Misty Mountain clustering: application to fast unsupervised flow cytometry gating

<p>Abstract</p> <p>Background</p> <p>There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 10⁶points that are often generated by high throughput experiments.</p> <p>Results</p> <p>To circumvent these limitations, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain, that is based on percolation theory and that efficiently analyzes large data sets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing only once the cross sections of the histogram. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. The clustering of 10⁶data points in 2D data space takes place within about 15 seconds on a standard laptop PC. Comparison of the performance of this algorithm with other state of the art automated flow cytometry gating methods indicate that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment.</p> <p>Conclusions</p> <p>Misty Mountain is fast, unbiased for cluster shape, identifies stable clusters and is robust to noise. It provides a useful, general solution for multidimensional clustering problems. We demonstrate its suitability for automated gating of flow cytometry data.</p

Diagnostic potential of near-infrared Raman spectroscopy in the stomach: differentiating dysplasia from normal tissue

Raman spectroscopy is a molecular vibrational spectroscopic technique that is capable of optically probing the biomolecular changes associated with diseased transformation. The purpose of this study was to explore near-infrared (NIR) Raman spectroscopy for identifying dysplasia from normal gastric mucosa tissue. A rapid-acquisition dispersive-type NIR Raman system was utilised for tissue Raman spectroscopic measurements at 785 nm laser excitation. A total of 76 gastric tissue samples obtained from 44 patients who underwent endoscopy investigation or gastrectomy operation were used in this study. The histopathological examinations showed that 55 tissue specimens were normal and 21 were dysplasia. Both the empirical approach and multivariate statistical techniques, including principal components analysis (PCA), and linear discriminant analysis (LDA), together with the leave-one-sample-out cross-validation method, were employed to develop effective diagnostic algorithms for classification of Raman spectra between normal and dysplastic gastric tissues. High-quality Raman spectra in the range of 800–1800 cm−1 can be acquired from gastric tissue within 5 s. There are specific spectral differences in Raman spectra between normal and dysplasia tissue, particularly in the spectral ranges of 1200–1500 cm−1 and 1600–1800 cm−1, which contained signals related to amide III and amide I of proteins, CH3CH2 twisting of proteins/nucleic acids, and the C=C stretching mode of phospholipids, respectively. The empirical diagnostic algorithm based on the ratio of the Raman peak intensity at 875 cm−1 to the peak intensity at 1450 cm−1 gave the diagnostic sensitivity of 85.7% and specificity of 80.0%, whereas the diagnostic algorithms based on PCA-LDA yielded the diagnostic sensitivity of 95.2% and specificity 90.9% for separating dysplasia from normal gastric tissue. Receiver operating characteristic (ROC) curves further confirmed that the most effective diagnostic algorithm can be derived from the PCA-LDA technique. Therefore, NIR Raman spectroscopy in conjunction with multivariate statistical technique has potential for rapid diagnosis of dysplasia in the stomach based on the optical evaluation of spectral features of biomolecules

The future of medical diagnostics: Review paper

While histopathology of excised tissue remains the gold standard for diagnosis, several new, non-invasive diagnostic techniques are being developed. They rely on physical and biochemical changes that precede and mirror malignant change within tissue. The basic principle involves simple optical techniques of tissue interrogation. Their accuracy, expressed as sensitivity and specificity, are reported in a number of studies suggests that they have a potential for cost effective, real-time, in situ diagnosis. We review the Third Scientific Meeting of the Head and Neck Optical Diagnostics Society held in Congress Innsbruck, Innsbruck, Austria on the 11th May 2011. For the first time the HNODS Annual Scientific Meeting was held in association with the International Photodynamic Association (IPA) and the European Platform for Photodynamic Medicine (EPPM). The aim was to enhance the interdisciplinary aspects of optical diagnostics and other photodynamic applications. The meeting included 2 sections: oral communication sessions running in parallel to the IPA programme and poster presentation sessions combined with the IPA and EPPM posters sessions. © 2011 Jerjes et al; licensee BioMed Central Ltd

Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure.

Numerous genetic loci have been associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP) in Europeans. We now report genome-wide association studies of pulse pressure (PP) and mean arterial pressure (MAP). In discovery (N = 74,064) and follow-up studies (N = 48,607), we identified at genome-wide significance (P = 2.7 × 10(-8) to P = 2.3 × 10(-13)) four new PP loci (at 4q12 near CHIC2, 7q22.3 near PIK3CG, 8q24.12 in NOV and 11q24.3 near ADAMTS8), two new MAP loci (3p21.31 in MAP4 and 10q25.3 near ADRB1) and one locus associated with both of these traits (2q24.3 near FIGN) that has also recently been associated with SBP in east Asians. For three of the new PP loci, the estimated effect for SBP was opposite of that for DBP, in contrast to the majority of common SBP- and DBP-associated variants, which show concordant effects on both traits. These findings suggest new genetic pathways underlying blood pressure variation, some of which may differentially influence SBP and DBP

Detection and differentiation of causative organisms of onychomycosis in an ex vivo nail model by means of Raman spectroscopy

BackgroundOnychomycosis is worldwide the most prevalent infection of the nail. It is mainly caused by the dermatophytes Trichophyton rubrum and Trichophyton mentagrophytes and to a lesser extent Trichophyton tonsurans. The yeast Candida albicans and the mould Scopulariopsis brevicaulis can also cause onychomycosis. Management of these nail conditions may require appropriate treatment methods and therefore the identification of the causative species can be of importance. However, the determination of agents causing onychomycosis is still not optimal. ObjectivesTo detect and differentiate causative organisms of onychomycosis in an ex vivo nail model by means of Raman spectroscopy. The work focusses is on the discriminative power of Raman spectroscopy for detection of differences between T. rubrum, T. mentagrophytus and T. tonsurans on human nail and distinguishing these dermatophytic from the non-dermatophytic species S. brevicaulis and C. albicans. MethodsRaman spectra (200/sample) were taken from 50-m human nail slices infected with T. rubrum, T. mentagrophytus, T. tonsurans, S. brevicaulis or C. albicans using a 2500 High-Performance Raman Module and 785-nm diode laser. Processed spectra were analysed by sorting the correlation matrix and presented as dendrogram and heat map. Raman spectra from suspended dermatophytic microconidia were taken for mutual comparisons. ResultsSpectral differences between the dermatophytes T. rubrum, T. mentagrophytus and T. tonsurans (635-795, 840-894, 1018-1112, 1206-1372, 1566-1700/cm) and the non-dermatophytes S. brevicaulis and C. albicans (442-610, 692-758, 866-914, 1020-1100, 1138-1380,1492-1602/cm) growing on nail were confirmed by clustering correlation showing two main clusters. Dissimilarities between tested dermatophytes were also found with T. rubrum being most different. Raman spectra of the dermatophytic microconidia varied over the whole tested 400-1800/cm range. ConclusionImportant dermatophytic and non-dermatophytic agents of onychomycosis growing on ex vivo human nail can be distinguished specifically and non-invasively by Raman spectroscopy

Compatibility of Staining Protocols for Bone Tissue with Raman Imaging

We report the use of Raman microscopy to image mouse calvaria stained with hematoxylin, eosin and toluidine blue. Raman imaging of stained specimens allows for direct correlation of histological and spectral information. A line-focus 785 nm laser imaging system with specialized near-infrared (NIR) microscope objectives and CCD detector were used to collect approximately 100 × 450 µm Raman images. Principal components analysis, a multivariate analysis technique, was used to determine whether the histological stains cause spectral interference (band shifts or intensity changes) or result in thermal damage to the examined tissue. Image analysis revealed factors for tissue components and the embedding medium, glycol methacrylate, only. Thus, Raman imaging proved to be compatible with histological stains such as hematoxylin, eosin and toluidine blue.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48009/1/223_2003_Article_38.pd

Optimal deductibles for outpatient services

Co-insurance, Deductible, Outpatient benefits, Price elasticity, I11,