A method that analyzes mass spectra using a digital computer is disclosed. The method includes entering into a digital computer a data set obtained from mass spectra from a plurality of samples. Each sample is, or is to be assigned to a class within a class set having two or more classes and each class is characterized by a different biological status. A classification model is then formed. The classification model discriminates between the classes in the class set.

Method for analyzing mass spectra

The invention describes a process for determining a biological state through the discovery and analysis of hidden or non-obvious, discriminatory biological data patterns. The biological data can be from health data, clinical data, or from a biological sample, (e.g., a biological sample from a human, e.g., serum, blood, saliva, plasma, nipple aspirants, synovial fluids, cerebrospinal fluids, sweat, urine, fecal matter, tears, bronchial lavage, swabbings, needle aspirantas, semen, vaginal fluids, pre-ejaculate.), etc. which is analyzed to determine the biological state of the donor. The biological state can be a pathologic diagnosis, toxicity state, efficacy of a drug, prognosis of a disease, etc. Specifically, the invention concerns processes that discover hidden discriminatory biological data patterns (e.g., patterns of protein expression in a serum sample that classify the biological state of an organ) that describe biological states.

Process for discriminating between biological states based on hidden patterns from biological data

Desorption electrospray ionization mass spectrometry (DESI-MS) was used in an imaging mode to interrogate the lipid profiles of thin tissue sections of canine spontaneous invasive transitional cell carcinoma of the urinary bladder (a model of human invasive bladder cancer) as well as adjacent normal tissue from four different dogs. The glycerophospholipids and sphingolipids that appear as intense signals in both the negative ion and positive ion modes were identified by tandem mass spectrometry product ion scans using collision-induced dissociation. Differences in the relative distributions of the lipid species were present between the tumor and adjacent normal tissue in both the negative and positive ion modes. DESI-MS images showing the spatial distributions of particular glycerophospholipids, sphinoglipids, and free fatty acids in both the negative and positive ion modes were compared to serial tissue sections that were stained with hematoxylin and eosin (H&E). Increased absolute and relative intensities for at least five different glycerophospholipids and three free fatty acids in the negative ion mode and at least four different lipid species in the positive ion mode were seen in the tumor region of the samples in all four dogs. In addition, one sphingolipid species exhibited increased signal intensity in the positive ion mode in normal tissue relative to the diseased tissue. Principal component analysis was also used to generate unsupervised statistical images from the negative ion mode data, and these images are in excellent agreement with the DESI images obtained from the selected ions and also the H&E-stained tissue.

/pdf/lipid-profiles-of-canine-invasive-transitional-cell-1gwwjf05zv.pdf

Lipid profiles of canine invasive transitional cell carcinoma of the urinary bladder and adjacent normal tissue by desorption electrospray ionization imaging mass spectrometry.

http://www.hpl.hp.com/techreports/90/HPL-90-161.pdf

MSnet: A Neural Network which Classifies Mass Spectra

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) is demonstrated to be a potentially useful tool for the rapid identification of yeasts, the grouping of Candida albicans strains, and the monitoring of germ tube-specific markers. Co-crystallized with sinapinic acid as the MALDI matrix, intact yeast cells yielded a sufficient number of medium-sized ions (4-15 kDa) in MALDI mass spectra to provide "mass signatures" that were diagnostic of strain type. For most isolates, the mass signatures were affected by the growth medium, length of incubation and the cell preparation method. While the overall past success of this methodology for fungal cells has been relatively low compared to its application to bacteria, fixing the yeast cells in 50% methanol inactivated the cells, reduced cell aggregation in aqueous suspension solution, and more importantly, it significantly improved the mass signature quality. This simple but critical advance in sample treatment improved mass spectrometric signal-to-noise ratios and allowed the identification of yeasts by a mass signature approach. Under optimized conditions, Candida species (C. albicans, C. glabrata, C. krusei, C. kefyr), Aspergillus species (A. terreus, A. fumigatus, A. syndowii) and other yeast genera (Cryptococcus neoformans, Saccharomyces cerevisiae and a Rhodotorula sp.) could be distinguished. Within the C. albicans species, several common ions in the m/z 5,000-10,000 range were apparent in the mass spectra of all tested strains. In addition to shared ions, the mass spectra of individual C. albicans strains permitted grouping of the strains. Principal component analysis (PCA) was employed to confirm spectral reproducibility and C. albicans strain grouping by mass signatures. Finally, C. albicans germ tubes produced MALDI-TOF mass signatures that differed from yeast forms of this species. This is a rapid, sensitive and simple method for identifying yeasts, grouping strains and following the morphogenesis of C. albicans.

MALDI-TOF mass signatures for differentiation of yeast species, strain grouping and monitoring of morphogenesis markers

A rule-building expert system for classification of mass spectra

http://link.springer.com/content/pdf/10.1007/978-1-4684-1312-0_11.pdf

Approaches to Pyrolysis/Mass Spectrometry Data Analysis of Biological Materials

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A rule-building expert system for classification of mass spectra

Approaches to Pyrolysis/Mass Spectrometry Data Analysis of Biological Materials