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Goh WWB, Hui HWH, Wong L. How missing value imputation is confounded with batch effects and what you can do about it. Drug Discov Today 2023; 28:103661. [PMID: 37301250 DOI: 10.1016/j.drudis.2023.103661] [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] [Received: 02/08/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/12/2023]
Abstract
In data-processing pipelines, upstream steps can influence downstream processes because of their sequential nature. Among these data-processing steps, batch effect (BE) correction (BEC) and missing value imputation (MVI) are crucial for ensuring data suitability for advanced modeling and reducing the likelihood of false discoveries. Although BEC-MVI interactions are not well studied, they are ultimately interdependent. Batch sensitization can improve the quality of MVI. Conversely, accounting for missingness also improves proper BE estimation in BEC. Here, we discuss how BEC and MVI are interconnected and interdependent. We show how batch sensitization can improve any MVI and bring attention to the idea of BE-associated missing values (BEAMs). Finally, we discuss how batch-class imbalance problems can be mitigated by borrowing ideas from machine learning.
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Affiliation(s)
- Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore; Center for Biomedical Informatics, Nanyang Technological University, Singapore.
| | - Harvard Wai Hann Hui
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore; Department of Pathology, National University of Singapore, Singapore.
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2
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Zhu Z, Chen Y, Qin X, Liu S, Wang J, Ren H. Multidimensional landscape of non-alcoholic fatty liver disease-related disease spectrum uncovered by big omics data: Profiling evidence and new perspectives. SMART MEDICINE 2023; 2:e20220029. [PMID: 39188279 PMCID: PMC11236021 DOI: 10.1002/smmd.20220029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 02/22/2023] [Indexed: 08/28/2024]
Abstract
Characterized by hepatic lipid accumulation, non-alcoholic fatty liver disease (NAFLD) is a multifactorial metabolic disorder that could promote the progression of non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). Benefiting from recent advances in omics technologies, such as high-throughput sequencing, voluminous profiling data in HCC-integrated molecular science into clinical medicine helped clinicians with rational guidance for treatments. In this review, we conclude the majority of publicly available omics data on the NAFLD-related disease spectrum and bring up new insights to inspire next-generation therapeutics against this increasingly prevalent disease spectrum in the post-genomic era.
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Affiliation(s)
- Zhengyi Zhu
- Department of Hepatobiliary SurgeryAffiliated Drum Tower HospitalMedical SchoolNanjing UniversityNanjingChina
| | - Yuyan Chen
- Department of Hepatobiliary SurgeryAffiliated Drum Tower HospitalMedical SchoolNanjing UniversityNanjingChina
| | - Xueqian Qin
- Department of Hepatobiliary SurgeryAffiliated Drum Tower HospitalMedical SchoolNanjing UniversityNanjingChina
| | - Shujun Liu
- Department of Hepatobiliary SurgeryAffiliated Drum Tower HospitalMedical SchoolNanjing UniversityNanjingChina
| | - Jinglin Wang
- Department of Hepatobiliary SurgeryAffiliated Drum Tower HospitalMedical SchoolNanjing UniversityNanjingChina
| | - Haozhen Ren
- Department of Hepatobiliary SurgeryAffiliated Drum Tower HospitalMedical SchoolNanjing UniversityNanjingChina
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3
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Pati SK, Gupta MK, Shai R, Banerjee A, Ghosh A. Missing value estimation of microarray data using Sim-GAN. Knowl Inf Syst 2022. [DOI: 10.1007/s10115-022-01718-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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4
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Gossmann TI, Waxman D. Correcting Bias in Allele Frequency Estimates Due to an Observation Threshold: A Markov Chain Analysis. Genome Biol Evol 2022; 14:evac047. [PMID: 35349695 PMCID: PMC9016752 DOI: 10.1093/gbe/evac047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/23/2022] [Indexed: 11/30/2022] Open
Abstract
There are many problems in biology and related disciplines involving stochasticity, where a signal can only be detected when it lies above a threshold level, while signals lying below threshold are simply not detected. A consequence is that the detected signal is conditioned to lie above threshold, and is not representative of the actual signal. In this work, we present some general results for the conditioning that occurs due to the existence of such an observational threshold. We show that this conditioning is relevant, for example, to gene-frequency trajectories, where many loci in the genome are simultaneously measured in a given generation. Such a threshold can lead to severe biases of allele frequency estimates under purifying selection. In the analysis presented, within the context of Markov chains such as the Wright-Fisher model, we address two key questions: (1) "What is a natural measure of the strength of the conditioning associated with an observation threshold?" (2) "What is a principled way to correct for the effects of the conditioning?". We answer the first question in terms of a proportion. Starting with a large number of trajectories, the relevant quantity is the proportion of these trajectories that are above threshold at a later time and hence are detected. The smaller the value of this proportion, the stronger the effects of conditioning. We provide an approximate analytical answer to the second question, that corrects the bias produced by an observation threshold, and performs to reasonable accuracy in the Wright-Fisher model for biologically plausible parameter values.
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Affiliation(s)
- Toni I. Gossmann
- Department of Evolutionary Genetics, Bielefeld University, Konsequenz 45, 33501 Bielefeld, Germany
- Berlin Institute for Advanced Study, Wallotstrasse 19, 14193 Berlin, Germany
| | - David Waxman
- Centre for Computational Systems Biology, ISTBI, Fudan University, 220 Handan Road, Shanghai 20433, People’s Republic of China
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5
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Lovero D, D'Oronzo S, Palmirotta R, Cafforio P, Brown J, Wood S, Porta C, Lauricella E, Coleman R, Silvestris F. Correlation between targeted RNAseq signature of breast cancer CTCs and onset of bone-only metastases. Br J Cancer 2022; 126:419-429. [PMID: 34272498 PMCID: PMC8810805 DOI: 10.1038/s41416-021-01481-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 06/04/2021] [Accepted: 06/30/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Bone is the most frequent site of metastases from breast cancer (BC), but no biomarkers are yet available to predict skeletal dissemination. METHODS We attempted to identify a gene signature correlated with bone metastasis (BM) onset in circulating tumour cells (CTCs), isolated by a DEPArray-based protocol from 40 metastatic BC patients and grouped according to metastasis sites, namely "BM" (bone-only), "ES" (extra-skeletal) or BM + ES (bone + extra-skeletal). RESULTS A 134-gene panel was first validated through targeted RNA sequencing (RNAseq) on sub-clones of the MDA-MB-231 BC cell line with variable organotropism, which successfully shaped their clustering. The panel was then applied to CTC groups and, in particular, the "BM" vs "ES" CTC comparison revealed 31 differentially expressed genes, including MAF, CAPG, GIPC1 and IL1B, playing key prognostic roles in BC. CONCLUSION Such evidence confirms that CTCs are suitable biological sources for organotropism investigation through targeted RNAseq and might deserve future applications in wide-scale prospective studies.
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Affiliation(s)
- Domenica Lovero
- Department of Biomedical Sciences and Human Oncology-Section of Internal Medicine and Clinical Oncology, University of Bari Aldo Moro, Bari, Italy
| | - Stella D'Oronzo
- Department of Biomedical Sciences and Human Oncology-Section of Internal Medicine and Clinical Oncology, University of Bari Aldo Moro, Bari, Italy
| | - Raffaele Palmirotta
- Department of Biomedical Sciences and Human Oncology-Section of Internal Medicine and Clinical Oncology, University of Bari Aldo Moro, Bari, Italy
| | - Paola Cafforio
- Department of Biomedical Sciences and Human Oncology-Section of Internal Medicine and Clinical Oncology, University of Bari Aldo Moro, Bari, Italy
| | - Janet Brown
- Department of Oncology and Metabolism, University of Sheffield, Weston Park Hospital, Sheffield, UK
| | - Steven Wood
- Department of Oncology and Metabolism, University of Sheffield, Weston Park Hospital, Sheffield, UK
| | - Camillo Porta
- Department of Biomedical Sciences and Human Oncology-Section of Internal Medicine and Clinical Oncology, University of Bari Aldo Moro, Bari, Italy
| | - Eleonora Lauricella
- Department of Biomedical Sciences and Human Oncology-Section of Internal Medicine and Clinical Oncology, University of Bari Aldo Moro, Bari, Italy
| | - Robert Coleman
- Department of Oncology and Metabolism, University of Sheffield, Weston Park Hospital, Sheffield, UK
| | - Franco Silvestris
- Department of Biomedical Sciences and Human Oncology-Section of Internal Medicine and Clinical Oncology, University of Bari Aldo Moro, Bari, Italy.
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6
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Jiang R, Sun T, Song D, Li JJ. Statistics or biology: the zero-inflation controversy about scRNA-seq data. Genome Biol 2022; 23:31. [PMID: 35063006 PMCID: PMC8783472 DOI: 10.1186/s13059-022-02601-5] [Citation(s) in RCA: 178] [Impact Index Per Article: 59.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/04/2022] [Indexed: 12/13/2022] Open
Abstract
Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.
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Affiliation(s)
- Ruochen Jiang
- Department of Statistics, University of California, Los Angeles, 90095-1554, CA, USA
| | - Tianyi Sun
- Department of Statistics, University of California, Los Angeles, 90095-1554, CA, USA
| | - Dongyuan Song
- Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, 90095-7246, CA, USA
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, 90095-1554, CA, USA.
- Department of Human Genetics, University of California, Los Angeles, 90095-7088, CA, USA.
- Department of Computational Medicine, University of California, Los Angeles, 90095-1766, CA, USA.
- Department of Biostatistics, University of California, Los Angeles, 90095-1772, CA, USA.
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7
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Abstract
Epigenome regulation has emerged as an important mechanism for the maintenance of organ function in health and disease. Dissecting epigenomic alterations and resultant gene expression changes in single cells provides unprecedented resolution and insight into cellular diversity, modes of gene regulation, transcription factor dynamics and 3D genome organization. In this chapter, we summarize the transformative single-cell epigenomic technologies that have deepened our understanding of the fundamental principles of gene regulation. We provide a historical perspective of these methods, brief procedural outline with emphasis on the computational tools used to meaningfully dissect information. Our overall goal is to aid scientists using these technologies in their favorite system of interest.
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Affiliation(s)
- Krystyna Mazan-Mamczarz
- Laboratory of Genetics and Genomics, National Institute on Aging (NIA), Intramural Research Program (IRP), National Institutes of Health (NIH), Baltimore, MD, USA
| | - Jisu Ha
- Laboratory of Genetics and Genomics, National Institute on Aging (NIA), Intramural Research Program (IRP), National Institutes of Health (NIH), Baltimore, MD, USA
| | - Supriyo De
- Laboratory of Genetics and Genomics, National Institute on Aging (NIA), Intramural Research Program (IRP), National Institutes of Health (NIH), Baltimore, MD, USA
- Laboratory of Genetics and Genomics, and Computational Biology and Genomics Core, National Institute on Aging-Intramural Research Program, National Institute of Health, Baltimore, MD, USA
| | - Payel Sen
- Laboratory of Genetics and Genomics, National Institute on Aging (NIA), Intramural Research Program (IRP), National Institutes of Health (NIH), Baltimore, MD, USA.
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8
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Patruno L, Maspero D, Craighero F, Angaroni F, Antoniotti M, Graudenzi A. A review of computational strategies for denoising and imputation of single-cell transcriptomic data. Brief Bioinform 2021; 22:bbaa222. [PMID: 33003202 DOI: 10.1093/bib/bbaa222] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 08/07/2020] [Accepted: 08/19/2020] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION The advancements of single-cell sequencing methods have paved the way for the characterization of cellular states at unprecedented resolution, revolutionizing the investigation on complex biological systems. Yet, single-cell sequencing experiments are hindered by several technical issues, which cause output data to be noisy, impacting the reliability of downstream analyses. Therefore, a growing number of data science methods has been proposed to recover lost or corrupted information from single-cell sequencing data. To date, however, no quantitative benchmarks have been proposed to evaluate such methods. RESULTS We present a comprehensive analysis of the state-of-the-art computational approaches for denoising and imputation of single-cell transcriptomic data, comparing their performance in different experimental scenarios. In detail, we compared 19 denoising and imputation methods, on both simulated and real-world datasets, with respect to several performance metrics related to imputation of dropout events, recovery of true expression profiles, characterization of cell similarity, identification of differentially expressed genes and computation time. The effectiveness and scalability of all methods were assessed with regard to distinct sequencing protocols, sample size and different levels of biological variability and technical noise. As a result, we identify a subset of versatile approaches exhibiting solid performances on most tests and show that certain algorithmic families prove effective on specific tasks but inefficient on others. Finally, most methods appear to benefit from the introduction of appropriate assumptions on noise distribution of biological processes.
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Affiliation(s)
- Lucrezia Patruno
- Department of Informatics, Systems and Communication of the University of Milan-Bicocca
| | - Davide Maspero
- Department of Informatics, Systems and Communication of the University of Milan-Bicocca
| | - Francesco Craighero
- Department of Informatics, Systems and Communication of the University of Milan-Bicocca
| | - Fabrizio Angaroni
- Department of Informatics, Systems and Communication of the University of Milan-Bicocca
| | - Marco Antoniotti
- Department of Informatics, Systems and Communication of the University of Milan-Bicocca
| | - Alex Graudenzi
- Department of Informatics, Systems and Communication of the University of Milan-Bicocca
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9
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Odenkirk MT, Reif DM, Baker ES. Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments. Anal Chem 2021; 93:7763-7773. [PMID: 34029068 PMCID: PMC8465926 DOI: 10.1021/acs.analchem.0c04850] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.
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Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - David M Reif
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States
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10
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Zhu X, Wang J, Sun B, Ren C, Yang T, Ding J. An efficient ensemble method for missing value imputation in microarray gene expression data. BMC Bioinformatics 2021; 22:188. [PMID: 33849444 PMCID: PMC8045198 DOI: 10.1186/s12859-021-04109-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 03/29/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The genomics data analysis has been widely used to study disease genes and drug targets. However, the existence of missing values in genomics datasets poses a significant problem, which severely hinders the use of genomics data. Current imputation methods based on a single learner often explores less known genomic data information for imputation and thus causes the imputation performance loss. RESULTS In this study, multiple single imputation methods are combined into an imputation method by ensemble learning. In the ensemble method, the bootstrap sampling is applied for predictions of missing values by each component method, and these predictions are weighted and summed to produce the final prediction. The optimal weights are learned from known gene data in the sense of minimizing a cost function about the imputation error. And the expression of the optimal weights is derived in closed form. Additionally, the performance of the ensemble method is analytically investigated, in terms of the sum of squared regression errors. The proposed method is simulated on several typical genomic datasets and compared with the state-of-the-art imputation methods at different noise levels, sample sizes and data missing rates. Experimental results show that the proposed method achieves the improved imputation performance in terms of the imputation accuracy, robustness and generalization. CONCLUSION The ensemble method possesses the superior imputation performance since it can make use of known data information more efficiently for missing data imputation by integrating diverse imputation methods and learning the integration weights in a data-driven way.
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Affiliation(s)
- Xinshan Zhu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.,State Key Laboratory of Digital Publishing Technology, Beijing, 100871, China
| | - Jiayu Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
| | - Biao Sun
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.
| | - Chao Ren
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
| | - Ting Yang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
| | - Jie Ding
- China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai, 201306, China
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11
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Thomas T, Rajabi E. A systematic review of machine learning-based missing value imputation techniques. DATA TECHNOLOGIES AND APPLICATIONS 2021. [DOI: 10.1108/dta-12-2020-0298] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?Design/methodology/approachThe review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.FindingsThis study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.Originality/valueIt is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.
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12
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Peng A, Mao X, Zhong J, Fan S, Hu Y. Single-Cell Multi-Omics and Its Prospective Application in Cancer Biology. Proteomics 2020; 20:e1900271. [PMID: 32223079 DOI: 10.1002/pmic.201900271] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 02/29/2020] [Indexed: 01/24/2023]
Abstract
In recent years, the emergence of single-cell omics technologies, which can profile genomics, transcriptomics, epigenomics, and proteomics, has provided unprecedented insights into characteristics of cancer, enabling higher resolution and accuracy to decipher the cellular and molecular mechanisms relating to tumorigenesis, evolution, metastasis, and immune responses. Single-cell multi-omics technologies, which are developed based on the combination of multiple single-cell mono-omics technologies, can simultaneously analyze RNA expression, single nucleotide polymorphism, epigenetic modification, or protein abundance, enabling the in-depth understanding of gene expression regulatory mechanisms. In this review, the state-of-the-art single-cell multi-omics technologies are summarized and the prospects of their application in cancer biology are discussed.
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Affiliation(s)
- Anghui Peng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Xiying Mao
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Jiawei Zhong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Shuxin Fan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Youjin Hu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510000, China
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13
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Yang MQ, Weissman SM, Yang W, Zhang J, Canaan A, Guan R. Correction to: MISC: missing imputation for single-cell RNA sequencing data. BMC SYSTEMS BIOLOGY 2019; 13:13. [PMID: 30670065 PMCID: PMC6343234 DOI: 10.1186/s12918-019-0681-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
It was highlighted that the original article [1] contained a typesetting error in the last name of Allon Canaan. This was incorrectly captured as Allon Canaann in the original article which has since been updated.
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Affiliation(s)
- Mary Qu Yang
- Joint Bioinformatics Program, University of Arkansas Little Rock George Washington Donaghey College of Engineering & IT and University of Arkansas for Medical Sciences, Little Rock, AR, 72204, USA.
| | | | - William Yang
- Department of Genetics, Yale University, New Haven, CT, 06512, USA.,Department of Computer Science, Carnegie Mellon University School of Computer Science, Pittsburgh, PA, 15213, USA
| | - Jialing Zhang
- Department of Genetics, Yale University, New Haven, CT, 06512, USA
| | - Allon Canaan
- Department of Genetics, Yale University, New Haven, CT, 06512, USA
| | - Renchu Guan
- Joint Bioinformatics Program, University of Arkansas Little Rock George Washington Donaghey College of Engineering & IT and University of Arkansas for Medical Sciences, Little Rock, AR, 72204, USA. .,College of Computer Science and Technology, Jilin University, Changchun, 130,012, Jilin, China.
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