CLUE-TIPS, Clustering Methods for Pattern Analysis of LC−MS Data

Abstract

Liquid Chromatography Mass Spectrometry (LC-MS) based proteomics is an important tool in detecting changes in peptide/protein abundances in samples potentially leading to the discovery of disease biomarker candidates. We present CLUE-TIPS (Clustering Using Euclidean distance in Tanimoto Inter-Point Space), an approach that compares complex proteomic samples for similarity/dissimilarity analysis. In CLUE-TIPS, an intersample distance feature map is generated from filtered, aligned and binarized raw LC-MS data by applying the Tanimoto distance metric to obtain normalized similarity scores between all sample pairs for each m/z value. We developed clustering and visualization methods for the intersample distance map to analyze various samples for differences at the sample level as well as the individual m/z level. An approach to query for specific m/z values that are associated with similarity/dissimilarity patterns in a set of samples was also briefly described. CLUE-TIPS can also be used as a tool in assessing the quality of LC-MS runs. The presented approach does not rely on tandem mass-spectrometry (MS/MS), isotopic labels or gels and also does not rely on feature extraction methods. CLUE-TIPS suite was applied to LC-MS data obtained from plasma samples collected at various time points and treatment conditions from immunosuppressed mice implanted with MCF-7 human breast cancer cells. The generated raw LC-MS data was used for pattern analysis and similarity/dissimilarity detection. CLUE-TIPS successfully detected the differences/similarities in samples at various time points taken during the progression of tumor, and also recognized differences/similarities in samples representing various treatment conditions.

Toward the complete characterization of host cell proteins in biotherapeutics via affinity depletions, LC-MS/MS, and multivariate analysis

“…Furthermore, due to its non-parametric nature, insensitivity to the monotonic transformations of the data, and existence of distribution-free estimates of statistical significance, Spearman correlation coefficients were used to compare abundances of the proteins common to any two HCP profiles. Jaccard distances [38][39][40] and Spearman correlations [41][42][43] have both been applied successfully to complex proteomic datasets in previous studies. We demonstrated herein that the application of these statistical tools to our host cell protein data enabled sensitive differentiation between samples (i.e., those generated from different cell lines and upstream/downstream processes).…”

Biomarkers Discovery through Multivariate Statistical Methods: A Review of Recently Developed Methods and Applications in Proteomics

“…[122] developed digger, a graphical user interface R package for analyzing 2D-DIGE data by different multivariate tools. Akella et al [48] instead developed CLUE-TIPS (Clustering Using Euclidean distance in Tanimoto Inter-Point Space), a clustering method for pattern analysis of LC-MS Data. In CLUE-TIPS, an intersample distance feature map is generated from filtered, aligned and binarized raw LC-MS data by applying the Tanimoto distance metric to obtain normalized similarity scores between all sample pairs for each m/z value.…”

“…In general, we can speak of biomarkers identification whenever a pool of features differentiating two or more groups of samples has to be identified. For what concerns the particular application to proteomics, the search for biomarkers involves the identification of features responsible for sample differentiation from a quite wide range of instrumental applications: from classical proteomics , exploiting 2D-PAGE and 2D-DIGE, to mass spectrometry-based approaches based on MALDI-TOF [24][25][26][27][28][29][30] and SELDI-TOF profiling [31][32][33][34][35][36][37][38][39][40][41][42], HPLC-MS [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57] or shotgun approaches [58][59].…”

Chemometrics

“…These methods were successfully applied to fermentation data. Hancock (31) reported that changes in peptide/protein abundance in samples indicative of potential disease biomarkers can be identified from LC/MS data using CLUE-TIPS, clustering using Euclidean distance in Tanimoto interpoint space. CLUE-TIPS can also be used to assess the quality of data from different LC/MS runs.…”