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PyDRMetrics - A Python toolkit for dimensionality reduction quality assessment.

ABSTRACT: High-dimensional data are pervasive in this bigdata era. To avoid the curse of the dimensionality problem, various dimensionality reduction (DR) algorithms have been proposed. To facilitate systematic DR quality comparison and assessment, this paper reviews related metrics and develops an open-source Python package pyDRMetrics. Supported metrics include reconstruction error, distance matrix, residual variance, ranking matrix, co-ranking matrix, trustworthiness, continuity, co-k-nearest neighbor size, LCMC (local continuity meta criterion), and rank-based local/global properties. pyDRMetrics provides a native Python class and a web-oriented API. A case study of mass spectra is conducted to demonstrate the package functions. A web GUI wrapper is also published to support user-friendly B/S applications.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7887408 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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pyDRMetrics - A Python toolkit for dimensionality reduction quality assessment.

Zhang Yinsheng Y Shang Qian Q Zhang Guoming G

Heliyon 20210206 2

High-dimensional data are pervasive in this bigdata era. To avoid the curse of the dimensionality problem, various dimensionality reduction (DR) algorithms have been proposed. To facilitate systematic DR quality comparison and assessment, this paper reviews related metrics and develops an open-source Python package pyDRMetrics. Supported metrics include reconstruction error, distance matrix, residual variance, ranking matrix, co-ranking matrix, trustworthiness, continuity, co-k-nearest neighbor ...[more]

PMID: 33644472

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Project description:BackgroundSystems wide modeling and analysis of signaling networks is essential for understanding complex cellular behaviors, such as the biphasic responses to different combinations of cytokines and growth factors. For example, tumor necrosis factor (TNF) can act as a proapoptotic or prosurvival factor depending on its concentration, the current state of signaling network and the presence of other cytokines. To understand combinatorial regulation in such systems, new computational approaches are required that can take into account non-linear interactions in signaling networks and provide tools for clustering, visualization and predictive modeling.ResultsHere we extended and applied an unsupervised non-linear dimensionality reduction approach, Isomap, to find clusters of similar treatment conditions in two cell signaling networks: (I) apoptosis signaling network in human epithelial cancer cells treated with different combinations of TNF, epidermal growth factor (EGF) and insulin and (II) combination of signal transduction pathways stimulated by 21 different ligands based on AfCS double ligand screen data. For the analysis of the apoptosis signaling network we used the Cytokine compendium dataset where activity and concentration of 19 intracellular signaling molecules were measured to characterise apoptotic response to TNF, EGF and insulin. By projecting the original 19-dimensional space of intracellular signals into a low-dimensional space, Isomap was able to reconstruct clusters corresponding to different cytokine treatments that were identified with graph-based clustering. In comparison, Principal Component Analysis (PCA) and Partial Least Squares - Discriminant analysis (PLS-DA) were unable to find biologically meaningful clusters. We also showed that by using Isomap components for supervised classification with k-nearest neighbor (k-NN) and quadratic discriminant analysis (QDA), apoptosis intensity can be predicted for different combinations of TNF, EGF and insulin. Prediction accuracy was highest when early activation time points in the apoptosis signaling network were used to predict apoptosis rates at later time points. Extended Isomap also outperformed PCA on the AfCS double ligand screen data. Isomap identified more functionally coherent clusters than PCA and captured more information in the first two-components. The Isomap projection performs slightly worse when more signaling networks are analyzed; suggesting that the mapping function between cues and responses becomes increasingly non-linear when large signaling pathways are considered.ConclusionWe developed and applied extended Isomap approach for the analysis of cell signaling networks. Potential biological applications of this method include characterization, visualization and clustering of different treatment conditions (i.e. low and high doses of TNF) in terms of changes in intracellular signaling they induce.

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PyDRMetrics - A Python toolkit for dimensionality reduction quality assessment.

Publications

pyDRMetrics - A Python toolkit for dimensionality reduction quality assessment.

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