Gene expression analysis and clinical diagnosis.

BACKGROUND: A new basis for diagnostic tests is being provided by the vast amount of data on gene expression that are now becoming available through large-scale measurement of mRNA abundance. The insights gained from these resources are most likely going to provide both a better basic understanding of disease mechanisms, and to identify molecular markers for more precise diagnoses and for prediction of prognosis and treatment response. METHODS: Some quantitative RT-PCR assays are utilized today for diagnosis of both malignant and non-malignant disease, but the use of gene expression measurements in clinical medicine can be expected to increase dramatically. CONCLUSIONS: There are important technical issues that must be adequately solved in order to obtain robust assays, such as standardized protocols with appropriate quality controls that ensure reliable data for the specific samples being analysed and good inter-laboratory reproducibility.

Zherlock: an open source data analysis software.

Zherlock is an open source software that provides state-of-the-art data analysis tools to the user in an intuitive and flexible way. It is a front-end to different numerical "engines" to produce a seamless integration of algorithms written in different computer languages. Of particular interest is creating an interface to high-level scientific languages such as Octave (a Matlab clone) and R (an S-PLUS clone) to enable efficient porting of new data analytical methods. Zherlock uses advanced scientific visualization tools in 2-D and 3-D and has been extended to work on virtual reality (VR) systems. Central to Zherlock is a visual programming environment (VPE) which enables diagram based programming. These diagrams consist of nodes and connection lines where each node is an operator or a method and lines describe the flow of data between nodes. A VPE is chosen for Zherlock because it forms an effective way to control the processing pipeline in complex data analyses. The VPE is similar in functionality to other programs such as IRIS Explorer, AVS or LabVIEW.

Detection of the dipicolinic acid biomarker in Bacillus spores using Curie-point pyrolysis mass spectrometry and Fourier transform infrared spectroscopy.

Thirty-six strains of aerobic endospore-forming bacteria confirmed by polyphasic taxonomic methods to belong to Bacillus amyloliquefaciens, Bacillus cereus, Bacillus licheniformis, Bacillus megaterium, Bacillus subtilis (including Bacillus niger and Bacillus globigii), Bacillus sphaericus, and Brevi laterosporus were grown axenically on nutrient agar, and vegetative and sporulated biomasses were analyzed by Curie-point pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier-transform infrared spectroscopy (FT-IR). Chemometric methods based on rule induction and genetic programming were used to determine the physiological state (vegetative cells or spores) correctly, and these methods produced mathematical rules which could be simply interpreted in biochemical terms. For PyMS it was found that m/z 105 was characteristic and is a pyridine ketonium ion (C6H3ON+) obtained from the pyrolysis of dipicolinic acid (pyridine-2,6-dicarboxylic acid; DPA), a substance found in spores but not in vegetative cells; this was confirmed using pyrolysis-gas chromatography/mass spectrometry. In addition, a pyridine ring vibration at 1447-1439 cm-1 from DPA was found to be highly characteristic of spores in FT-IR analysis. Thus, although the original data sets recorded hundreds of spectral variables from whole cells simultaneously, a simple biomarker can be used for the rapid and unequivocal detection of spores of these organisms.

Rapid analysis of high-dimensional bioprocesses using multivariate spectroscopies and advanced chemometrics.

There are an increasing number of instrumental methods for obtaining data from biochemical processes, many of which now provide information on many (indeed many hundreds) of variables simultaneously. The wealth of data that these methods provide, however, is useless without the means to extract the required information. As instruments advance, and the quantity of data produced increases, the fields of bioinformatics and chemometrics have consequently grown greatly in importance. The chemometric methods nowadays available are both powerful and dangerous, and there are many issues to be considered when using statistical analyses on data for which there are numerous measurements (which often exceed the number of samples). It is not difficult to carry out statistical analysis on multivariate data in such a way that the results appear much more impressive than they really are. The authors present some of the methods that we have developed and exploited in Aberystwyth for gathering highly multivariate data from bioprocesses, and some techniques of sound multivariate statistical analyses (and of related methods based on neural and evolutionary computing) which can ensure that the results will stand up to the most rigorous scrutiny.

Multiscale cluster analysis.

This article describes how the concept of multiresolution is used with cluster analysis of spectral data. Multiresolution analysis progressively increases the resolution of a spectrum profile by adding levels of details contained in scales obtained from a discrete wavelet transform. At each resolution level a cluster analysis is performed on all the spectra profiles in the data set. This allows the relating of changes in the cluster pattern to various broad and narrow features in the spectral data profiles. The analysis also provides an approximate location of the important features in the original wavenumber domain.

Rapid differentiation of closely related Candida species and strains by pyrolysis-mass spectrometry and Fourier transform-infrared spectroscopy.

Two rapid spectroscopic approaches for whole-organism fingerprinting of pyrolysis-mass spectrometry (PyMS) and Fourier transform-infrared spectroscopy (FT-IR) were used to analyze a group of 29 clinical and reference Candida isolates. These strains had been identified by conventional means as belonging to one of the three species Candida albicans, C. dubliniensis (previously reported as atypical C. albicans), and C. stellatoidea (which is also closely related to C. albicans). To observe the relationships of the 29 isolates as judged by PyMS and FT-IR, the spectral data were clustered by discriminant analysis. On visual inspection of the cluster analyses from both methods, three distinct clusters, which were discrete for each of the Candida species, could be seen. Moreover, these phenetic classifications were found to be very similar to those obtained by genotypic studies which examined the HinfI restriction enzyme digestion patterns of genomic DNA and by use of the 27A C. albicans-specific probe. Both spectroscopic techniques are rapid (typically, 2 min for PyMS and 10 s for FT-IR) and were shown to be capable of successfully discriminating between closely related isolates of C. albicans, C. dubliniensis, and C. stellatoidea. We believe that these whole-organism fingerprinting methods could provide opportunities for automation in clinical microbial laboratories, improving turnaround times and the use of resources.

Variable selection in discriminant partial least-squares analysis.

Variable selection enhances the understanding and interpretability of multivariate classification models. A new chemometric method based on the selection of the most important variables in discriminant partial least-squares (VS-DPLS) analysis is described. The suggested method is a simple extension of DPLS where a small number of elements in the weight vector w is retained for each factor. The optimal number of DPLS factors is determined by cross-validation. The new algorithm is applied to four different high-dimensional spectral data sets with excellent results. Spectral profiles from Fourier transform infrared spectroscopy and pyrolysis mass spectrometry are used. To investigate the uniqueness of the selected variables an iterative VS-DPLS procedure is performed. At each iteration, the previously found selected variables are removed to see if a new VS-DPLS classification model can be constructed using a different set of variables. In this manner, it is possible to determine regions rather than individual variables that are important for a successful classification.