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Figure 2.

False color image of raw intensities in the reference channel. Columns represent arrays and rows represent spots. Only human microRNA spots are shown. The intensities have been location- and scale-transformed within each row (spot) to lie between 0 and 1; this enables comparison across arrays. Other transformations are possible, and give similar results. There are two interesting features in this plot. First, there is a banding pattern of high and low intensity on alternate arrays. This is presumably due to the fact that two arrays are hybridized on each slide. Second, the last six arrays, run on a different day, have consistently higher intensity, suggesting a batch effect. This effect is also seen in Pantanetti casual derby shoes Brown Pay With Paypal For Sale EzpCh
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We use this graphic to demonstrate the presence of a batch effect in our experiment. There are two well-defined batches: Arrays 159–184 were labeled on 4/13/07 and hybridized on 4/16/07 (159–182) and 4/17/07 (184). Arrays 207–213 were labeled on 5/5/07 and hybridized on 5/14/07. The arrays from the second batch (207–213) have larger intensity in the green channel. The banding pattern visible in this figure is presumably due to the fact that two arrays were printed on each slide.

Other plots designed to highlight this batch effect can also serve as diagnostics for assessing different normalization methods. Supplementary Figure S2 , which encodes a pairwise distance between all arrays, is an example of such a plot. Another example is Figure 3 , which plots the estimated densities for the green channel log 2 intensities. The two batches are indicated by different colors in Figure 3 , and a visual separation of the two batches is readily apparent in the densities for the unnormalized intensities. The separation is removed by all three normalization methods considered in this article (see the next section), but with some differences in behavior for low intensities. Figure 3 also serves as a diagnostic of array quality; had there been any bad arrays, one or more of the corresponding density estimates are likely to have been aberrant.

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Histograms (kernel density estimates) of log intensity in the reference channel. The rows correspond to different types of probes, and the columns represent the different normalization methods used. Colors distinguish the two batches, and show a systematic upward shift in the second batch. All normalization methods appear to rectify the batch effect for high intensity spots, but have different effects on the low intensity spots.

Normalization is an essential preprocessing step in the analysis of any microarray experiment. Its primary purpose is to try to ensure that the observed between-array differences are due to biological phenomena, and not due to artifacts that arise due to differences in handling or processing of the samples. Normalization of microRNA microarrays is problematic primarily for two reasons. First, the number of microRNAs measured by the arrays is fairly small, numbering only in the hundreds ( Figure 1 ). Second, even among the measured microRNAs, only a small number are expressed at all in a given tissue, and consequently, most of the spots on the arrays cannot be used for normalization. In other words, the usual normalization assumptions do not apply. A careful analysis requires the development of a reasonable normalization strategy, as well as diagnostics that can be used to assess the performance of potential normalization strategies.

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