Megavariate data analysis of mass spectrometric proteomics data using latent variable projection method

Lee, Kwan R.; Lin, Xiwu; Park, Daniel C.; Eslava, Sergio

doi:10.1002/pmic.200300515

Cited by 70 publications

(49 citation statements)

References 13 publications

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“…Identification of disease specific proteins could yield mechanistic information as well as potential diagnostic markers or drug targets. The importance in recognizing these points is highlighted in recent comparative analyses of profile spectra [25][26][27][28][29][30][31], whereby varied biostatisticians all reported an accuracy >90% in classifying the same profile spectra from either tumor or non-tumor samples. However, in identifying which ions were most significant in determining the classifications there was little to no agreement.…”

Section: Introductionmentioning

confidence: 99%

Processing MALDI mass spectra to improve mass spectral direct tissue analysis

Norris¹,

Cornett

Mobley

et al. 2007

International Journal of Mass Spectrometry

193

188

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Profiling and imaging biological specimens using MALDI mass spectrometry has significant potential to contribute to our understanding and diagnosis of disease. The technique is efficient and high-throughput providing a wealth of data about the biological state of the sample from a very simple and direct experiment. However, in order for these techniques to be put to use for clinical purposes, the approaches used to process and analyze the data must improve. This study examines some of the existing tools to baseline subtract, normalize, align, and remove spectral noise for MALDI data, comparing the advantages of each. A preferred workflow is presented that can be easily implemented for data in ASCII format. The advantages of using such an approach are discussed for both molecular profiling and imaging mass spectrometry.

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Section: Introductionmentioning

confidence: 99%

Processing MALDI mass spectra to improve mass spectral direct tissue analysis

Norris¹,

Cornett

Mobley

et al. 2007

International Journal of Mass Spectrometry

193

188

View full text Add to dashboard Cite

show abstract

“…Distinguishing protein ions as disease-specific requires isolating those abundance changes symptomatic of disease from those changes influenced by experimental conditions. From this perspective, various studies have explored techniques for pre-processing spectra as a means of minimizing overall variance (7)(8)(9)(10)(11) prior to statistical analysis, but these procedures vary and do not completely eliminate inconsistent classification results (7,(11)(12)(13)(14)(15). Nevertheless, the existing body of work is strong evidence that biomarker discovery by tissue profiling is possible and may one day benefit the clinical diagnosis and treatment of disease.…”

mentioning

confidence: 99%

A Novel Histology-directed Strategy for MALDI-MS Tissue Profiling That Improves Throughput and Cellular Specificity in Human Breast Cancer

Cornett

Mobley

Dias

et al. 2006

Molecular & Cellular Proteomics

175

149

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We describe a novel tissue profiling strategy that improves the cellular specificity and analysis throughput of protein profiles obtained by direct MALDI analysis. The new approach integrates the cellular specificity of histology, the accuracy and reproducibility of robotic liquid dispensing, and the speed and objectivity of automated spectra acquisition. Traditional methodologies for preparing and analyzing tissue samples rely heavily on manual procedures, which for various reasons discussed, restrict cellular specificity and sample throughput. Here, a robotic spotter deposits micron-sized droplets of matrix precisely onto foci of normal mammary epithelium, ductal carcinoma in situ, invasive mammary cancer, and peritumoral stroma selected by a pathologist from high resolution histological images of sectioned human breast cancer samples. The location of each matrix spot was then determined and uploaded into the instrument to facilitate automated profile acquisition by MALDI-TOF. In the example shown, the different lesions were clearly differentiated using mass profiling. Further, the workflow permits a visual projection of any information produced from the profile analyses directly on the histological image for a unique combination of proteomic and histological assessment of sample regions. The higher performance characteristics offered by the new workflow promises to be a significant advancement toward the next generation of tissue profiling studies. Molecular & Cellular Proteomics 5: 1975-1983, 2006.

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“…One of our central tools was wavelet decomposition. Wavelets have been used before in mass spectrometry [8,9] and in other domains like measuring time series similarities [10]. Nevertheless they all worked with the wavelet coefficients.…”

Section: Resultsmentioning

confidence: 99%

Feature Extraction from Mass Spectra for Classification of Pathological States

Kalousis

Prados

Rexhepaj

et al. 2005

Knowledge Discovery in Databases: PKDD 2005

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Abstract. Mass spectrometry is becoming an important tool in proteomics. The representation of mass spectra is characterized by very high dimensionality and a high level of redundancy. Here we present a feature extraction method for mass spectra that directly models for domain knowledge, reduces the dimensionality and redundancy of the initial representation and controls for the level of granularity of feature extraction by seeking to optimize classification accuracy. A number of experiments are performed which show that the feature extraction preserves the initial discriminatory content of the learning examples. Proteomics and Mass SpectrometryClinical proteomics aims at investigating changes in protein expression in order to discover new disease markers and drug targets. Mass spectrometry is emerging as an important tool for biomarker discovery in proteomics. To discover these biomarker patterns, the data miner must face a number of technical challenges, foremost among which is the extremely high-dimensionality and high redundancy of mass spectra.In this paper we will present a method of feature extraction from mass spectra that retains as much as possible the initial information content of learning examples, extracts biologically meaningful features, reduces the degree of spatial redundancy, and achieves a significant level of dimensionality reduction.One of the main problems in preprocessing and feature extraction from mass spectra is the appropriate selection of the preprocessing parameters. This is qualitative relying on systematic experimentation and visual inspection of the resulting spectra. There is a strong need for automatic and objective methods of parameter selection, [1]. The problem is that it is not obvious what should be the measure that a given parameter set should optimize. However when the goal is classification and biomarker discovery one obvious measure to optimize is classification accuracy. We propose a solution that tightly couples the preprocessing and feature extraction steps with the learning task and uses the estimated classification performance of the latter to guide the selection of the appropriate set of parameters. In order to do that we exploit a common practice in machine learning in which cross-validation is used for parameter selection, [2].

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Megavariate data analysis of mass spectrometric proteomics data using latent variable projection method

Cited by 70 publications

References 13 publications

Processing MALDI mass spectra to improve mass spectral direct tissue analysis

Processing MALDI mass spectra to improve mass spectral direct tissue analysis

A Novel Histology-directed Strategy for MALDI-MS Tissue Profiling That Improves Throughput and Cellular Specificity in Human Breast Cancer

Feature Extraction from Mass Spectra for Classification of Pathological States

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