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Vol. 12, Issue 2, 325-332, February 2002
METHODS
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
METHODS
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
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DNA microarrays are now widely used to measure expression levels and DNA copy number in biological samples. Ratios of relative abundance of nucleic acids are derived from images of regular arrays of spots containing target genetic material to which fluorescently labeled samples are hybridized. Whereas there are a number of methods in use for the quantification of images, many of the software systems in wide use either encourage or require extensive human interaction at the level of individual spots on arrays. We present a fully automatic system for microarray image quantification. The system automatically locates both subarray grids and individual spots, requiring no user identification of any image coordinates. Ratios are computed based on explicit segmentation of each spot. On a typical image of 6000 spots, the entire process takes less than 20 sec. We present a quantitative assessment of performance on multiple replicates of genome-wide array-based comparative genomic hybridization experiments. By explicitly identifying the pixels in each spot, the system yields more accurate estimates of ratios than systems assuming spot circularity. The software, called UCSF Spot, runs on Windows platforms and is available free of charge for academic use.
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
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DNA microarrays have come into widespread use to compare expression
levels and DNA copy number in biological samples (Alizadeh et al. 2000
;
Golub et al. 1999; Perou et al. 2000; Pinkel et al. 1998). Ratios of relative abundance in "test" and "reference" nucleic acid samples are derived from fluorescence images of regular arrays of spots containing target genetic material to which the differentially labeled samples are hybridized. Figure
1 shows an example of a typical image
containing ~6700 array spots. There are deviations from perfect
regularity in the positions of the subarray grids and in the positions
of individual spots. Also, in the detailed shapes of individual spots,
there are deviations from the ideal of a uniform circle, and some spots
are missing. In addition, there may be background signals due to
nonspecific binding of the labeled nucleic acids to the array substrate
and substrate fluorescence, and the background may vary with location.
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Determination of a fluorescence ratio requires finding the location and
extent of a printed array spot and a method to estimate the
contribution of background signal to the area of each spot. Although
there are a number of methods for the quantification of images, many of
the software systems in wide use either encourage or require extensive
human interaction at the level of individual spots on arrays (Eisen
1999; Axon Inc. 2001). This can lead to unnecessary
variation in the derived parameters from an experiment, depending on
the bias or fatigue of a human operator, and the process can be
very time-consuming. Further, many systems rely on an assumption
of spot shape that can further degrade accuracy. Figure
2 (top) illustrates this with an example
that assumes spots are circular. The area inside the circle is
identified as containing the spot, but it contains both the actual spot
and a proportion of background. The local background area (outside the
circle) is correctly identified. As the proportion of background
misidentified as foreground increases, there is a linear "dilution"
effect of the specific signal. The estimate of the absolute signal due
to specific hybridization, computed as the mean foreground pixel intensity minus the mean background pixel intensity, is lower than it
should be. If there is no noise in the images, and background is
properly subtracted, the dilution effect has no impact on the computed ratio. However, real images have noise, and there may be
errors in background estimates; consequently, the effect is to increase
the variance of the estimated ratio as the proportion of misidentified
foreground pixels increases. Figure 2 (bottom) shows a plot of the
excess variance due to varying levels of noise in conjunction with
pixel misidentification (simulated data). The effect is particularly
strong in cases wherein the specific signal in either the test or
reference channel is low relative to the noise level.
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We present a highly automated system for microarray image quantification. The system automatically locates and segments each spot and estimates ratios, requiring no user identification of any image coordinates. The software is designed to work with two or three fluorescence images. In the two-color mode, spots are segmented based on the composite test and reference images. In the three-color mode, an image of a DNA counterstain, typically DAPI, is also obtained to allow accurate segmentation of the spots based on their DNA content. By explicitly identifying the precise pixels in the printed spot, the system yields more accurate estimates of ratios than systems assuming spot circularity. On a typical image of 6000 spots, the entire process takes less than 20 sec. The software, called Spot, runs on Windows platforms and is available free of charge for academic use. We present the basic algorithms, give a brief description of the software's functionality, and show the utility of the method on microarray-based comparative genomic hybridization data.
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METHODS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
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Algorithms and Software
The algorithm requires input of only the geometry of subarray grids (e.g., 4 × 4) and the geometry of spots in each subarray (e.g., 21 × 18), along with input images. The algorithm will be presented as a brief summary followed by a more detailed explanation. The description of the software and user interface will be brief because the full manual is available in the software distribution.
Algorithm
There are five steps in the process of deriving ratios from the input images: (1) Estimating the spot spacing and the subarray spacing, (2) locating and optimizing the position of the subarray grids, (3) locating and optimizing the position of individual spots, (4) identifying foreground and local background pixels for each spot, and (5) computing ratios and statistical quality measures. Estimation of spot spacing and subarray spacing is accomplished by summing the signal intensities in the X and Y directions of the image. Figure 3 shows a plot of the results for the image in Figure 1. The pattern of peaks is used to determine both the interspot spacing and the intersubarray spacing. Initial location of the subarray grids is performed first on the integrated images. The algorithm seeks to find a combination of subarray offsets and spacings in both the X and Y directions such that the nominal spot locations correspond to the peaks. A simple scoring function is used that computes the difference between the values of the integrated image at spot centers and midway between spot centers. Further refinement takes place by recomputing local integrations using only the rectangle enclosing the estimated location of each grid. Final refinement takes place in the raw image domain, shifting grids in multiples of the refined X and Y spot spacings to maximize the score of a function of the difference between the spot centers' brightness minus the interspot areas' brightness. The positions of individual spots are then optimized using the same scoring function to allow for a degree of noncolinearity in the final optimized centers (spots are allowed to move up to 15% of the interspot spacing by default).
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Software
UCSF Spot runs on Windows platforms with Intel-based architecture, having a minimum recommended RAM size of 128Mb (256Mb is preferable). It is written entirely in C. A typical hybridization, consisting of three 1024 × 1024 16-bit images, containing 6000 spots, takes less than 20 sec to process, including all I/O, subarray grid identification, spot segmentation, and ratio quantification (933 MHz Pentium III, 512Mb RAM, running Windows 2000 Professional). Spot produces a summary image for review of the segmentation (Fig. 3), a tab-delimited text file containing derived parameters, and a file (with an "SPT" suffix) that describes the geometry of the array and the path names of the images in the hybridization. UCSF Spot is available at http://cc.ucsf.edu/jain/public. Figure 5 shows the graphical user interface; the program can also be run from the command-line for processing large sets of hybridizations. There are three modes of operation, which select the method of grid placement (selectable in box 1 of Fig. 5). The following first discusses each mode along with its primary user-adjustable parameters; the primary user-adjustable parameters that affect ratio quantification are then described.
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De Novo Grid Placement
The user must specify the following: (1) Hybridization images: An image to be used for grid identification and spot segmentation (e.g., a DNA counterstain image of the array) and test and reference images. The segmentation image may be specified as "blank," in which case a composite test/reference image is used for segmentation. These are entered in boxes 2 to 4 in Figure 5. (2) Number of subarray columns and rows (boxes 5-6). (3) Number of spot columns and rows in each subarray (boxes 7-8). Optionally, the user may specify hints for spot spacing and subarray spacing (box 9). This can be helpful if Spot incorrectly estimates the spacing. Generally, the two modes described below are more useful when there is some difficulty in the fully automated grid placement mode. In practice, ~80%-90% of hybridizations captured using our custom CCD imaging system yield error-free grid placement and spot identification in the fully automatic mode with default parameters.Grid Placement Using a Hint
Within a single batch of slides, there may be some proportion whose hybridization signals yield images that are difficult to process automatically. In such cases, Spot can accept a "hint" from the successful geometry derived from another hybridization's images (typically used is an array from the same print run). In this case, the user selects "Place grids from hint" (box 1) and must specify a Spot SPT file from which to read the geometry. The array layout parameters are automatically read from the file.Grid Adjustment
The positions of grids can be manually adjusted by selecting "Adjust grids from layout" if errors are evident. This mode is automatically selected if Spot is launched by double-clicking on an SPT file. This mode is also used to requantify ratios if the user decides to change parameters affecting spot segmentation or size (described below). When Spot fails to properly place a grid, it is generally off by an integral multiple of spot spacings. To adjust the grid in column 1 row 2 by 1 spacing to the right, the user specifies 1, 2, 1, 0 in boxes 10 to 13, respectively.Parameters Affecting Quantification
The user has control of several parameters that control the segmentation thresholds for foreground and background identification, as well as spot size: (1) Foreground threshold (box 14): The default is to find the threshold that includes the histogram area's brightest 30% (specified as 0.3). A setting of 0.2 sets the threshold such that the brightest 20% of pixels are included. (2) Background threshold (box 15): The default is to find the threshold that includes the histogram area's dimmest 10% (specified as 0.1). A setting of 0.2 sets the threshold such that the dimmest 20% of pixels are included. (3) Spot size (box 16): Default spot size is computed relative to the spot spacing. This parameter scales the default size. A setting of 0.8 reduces the spot size by 20%. Spot computes a large number of features for each spot, including information about spot placement, size, multiple ratio estimates, and quality parameters. An extensive description of the detailed operation of the software and its input/output is provided with the software distribution.Experimental Data
Genomic DNA from breast cancer cell line BT474 was labeled with Cy3
and hybridized with Cy5 labeled normal male genomic DNA to an array
consisting of triplicate spots of each of ~2000 BACs whose map
positions are distributed quasi-uniformly across the human genome
(Snijders et al. 2001). Cot1 DNA is included to suppress hybridization of the repetitive sequences. The arrays were printed with
a 16-pin print head from 864-well microtiter plates and are 12 mm
square. The spots are on ~130 µm centers. After hybridization, the
slides are mounted in 90% glycerol/10% PBS and 1 µg/mL of the DNA
stain DAPI and a coverslip applied. DAPI, Cy3, and Cy5 images are
obtained using a custom-built CCD system. The entire array is contained
in a single 1024 × 1024 pixel image.
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RESULTS AND DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
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There are two broad areas that are important for assessing performance: quantitative accuracy and ease of use. For the purposes of this paper, the quantitative accuracy issue is the most important. Spot has been used extensively for microarray image quantification in our institution, in both array-based genome copy number experiments and expression experiments. Its adoption has been driven primarily by its convenience and speed, and by demonstration that it provides accurate results. Investigators are encouraged to assess ease of use by requesting the software and testing it directly.
Ideally, quantitative accuracy could be assessed by direct comparison
with other methods. However, because the methods available to us
require significant user-specific interaction, it is difficult to make
formal comparisons meaningful. Our informal comparisons with a
commercial system (GenePix, Axon Instruments Inc. 2001) parallel the
formal results reported in what follows. Apart from the automated grid
placements, Spot's chief departures from widely-used
software are (1) explicit identification of local foreground and
background with no strong assumption about spot shape, and (2) the
ability to make use of a counterstained target image so that
segmentation is based on the DNA distribution in the spots, not on the
possibly weak hybridization signal. We tested performance of image
quantification by making use of replicate spots on the arrays
(generally triplicates) and computing the sample variance of the log
ratios. This approach controls for all experimental variables except
for those that can be directly ameliorated by image quantification
methodology. We tested three effects: (1) the effect of an assumption
of spot circularity, (2) use of a counterstained image for segmentation
versus using either the reference image or a composite of test and
reference, and (3) the effect of local background versus global
background correction. The experimental details of the hybridizations
and subsequent imaging can be found in Snijders et al. (2001).
Circular Spots versus Segmented Spots
We ran Spot on a set of hybridizations of BT474 measuring both genomic copy number and expression under two conditions: (1) explicit spot segmentation and (2) assumption of circular spots. In the first condition, Spot operated as described above. In the second condition, Spot was run as above but by using the geometric constraint only for inclusion of pixels (pixels within a fixed radius of the spot center were identified as foreground). Figure 6 shows the difference in pixels identified as foreground and background using the two methods of defining spots. Note that under the circularity assumption, reddish crescents indicate areas that are included as foreground pixels but are not part of the spot. Using Spot's segmentation capability, the foreground pixels are consistently on target. Some actual foreground pixels are missed by the segmentation, but a sufficient number are correctly identified to yield good ratio estimates. Figure 7 shows the cumulative distributions of sample standard deviation of the three replicate spots for each clone using spot segmentation and the assumption of spot circularity. Results for three separate hybridizations of differing average signal intensity are shown. The difference between the two analysis methods is statistically significant, although the magnitude of the differences (for copy number data of the quality in these measurements) is not substantial. However, more clones survive an aggressive quality threshold on standard deviation using explicit segmentation, which results in fewer missing data points in the final derived ratios. Figure 8 shows the mean log2 ratios computed for the replicate spots for each clones for each of the three replicate hybridizations using explicit spot segmentation. Note that the error bars, due to the variability among the replicate spots, are much smaller in general than the variability due to differing experimental conditions. The average standard deviation of mean log2 ratios for the three hybridizations over all the spots is 0.030.
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Counterstained Image versus None
If the spots contain sufficient DNA to produce a bright counterstained image, spot segmentation is very accurate (see Fig. 6). The test, reference, or a combination of these images may also be used for segmentation, but there is a potential difficulty if the hybridization signal intensities are too low. Figure 9 illustrates this problem for the analysis of a hybridization of BT474 cell line versus normal DNA. If the reference signal is used to guide segmentation, we see that in the plot of ratio versus reference intensity, there is substantial skew to low ratios as reference intensity gets very low. This is because noise in the image is affecting the definition of spot extent. The foreground pixels in the reference image are selected by the algorithm to be bright, with the background pixels dark, but this includes image noise. The test image pixels have no selection bias, and the computed specific test signal ends up anomalously low relative to the reference signal, producing a low ratio. Using a composite test and reference image for segmentation partially overcomes this problem, but construction of the composite by simple means (e.g., the sum of the two images), may generate skew as well, depending on the dynamic range of the test and reference images. In this example, we see less skew than before (and in the opposite direction) when using a composite test/reference image for segmentation. In the case in which we use the Dapi counterstained image, we see little, if any, skew. We observe the expected increase in variability as the reference signal decreases, but there is little apparent bias. Note that the spread in ratios in these plots largely reflects true ratio variation due to the copy number changes in this cell line (Fig. 8). The bottom plot of Figure 9 explicitly shows the relationship of mean signal intensity in the test and reference channels to the replicate standard deviation. At the lowest signal intensities, the magnitude of the replicate standard deviations is higher, but it is still small enough that single copy number changes can be reliably distinguished from noise.
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Local Background versus Global Background
There is a question as to the importance of background estimation and whether local estimates or global estimates are more appropriate. Figure 10 (left) shows the relationship between the x-coordinate image position and the estimated local background for the test and reference images for all spots from the best of the three BT474 hybridizations. Clearly, there is some spatially consistent pattern of background between the two channels. We believe that this variation is attributable to a combination of illumination nonuniformity and physical factors on the slides (e.g., more nonspecific hybridization in the middle of subarrays attributable to more "leakage" of target material during printing in those areas). However, this may be because our estimation method is incorrect. Recall from the previous discussion (Fig. 4) that it is possible to estimate ratios using the slope of a line fitted to the absolute test and reference pixel intensities. This does not require explicit computation of a background estimate. When the two estimates agree, we gain confidence in the background computation. Figure 10 (right) shows the estimated background for those spots in which the two methods of computing ratios agree to within 0.2 log2 units (over 3600 spots of ~6700 total). Note that the same spatial pattern appears in this restricted set, supporting the proposition that there is real variation in the background in the images and that our method for local estimation of background is reasonable.
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We further tested the background estimation method by comparing it with a global background estimate. We recomputed ratios for all three BT474 hybridizations using a global constant background estimate (the mean of the local estimates) for the two channels. We quantified the difference by considering experimental replicates. Inasmuch as our replicate spots are printed adjacent to one another, and because there is a spatial relationship between local background estimates, we cannot simply compare the replicate standard deviations between the local and global approaches. The background corrections are essentially constant within the replicates in both cases. Rather, we compared the standard deviations of the final computed ratios across all three BT474 hybridizations. With the local background estimate, we found 2.0% more clones with standard deviations less than 0.1, and 1.7% more clones overall that had valid ratio estimates in at least two hybridizations.
We have tested UCSF Spot on several two-color cDNA microarray image sets, ranging from 1000 to 40,000 printed targets. The results parallel those presented here, but processing times are longer for the larger image sizes acquired for the highest density arrays. Examples of cDNA array images and segmentations are available on the Web site (http://cc.ucsf.edu/jain/public).
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CONCLUSION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
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Fully automated quantification of microarray hybridization images is feasible and it yields highly reproducible results in genome-wide DNA copy number measurements. The variability in ratio estimation due to image quantification errors is substantially less than the variation due to biological and experimental noise. Use of a counterstained image facilitates segmentation approaches that avoid assumptions about the shape of array spots. Such approaches should be more robust to noise to the extent that they minimize the proportion of pixels incorrectly identified as being part of spots. This may be particularly important in expression measurements of low-abundance genes. Local background estimation is more appropriate than global estimation, but the quantitative differences between the two are relatively small in this analysis. The UCSF Spot program offers a step toward fast and accurate operator-independent results in the quantification of DNA microarray image data.
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ACKNOWLEDGMENTS |
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This work was supported in part by grants from the National Cancer Institute (CA83040, CA89520, and CA64602).
The publication costs of this article were defrayed in part by payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 USC section 1734 solely to indicate this fact.
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FOOTNOTES |
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Present address: 4University of California, San Francisco, Cancer Center, Box 0128, San Francisco, CA 94143-0128, USA.
4Corresponding author.
E-MAIL ajain{at}cc.ucsf.edu; FAX (415) 502-3179.
Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.210902.
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REFERENCES |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
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Received August 17, 2001; accepted in revised form November 30, 2001.
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reference, composite, and
counterstained DAPI. Bottom: plot of replicate standard
deviation vs. mean signal (test and reference) intensity using the
counterstained DAPI image for segmentation.
