Microsoft Technology Licensing, LLC

Microsoft Corporation

Ernst S Henle

Brandon T Hunt

Andrey Y Bondarenko

Lee Weng

Alexander Alexandrovich Spiridonov

Silvia C Vega

Alexander Spiridonov

Andrey Bondarenko

Brandon Hunt

Hardware and software components are configured to image-process centroid data as if they were profile data to discover biological features. Proper mass/charge bin size are determined to raster centroid data into LC/MS images. To correct for the decrease in precision, the LC/MS images are re-evaluated in view of the original centroid data. Peak detection from the binned image is refined by re-considering the corresponding centroid data through cluster analysis. Additionally, some peaks that were merged through processing may be resolved into individual peaks by identifying more than one significant cluster masked by a peak from the binned image.

Centroid processing

A regional redefiner redefines a bounding area in sets of chromatographic/mass spectroscopic images. The redefiner defines and localizes peaks in the images which point to a common feature of interest. A redefined bounding area locates other peaks associated with the feature of interest. Peaks can be iteratively identified and extracted using constituent images or a composite image formed as a combination of a set of constituent images.

Iterative processing

Non-contiguous regions of interest as well as contiguous regions of interest are similarly processed. After an isotope peak detector has identified isotope peaks on LC/MS images, a microaligner microaligns bounding areas of identified isotope peaks and redefines the bounding areas to help subsequent scoring process. Forms of isotope peaks influence formation of a peak association matrix and a mass/charge association map which creates association in the mass/charge dimension. A correlation scorer produces reproducibility scores as well as quality scores to help aid scientists to discover biological features of interest.

Non-contiguous regions processing

An image processing system extracts parts or characteristics of interest from prepared biological samples One suitable use of the image processing system is to find biomarkers. But many other suitable uses are possible. Some components of the system include image preprocessing (data interpolation, retention time alignment, image noise filtering, background estimation, and formation of a composite image); image feature extraction (peaks, isotope groups, and charge groups); and computation of feature characteristics and expression statistics, differential expression, and non-differential expression. Outputs of the system include a candidate list of parts or characteristic of interest for aiding further discovery.

Discover biological features using composite images

In differential and non-differential analyses, composite images derived from replicates of liquid-chromatography/mass-spectrometry processes can provide scientists with a better signal-to-noise ratio in discovering biological features of interest. Certain distinct peaks in composite images point to distinct biological features but some distinct peaks in composite images may also point to biological features that have common chemical species ancestry. A peak reassembly process is used to indicate whether two adjacent peaks should point to a biological feature using complementation analysis and collision analysis.

Peak reassembly

After prepared biological samples have been submitted to liquid-chromatography/mass spectrometry equipment, digital images are produced that show variations. Some of these variations may be of interest while others are not of interest. Variations in regions of interest can be correlated and correlation scores produced to classify biological features aid in scientific discovery. Shape properties of variations can also be calculated by geometric scores. A microalignment method aids the correlation calculation without resorting to macroalignment.

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Issaquah, WA, United States of America

Ernst S Henle