Fluorescent-tagging and digital imaging are widely used to determine the subcellular location of proteins. An extensive publicly available collection of images for most proteins expressed in the yeast S. cerevisae has provided both an important source of information on protein location but also a testbed for methods designed to automate the assignment of locations to unknown proteins. The first system for automated classification of subcellular patterns in these yeast images utilized a computationally expensive method for segmentation of images into individual cells and achieved an overall accuracy of 81%. The goal of the present study was to improve on both the computational efficiency and accuracy of this task. Numerical features derived from applying Gabor filters to small image patches were implemented so that patterns could be classified without segmentation into single cells. When tested on 20 classes of images visually classified as showing a single subcellular pattern, an overall accuracy of 87.8% was achieved, with 2330 images out of 2655 images in the UCSF dataset being correctly classified. On the 4 largest classes of these images, 95.3% accuracy was achieved. The improvement over the previous approach is not only in classification accuracy but also in computational efficiency, with the new approach taking about 1 h on a desktop computer to complete all steps required to perform a 6-fold cross validation on all images. ' 2009 International Society for Advancement of Cytometry Key terms location proteomics; pattern recognition; subcellular location; machine learning GREEN Fluorescent Protein (GFP) and its variants are widely used in biological imaging because they can be linked with virtually any protein to visualize location in vivo. GFP-tagging is used both to confirm conjectured localizations and to determine them for previously uncharacterized proteins (although the localization can potentially be altered by the tagging). Traditionally, the assignment of a location is done by visual inspection. However, it is often difficult to insulate against the influence of prior experience or hypotheses on those assignments to rely exclusively on the tagged protein images themselves. In addition, visual inspection is not well suited to efficiently handling proteome-scale tasks such as classifying thousands of different GFP-tagged protein images. Automated classification of subcellular patterns in such images is a viable alternative, and a number of systems for this task have been described (1-3). These typically start by calculating numerical features from the microscope image that are designed to capture essential characteristics of the pattern without being sensitive to the position, orientation and brightness of individual cells. Machine learning algorithms are then trained to predict subcellular location labels from the numerical features. The classification problem generally consists of four steps: 1) image preprocessing, 2) feature extraction, 3) feature selection, and 4) classifier training and evaluation. Among these steps, the first two steps are commonly most important because the steps decide essential qualities of features that influence the entire process. To provide information on subcellular location for the many proteins about which little is known, efforts to create proteome-scale image collections have been described (4-7). The most comprehensive coverage to date has been of the yeast proteome, for which Huh et al. (4) collected images of over 4,000 GFP-tagged proteins encoded by cDNAs (a more recent collection for over 1,000 proteins was created by chromosomal tagging with GFP by Hayashi et al. (8)). The Human Protein Atlas (9) has collected images for over 6,000 proteins to date using monospecific antisera. The availability of such collections has permitted automated classification systems to be applied on a scale not previously possible. In the first such application, Chen et al. (10) developed an automated system capable of recognizing the patterns in the UCSF yeast image collection. The system used a graphical model method (11) to segment each image into single cell regions (using parallel images of differential image contrast (DIC) and a DNA-binding probe). For each cell, a feature set containing Zernike moment features, morphological features, wavelet features, DNA overlap features, edge features, and Haralick texture features was calculated. The system showed 81% agreement with visual assignments for proteins having a single location. Results using this approach depend on the accuracy of cell segmentation, and additional methods for segmentation of yeast cells have since been presented (12,13). Systems for performing other kinds of analyses of yeast images have also been described, including systems for analyzing cell morphology (14), counting peroxisomes (15), quantifying protein and RNA expression (16), and carrying out image-based screens (17, 18) . Some approaches to classifying subcellular patterns do not require segmentation into single cell regions (3, 19, 20) , but while these approaches offer reduced computation time they often sacrifice some accuracy of classification. In this article, we present a framework for subcellular pattern classification in yeast that shows both improved accuracy and computational efficiency compared with the previously reported results for this image collection. The approach does not require segmentation of images into single cell regions, eliminating the need for parallel DIC and DNA images. This makes it applicable to datasets for which images of only a single (protein) channel are available. METHODS The UCSF yeast GFP fusion localization database contains 4,156 sets of three 535 3 512 grayscale images (for DIC, DAPI, and GFP) where yeast cells in each set express different GFP-tagged proteins (4). The DAPI channel reflects the DNA distribution, and the DIC channel shows the boundaries of the cells. The original web site through which the images were made available, http://yeastgfp.ucsf.edu, is not currently online; therefore, the images are currently being made available at http://murphylab.web.cmu.edu/data. One or more labels have been assigned to each GFP image by visual examination (and in some cases using additional information) by two evaluators; a total of 22 labels were used (4). As in the previous work (10), we restricted our automated analysis to 2,655 images by selecting images assigned to a single location but eliminating those labeled as ''ambiguous'' and ''composite punctate'' which do not correspond to any particular subcellular location. In the resulting set, each image belongs to one of 20 classes such as nucleus, cytoplasm, and mitochondrion. The number of images in each class is uneven, ranging from 6 to 823. The names and sizes of the 20 classes are shown in Table 1 . Example images are shown in Figure 1 . Due to the different levels of tagged protein expression and varying positions of cells relative to the focal plane, the intensities of yeast cells with the same class label can vary significantly. Thus, we applied intensity adjustment to reduce the intra-class variance. To do this, we first took the 0.05th percentile of intensity distribution as the lowest intensity and the 99.95th percentile as the highest intensity. Then, we linearly adjusted each intensity between 0.05th percentile and 99.95th percentile to the percentile of the entire intensity range. We also mapped every intensity lower than 0.05th percentile to the minimum value and every intensity higher than 99.95th percentile to the maximum value of the entire intensity range. The rationale for using the 0.05th percentile and 99.95th percentiles instead of maximum and minimum was to avoid outlier pixels (e.g., resulting from dust particles or fluorescent debris).
The study proposed by Huh et al. REF was designed to improve both the computational efficiency and accuracy of the subcellular patterns classification.
Efficient framework for automated classification of subcellular patterns in budding yeast.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Medicine Biology