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Manes NP, Song J, Nita-lazar A. EnsMOD: A Software Program for Omics Sample Outlier Detection. J Comput Biol 2023; 30:726-735. [PMID: 37042708 PMCID: PMC10282819 DOI: 10.1089/cmb.2022.0243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023] Open
Abstract
Detection of omics sample outliers is important for preventing erroneous biological conclusions, developing robust experimental protocols, and discovering rare biological states. Two recent publications describe robust algorithms for detecting transcriptomic sample outliers, but neither algorithm had been incorporated into a software tool for scientists. Here we describe Ensemble Methods for Outlier Detection (EnsMOD) which incorporates both algorithms. EnsMOD calculates how closely the quantitation variation follows a normal distribution, plots the density curves of each sample to visualize anomalies, performs hierarchical cluster analyses to calculate how closely the samples cluster with each other, and performs robust principal component analyses to statistically test if any sample is an outlier. The probabilistic threshold parameters can be easily adjusted to tighten or loosen the outlier detection stringency. EnsMOD can be used to analyze any omics dataset with normally distributed variance. Here it was used to analyze a simulated proteomics dataset, a multiomic (proteome and transcriptome) dataset, a single-cell proteomics dataset, and a phosphoproteomics dataset. EnsMOD successfully identified all of the simulated outliers, and subsequent removal of a detected outlier improved data quality for downstream statistical analyses.
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Affiliation(s)
- Nathan P. Manes
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Jian Song
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Aleksandra Nita-lazar
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
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Shen X, Yan H, Wang C, Gao P, Johnson CH, Snyder MP. TidyMass an object-oriented reproducible analysis framework for LC-MS data. Nat Commun 2022; 13:4365. [PMID: 35902589 PMCID: PMC9334349 DOI: 10.1038/s41467-022-32155-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/15/2022] [Indexed: 02/05/2023] Open
Abstract
Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project ( https://www.tidymass.org/ ), a comprehensive R-based computational framework that can achieve the traceable, shareable, and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structure, which provides a comprehensive, reproducible, and object-oriented computational framework. The modular architecture makes tidyMass a highly flexible and extensible tool, which other users can improve and integrate with other tools to customize their own pipeline.
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Affiliation(s)
- Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Hong Yan
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Chuchu Wang
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Peng Gao
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Caroline H Johnson
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
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Jensch A, Lopes MB, Vinga S, Radde N. ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data. Stat Methods Med Res 2022; 31:947-958. [PMID: 35072570 PMCID: PMC9014683 DOI: 10.1177/09622802211072456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The extraction of novel information from omics data is a challenging task, in
particular, since the number of features (e.g. genes) often far exceeds the
number of samples. In such a setting, conventional parameter estimation leads to
ill-posed optimization problems, and regularization may be required. In
addition, outliers can largely impact classification accuracy. Here we introduce ROSIE, an ensemble classification approach, which combines
three sparse and robust classification methods for outlier detection and feature
selection and further performs a bootstrap-based validity check. Outliers of
ROSIE are determined by the rank product test using outlier rankings of all
three methods, and important features are selected as features commonly selected
by all methods. We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify
observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue
samples. The pre-processed dataset consists of 16,600 genes and more than 1,000 samples. We demonstrate that ROSIE selects important features
and outliers in a robust way. Identified outliers are concordant with the
distribution of the commonly selected genes by the three methods, and results
are in line with other independent studies. Furthermore, we discuss the
association of some of the selected genes with the TNBC subtype in other
investigations. In summary, ROSIE constitutes a robust and sparse procedure to
identify outliers and important genes through binary classification. Our
approach is ad hoc applicable to other datasets, fulfilling the overall goal of
simultaneously identifying outliers and candidate disease biomarkers to the
targeted in therapy research and personalized medicine frameworks.
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Affiliation(s)
- Antje Jensch
- Institute for Systems Theory and Automatic Control, 9149University of Stuttgart, Germany
| | - Marta B Lopes
- Center for Mathematics and Applications (CMA), NOVA School of Science and Technology, Caparica, Portugal.,NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), NOVA School of Science and Technology, Caparica, Portugal
| | - Susana Vinga
- INESC-ID, Instituto Superior Técnico, 72971Universidade de Lisboa, Portugal.,IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Portugal
| | - Nicole Radde
- Institute for Systems Theory and Automatic Control, 9149University of Stuttgart, Germany
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Lee HA, Lin PY, Solomatina AI, Koshevoy IO, Tunik SP, Lin HW, Pan SW, Ho ML. Glucose Sensing in Human Whole Blood Based on Near-Infrared Phosphors and Outlier Treatment with the Programming Language "R". ACS OMEGA 2022; 7:198-206. [PMID: 35036691 PMCID: PMC8757351 DOI: 10.1021/acsomega.1c04344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/09/2021] [Indexed: 06/14/2023]
Abstract
A near-infrared paper-based analytical device (NIR-PAD) for glucose detection in whole blood was based on iridium(III) metal complexes embedded in a three-dimensional (3D) enzyme gel. These complexes emit NIR luminescence, can avoid interference from the color of blood, and increase the sensitivity of sensing glucose. The glucose reaction behaviors of another two different iridium(III) and platinum(II) complexes were also tested. When the glucose solution was added to the device, the oxidation of glucose by glucose oxidase caused oxygen consumption and increased the intensity of the phosphorescence emission. To the best of our knowledge, this is the first time that data have been treated with the programming language "R", which uses Tukey's test to identify the outliers in the data and calculate a median for establishing a calibration curve, in order to improve the accuracy of NIR-PADs for sensing glucose. Compared with other published devices, NIR-PADs exhibit a wider linear range (1-30 mM, [relative emission intensity] = 0.0250[glucose] + 0.0451, and R 2 = 0.9984), a low detection limit (0.7 mM), a short response time (<2 s), and a small sample volume (2 μL). Finally, blood specimens were obtained from 19 patients enrolled in Taipei Veterans General Hospital under an approved IRB protocol (Taiwan; 2017-12-002CC). The sensors exhibited remarkable characteristics for glucose detection in comparison with other methods, including the clinical method in hospitals as well as those without blood sample pretreatment or a dilution factor. The above results confirm that NIR-PAD sensors can be put to practical use for glucose detection.
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Affiliation(s)
- Hsia-An Lee
- Department
of Chemistry, Soochow University, 70 Linhsi Road,
Shihlin, Taipei 111, Taiwan
| | - Peng-Yi Lin
- Department
of Chemistry, Soochow University, 70 Linhsi Road,
Shihlin, Taipei 111, Taiwan
| | - Anastasia I. Solomatina
- Institute
of Chemistry, St. Petersburg State University, Universitetskii pr. 26, St. Petersburg 198504, Russia
| | - Igor O. Koshevoy
- Department
of Chemistry, University of Eastern Finland, Joensuu 80101, Finland
| | - Sergey P. Tunik
- Institute
of Chemistry, St. Petersburg State University, Universitetskii pr. 26, St. Petersburg 198504, Russia
| | - Hui-Wen Lin
- Department
of Mathematics, Soochow University, 70 Linhsi Road,
Shihlin, Taipei 111, Taiwan
| | - Sheng-Wei Pan
- Department
of Chest Medicine, Taipei Veterans General
Hospital, Taipei 11217, Taiwan
- School
of Medicine, National Yang Ming Chiao Tung
University, Taipei 11221, Taiwan
| | - Mei-Lin Ho
- Department
of Chemistry, Soochow University, 70 Linhsi Road,
Shihlin, Taipei 111, Taiwan
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An Efficient Algorithm for the Detection of Outliers in Mislabeled Omics Data. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2021:9436582. [PMID: 34976114 PMCID: PMC8716222 DOI: 10.1155/2021/9436582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 11/30/2021] [Indexed: 11/18/2022]
Abstract
High dimensionality and noise have made it difficult to detect related biomarkers in omics data. Through previous study, penalized maximum trimmed likelihood estimation is effective in identifying mislabeled samples in high-dimensional data with mislabeled error. However, the algorithm commonly used in these studies is the concentration step (C-step), and the C-step algorithm that is applied to robust penalized regression does not ensure that the criterion function is gradually optimized iteratively, because the regularized parameters change during the iteration. This makes the C-step algorithm runs very slowly, especially when dealing with high-dimensional omics data. The AR-Cstep (C-step combined with an acceptance-rejection scheme) algorithm is proposed. In simulation experiments, the AR-Cstep algorithm converged faster (the average computation time was only 2% of that of the C-step algorithm) and was more accurate in terms of variable selection and outlier identification than the C-step algorithm. The two algorithms were further compared on triple negative breast cancer (TNBC) RNA-seq data. AR-Cstep can solve the problem of the C-step not converging and ensures that the iterative process is in the direction that improves criterion function. As an improvement of the C-step algorithm, the AR-Cstep algorithm can be extended to other robust models with regularized parameters.
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Duindam HM, Williams DP, Asscher JJ, Hoeve M, Thayer JF, Creemers HE. Heart-wired to be cold? Exploring cardiac markers of callous-unemotional traits in incarcerated offenders. Int J Psychophysiol 2021; 170:168-177. [PMID: 34666106 DOI: 10.1016/j.ijpsycho.2021.10.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 09/02/2021] [Accepted: 10/09/2021] [Indexed: 10/20/2022]
Abstract
Elevated callous-unemotional (CU) traits have been repeatedly identified in a subgroup of offenders that displays severe antisocial behavior; establishing physiological markers may help improve early identification and treatment efforts. This study examines to what extent baseline-resting heart rate (HR) and heart rate variability (HRV) can be used as markers of CU in incarcerated juvenile and adult offenders. CU traits were assessed using the Inventory of Callous-Unemotional traits. Results of the multiple hierarchical regression tests indicated that there was a small yet significant positive association between baseline HR and CU and negative association between HRV and CU in juvenile offenders with medium model effect sizes (R2 = 0.115 for HR-CU; R2 = 0.126 for HRV-CU). The cardiac markers were unrelated to CU in adult offenders. These findings are important because they demonstrate that impaired cardiac autonomic activity is related to CU traits in juveniles, suggesting that socioemotional processing difficulties should be considered in understanding these deficits. Future research should be conducted in large samples, under reactive and static conditions, while including cardiac covariates, to get more clarity on the interplay between biological systems and behavioral expression.
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Affiliation(s)
- Hanne M Duindam
- Utrecht University, Clinical Child and Family Studies, Netherlands.
| | - DeWayne P Williams
- University of California-Irvine, Department of Psychological Sciences, United States
| | - Jessica J Asscher
- University of Amsterdam, Research Institute of Child Development and Education, Netherlands; Utrecht University, Clinical Child and Family Studies, Netherlands
| | - Machteld Hoeve
- University of Amsterdam, Research Institute of Child Development and Education, Netherlands
| | - Julian F Thayer
- University of California-Irvine, Department of Psychological Sciences, United States
| | - Hanneke E Creemers
- University of Amsterdam, Research Institute of Child Development and Education, Netherlands
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