin mind we put together another set of experimentsbased on images from two channels of the same ar-ray. Both channels contain reference experiments, i.e.the same RNA material was labeled with two differentdyes and hybridized to the same array. However, theresulting two images, one for each channel, were pro-cessed completely separately through the analysis soft-ware (independent grid and spot finding, segmentation,and quantification). Ideally, after appropriate normal-ization is applied, measurements of spot signals shouldbe identical for each channel and thus lie along the di-agonal of a channel-to-channel scatter plot. Figure 10shows the scatter plot for signal means.

One of possible characteristics of a quality of afore-mentioned scatter plot would be average square resid-ual of a linear fit. Table 1 displays such characteristicsfor two segmentation methods and three quantificationapproaches. We can see that average square spread fromthe fitted line is consistently lower for measurementsextracted based on hybrid segmentation. We can also

Table 1. Average square residuals for inter-channellinear fit.

Mean Median Mode

Spatial (circular) 58,899 134,237 1,784,585

Hybrid 49,379 85,734 846,003

see that the mean gives the best linear fit and the modestands out for its high residuals.

Another challenging question we have to answerrefers to possible advantage in using volume for mea-suring signal. Since volume is supposed to measurecollective intensity of a spot, it is not easy to com-pare its performance against mean or median. To over-come this difficulty we used normalization technique.Figure 11 shows plot for replicate Standard Deviationsfor signal means versus normalized signal volumes.Normalized signal volumes were obtained dividing thesignal volumes by normalizing constant C describedbelow.

C =∑5

k=1

∑10752i=1 vki

∑5k=1

∑10752i=1 mki

= 226.0,

Figure 11. Replicate Standard Deviation plot for 8 intensity binsusing (a) mean (solid line), (b) normalized volume (dotted line).


where mki and vki are signal mean and signal volumerespectively for spot i on slide k. Such normalizationensures that variations of the two measurements arecompared in an appropriate scale.

We can notice that signal volume measurements haveuniformly higher replicate Standard Deviation. Thispicture can be observed for different methods of choos-ing the normalizing constant C . For this purpose wetried ratio of distribution medians, ratio of average sig-nal areas etc. In all tests the plot looked like the one onFigure 11. This can be explained by the fact that whenmeasuring volume, one always encounters additionalrandom factor-varying spot size for every replicate.This factor does not play such important role in sig-nal mean, median or mode. Thus, our conclusion aboutsuperior reproducibility of mean and median measure-ment still stands.

In order to analyze and compare intensity ratio andcorrelation ratio, we require a different set of images.These images must be two-channel. We used a set ofimages containing two replicates of two-channel exper-iments (a total of four images). For intensity ratio wechose the ratio of signal means (with local backgroundmeans subtracted) based on hybrid image segmenta-tion results. For correlation ratio we selected the meanof pixel-wise intensity ratios for the corresponding sig-nal areas. Please note that we had to take into accountonly those pixels that where classified as signal in bothchannels, as the hybrid segmentation is performed ineach channel separately and may produce two signalareas slightly differing from each other. Local back-ground mean was subtracted from every intensity valuebefore taking the ratio. The results of our analysis in-dicated very similar average reproducibility (measuredby replicate variance) between the two measures.

However, the main question brought up in the lit-erature [12] is comparative distributional propertiesof the two measures. It is widely accepted that log-transformed ratios of the expressions between twochannels are approximately distributed according toGaussian law in absence of up- or down-regulation.Figures 7 and 9 actually support this assumption. Plotsshown on those figures demonstrate an exponent-likeshape. Keeping this in mind we applied log transforma-tion to both ratios an analyzed distribution of replicateresiduals for four different array types.

First we conducted eight Kolmogorov-Smirnov testsfor goodness of fit at p-value level of 0.05, comparingresidual distributions to a Gaussian PDF. Both typesof log-ratios failed the tests for all four array types.

Figure 12. Tail probabilities plot for 4 cutoff points of (a) Gaussiandistribution (solid line), (b) residual distribution of log intensity ratio(dotted line), (c) residual distribution of log correlation ratio (dashedline).

This proves that strictly speaking we can never applyGaussian properties to log-ratio microarray data in anystatistical framework. However, we can still analyze,which kind of log-ratio measure can be approximatedbetter by Gaussian distribution. In order to do so, weplotted two-sided tail probabilities for 4 cutoff points(0.5 × SD, 1 × SD, 1.5 × SD and 2 × SD). Typical plotfor intensity ratio distribution, correlation ratio distri-bution and Gaussian distribution is shown on Figure 12.As you can see, two types of measurements demon-strate close distributional behavior. However, both ofthem appear to be quite far from ideal Gaussian distri-bution. Again, even though a graph for distribution oflog-ratio data may roughly resemble Gaussian curve,statistically speaking we should use non-parametricmethods when making any inference about or basedon such a distribution. Similar picture can be observedfor intensity ratio versus correlation ratio when usingmedian signal instead of mean signal constructing theratios.

The moral of this small research is that evidently wecould select neither intensity ratio nor correlation ratioto be preferred in its distributional properties. The onlyobvious advantage of using correlation ratio is its abil-ity to clean up the output when fairly unsophisticatedsegmentation method is used (circular etc.).

Quality Control. This is the last, and the most chal-lenging stage of automated microarray image process-ing. To evaluate the performance of an automatedquality flagging system, we performed a careful com-parison of our automated system output to a flag-ging made by human operator. This experiment wasperformed using Amersham CodeLink system images


where 250 images were analyzed using the qualityflagging system implemented in ImaGene in its fullyautomated mode. Quality control criteria outlined inSection 5 were set-up based on several training im-ages prior to analysis. Then same images were viewedby human operators in order do flag the spots dis-playing visible defects. Results of that comparison ap-peared to be very encouraging. 90% of flags issued byImaGene coincided with manual flags. Also human op-erators considered 99% of the spots not flagged by Ima-Gene “healthy”. Moreover after the “conflicting” spotswere reviewed more carefully, in 75% of the cases itwas decided that ImaGene made a correct decision. Im-aGene sensitivity to deviations in the background leveland spot abnormalities allowed it to discover problemssometimes invisible for a human eye.

Of course the Quality Control system described inthis paper needs fine tuning for every new type of ar-ray, making a set-up of automated image processingprocedure somewhat involved. However, taking intothe account numbers of single-type microarray imagesprocessed per day by some of the high-throughput labs,tremendous saving of time and resources may be worththe hassle.

References

1. D.E. Bassett, M.B. Eisen, and M.S. Boguski, “Gene ExpressionInformatics—It’s all in Your Mine,” Nature Genetics Supple-ment, 1999, p. 21, 48.

2. Y-X. Zhou, P. Kalocsai, J.-Y. Chen, and S. Shams, “Informa-tion Processing Issues and Solutions Associated with MicroarrayTechnology,” in Microarray Biochip Technology, Schena (Ed.),Eaton Publishing, Massachusetts, 2000, pp. 167–200.

3. L.J. Heyer, S. Kruglyak, and S. Yooseph, “Exploring Expres-sion Data: Identification and Analysis of Coexpressed Genes,”Genome Research, vol. 9, 1999, p. 1106.

4. Y.H. Yang, M.J. Buckley, S. Dudoit, and T.P. Speed, “Compari-son of Methods for Image Analysis on cDNA Microarray Data,”Technical report, no. 584, Department of Statistics, Universityof California, Berkeley, 2000.

5. D. Bozinov and J. Rahnenfuhrer, “Unsupervised Technique forRobust Target Separation and Analysis of Microarray Spotsthrough Adaptive Pixel Clustering,” Bioinformatics, vol. 18,2002, pp. 747–756.

6. R.A. Johnson and D.A. Wichern, Applied Multivariate Statisti-cal Analysis. N.J.: Prentice Hall, 1998.

7. S.G. Hilsenbeck, W.E. Friedrichs, R.Schiff, P. O’Connell, R.K.Hansen, C.K. Osborne, and S.A.W. Fuqua, “Statistical Analysisof Array Expression Data as Applied to the Problem of Tamox-ifen resistance,” Journal of the National Cancer Institute, vol.91, 1999, p. 453.

8. Y. Chen, E.R. Dougherty, and M.L. Bittner, “Ratio-Based De-cisions and the Quantitative Analysis of cDNA Microarray Im-ages,” Journal of Biomedical Optics, vol. 2, no. 4, 1997.

9. R.O. Duda and P.E. Hart, Pattern Classification and Scene Anal-ysis. J. Wiley, 1973.

10. R. Adams and L. Bischof, “Seeded Region Growing,” IEEETransactions on Pattern Analysis and Machine Intelligence,no. 16, 1994.

11. K.R. Castleman, Digital Image Processing. Prentice Hall, 1996.12. J. Brody, B. Williams, B. Wol, and S. Quake, “Significance and

Statistical Errors in the Analysis of DNA Microarray Data,”PNAS, vol. 99, no. 20, 2002, pp. 12975–12978.

13. M.-L.T. Lee, F.C. Kuo, G.A. Whitmore, and J. Sklar, “Impor-tance of Replication in Microarray Gene Expression Studies:Statistical Methods and Evidence from Repetitive cDNA Hy-bridizations,” PNAS, vol. 97, no. 18, 2000.

14. G.C. Tseng, M.K. Oh, L. Rohlin, J.C. Liao, and W.H. Wong,Issues in cDNA Microarray Analysis: Quality Filtering, ChannelNormalization, Models of Variations and Assessment of GeneEffects,” Nucleic Acids Res, vol. 29, 2001, pp. 2549–2557.

15. D.M. Mutch, A. Berger, R. Mansourian, A. Rytz, and M.A.Roberts, “The Limit Fold Change Model: A Practical Approachfor Selecting Differentially Expressed Genes from MicroarrayData,” BMC Bioinformatics, vol. 3, no. 17, 2002.

Anton Petrov is employed as a statistician with BioDiscovery Inc. aprivately held bioinformatics company based in Marina del Rey, Cal-ifornia. Dr. Petrov received his B.S. in Applied Mathematics degreefrom St. Petersburg State Technical University (Russia) in 1997. Hegraduated with Ph.D. degree in Applied Mathematics from the Uni-versity of Southern California in 2001. His research interests includeapplied statistics, stochastic theory, image analysis and signal pro-cessing. Dr. Petrov worked on multiple projects in advanced targettracking for IR sensors in collaboration with U.S. Navy, Raytheon etc.Currently his research is concentrated on image processing, qualitycontrol and statistical data analysis for microarrays. He has 7 techni-cal publications and book chapters, 4 of them are microarray [email protected]

Soheil Shams is the founder and CEO of BioDiscovery, Inc.a privately held bioinformatics company based in Marina del


Rey, California. Dr. Shams received his Masters and Ph.D. de-grees from University of Southern California in 1986 and 1992respectively in the field of Computer Engineering. From 1984to 1997, Dr. Shams was employed at Hughes Aircraft Com-pany and the nine of those years working at HRL laboratoriesin Malibu California. His research interests span a wide rangewith concentration on pattern recognition technologies and paral-lel processing architectures. Dr. Shams is the author of 7 issuedpatents and 6 pending applications in addition to over 45 technical

publications and book chapters. During his tenure at HRL, heworked on multiple defense related projects including AutomaticTarget Recognition, Passive Sonar Target Classification, PassiveRadar Target Tracking, and Optimized Mapping of Neural Net-works on Parallel Processing Architectures. Since founding BioDis-covery, he has managed the growth of this company and has beeninvolved with all aspects of operations, sales, research, and [email protected]

microarray image processing and quality control

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