1
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Wu X, Liu S, Liang G. Detecting clusters of transcription factors based on a nonhomogeneous poisson process model. BMC Bioinformatics 2022; 23:535. [PMID: 36494794 PMCID: PMC9738027 DOI: 10.1186/s12859-022-05090-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Rapidly growing genome-wide ChIP-seq data have provided unprecedented opportunities to explore transcription factor (TF) binding under various cellular conditions. Despite the rich resources, development of analytical methods for studying the interaction among TFs in gene regulation still lags behind. RESULTS In order to address cooperative TF binding and detect TF clusters with coordinative functions, we have developed novel computational methods based on clustering the sample paths of nonhomogeneous Poisson processes. Simulation studies demonstrated the capability of these methods to accurately detect TF clusters and uncover the hierarchy of TF interactions. A further application to the multiple-TF ChIP-seq data in mouse embryonic stem cells (ESCs) showed that our methods identified the cluster of core ESC regulators reported in the literature and provided new insights on functional implications of transcrisptional regulatory modules. CONCLUSIONS Effective analytical tools are essential for studying protein-DNA relations. Information derived from this research will help us better understand the orchestration of transcription factors in gene regulation processes.
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Affiliation(s)
- Xiaowei Wu
- grid.438526.e0000 0001 0694 4940Department of Statistics, Virginia Tech, 250 Drillfield Drive, Blacksburg, VA 24061 USA
| | - Shicheng Liu
- grid.438526.e0000 0001 0694 4940Department of Mathematics, Virginia Tech, 225 Stanger Street, Blacksburg, VA 24061 USA
| | - Guanying Liang
- grid.438526.e0000 0001 0694 4940Department of Mathematics, Virginia Tech, 225 Stanger Street, Blacksburg, VA 24061 USA
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2
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Venkatesh I, Mehra V, Wang Z, Simpson MT, Eastwood E, Chakraborty A, Beine Z, Gross D, Cabahug M, Olson G, Blackmore MG. Co-occupancy identifies transcription factor co-operation for axon growth. Nat Commun 2021; 12:2555. [PMID: 33953205 PMCID: PMC8099911 DOI: 10.1038/s41467-021-22828-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 03/29/2021] [Indexed: 12/13/2022] Open
Abstract
Transcription factors (TFs) act as powerful levers to regulate neural physiology and can be targeted to improve cellular responses to injury or disease. Because TFs often depend on cooperative activity, a major challenge is to identify and deploy optimal sets. Here we developed a bioinformatics pipeline, centered on TF co-occupancy of regulatory DNA, and used it to predict factors that potentiate the effects of pro-regenerative Klf6 in vitro. High content screens of neurite outgrowth identified cooperative activity by 12 candidates, and systematic testing in a mouse model of corticospinal tract (CST) damage substantiated three novel instances of pairwise cooperation. Combined Klf6 and Nr5a2 drove the strongest growth, and transcriptional profiling of CST neurons identified Klf6/Nr5a2-responsive gene networks involved in macromolecule biosynthesis and DNA repair. These data identify TF combinations that promote enhanced CST growth, clarify the transcriptional correlates, and provide a bioinformatics approach to detect TF cooperation.
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Affiliation(s)
- Ishwariya Venkatesh
- Department of Biomedical Sciences, Marquette University, Milwaukee, WI, USA.
| | - Vatsal Mehra
- Department of Biomedical Sciences, Marquette University, Milwaukee, WI, USA
| | - Zimei Wang
- Department of Biomedical Sciences, Marquette University, Milwaukee, WI, USA
| | - Matthew T Simpson
- Department of Biomedical Sciences, Marquette University, Milwaukee, WI, USA
| | - Erik Eastwood
- Department of Biomedical Sciences, Marquette University, Milwaukee, WI, USA
| | | | - Zac Beine
- Department of Biomedical Sciences, Marquette University, Milwaukee, WI, USA
| | - Derek Gross
- Department of Biomedical Sciences, Marquette University, Milwaukee, WI, USA
| | - Michael Cabahug
- Department of Biomedical Sciences, Marquette University, Milwaukee, WI, USA
| | - Greta Olson
- Department of Biomedical Sciences, Marquette University, Milwaukee, WI, USA
| | - Murray G Blackmore
- Department of Biomedical Sciences, Marquette University, Milwaukee, WI, USA.
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3
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Hu H, Zhang Q, Hu FF, Liu CJ, Guo AY. A comprehensive survey for human transcription factors on expression, regulation, interaction, phenotype and cancer survival. Brief Bioinform 2021; 22:6124917. [PMID: 33517372 DOI: 10.1093/bib/bbab002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 12/30/2020] [Accepted: 01/02/2021] [Indexed: 11/13/2022] Open
Abstract
Transcription factors (TFs) act as key regulators in biological processes through controlling gene expression. Here, we conducted a systematic study for all human TFs on the expression, regulation, interaction, mutation, phenotype and cancer survival. We revealed that the average expression levels of TFs in normal tissues were lower than 50% expression of non-TFs, whereas TF expression was increased in cancers. TFs that are specifically expressed in an individual tissue or cancer may be potential marker genes. For instance, TGIF2LX/Y were preferentially expressed in testis and NEUROG1, PRDM14, SRY, ZNF705A and ZNF716 were specifically highly expressed in germ cell tumors. We found different distributions of target genes and TF co-regulations in different TF families. Some small TF families have huge protein interaction pairs, suggesting their central roles in transcriptional regulation. The bZIP family is a small family involving many signaling pathways. Survival analysis indicated that most TFs significantly affect survival of one or more cancers. Some survival-related TFs were also specifically highly expressed in the corresponding cancer types, which may be potential targets for cancer therapy. Finally, we identified 43 TFs whose mutations were closely correlated to survival, suggesting their cancer-driven roles. The systematic analysis of TFs provides useful clues for further investigation of TF regulatory mechanisms and the role of TFs in diseases.
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Affiliation(s)
- Hui Hu
- Center for Artificial Intelligence Biology, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qiong Zhang
- Center for Artificial Intelligence Biology, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Fei-Fei Hu
- Center for Artificial Intelligence Biology, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Chun-Jie Liu
- Center for Artificial Intelligence Biology, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - An-Yuan Guo
- Center for Artificial Intelligence Biology, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
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4
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Banerjee S, Zhu H, Tang M, Feng WC, Wu X, Xie H. Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells. Front Genet 2019; 9:731. [PMID: 30697231 PMCID: PMC6341026 DOI: 10.3389/fgene.2018.00731] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 12/22/2018] [Indexed: 12/19/2022] Open
Abstract
Gene expression regulation is a complex process involving the interplay between transcription factors and chromatin states. Significant progress has been made toward understanding the impact of chromatin states on gene expression. Nevertheless, the mechanism of transcription factors binding combinatorially in different chromatin states to enable selective regulation of gene expression remains an interesting research area. We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors to form regulatory modules in different chromatin states. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. These chromatin-state-specific regulatory modules were found to have significant influence on gene expression. We also observed different motif preferences for certain TFs between different chromatin states. Our results reveal a degree of interdependency between chromatin states and combinatorial binding of proteins in the complex transcriptional regulatory process. The software package is available on Github at - https://github.com/BSharmi/DPM-LGCP.
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Affiliation(s)
- Sharmi Banerjee
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, United States.,Biocomplexity Institute of Virginia Tech, Blacksburg, VA, United States
| | - Hongxiao Zhu
- Department of Statistics, Virginia Tech, Blacksburg, VA, United States
| | - Man Tang
- Department of Statistics, Virginia Tech, Blacksburg, VA, United States
| | - Wu-Chun Feng
- Department of Computer Science, Virginia Tech, Blacksburg, VA, United States
| | - Xiaowei Wu
- Department of Statistics, Virginia Tech, Blacksburg, VA, United States
| | - Hehuang Xie
- Biocomplexity Institute of Virginia Tech, Blacksburg, VA, United States.,Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Blacksburg, VA, United States.,Department of Biological Sciences, Virginia Tech, Blacksburg, VA, United States.,School of Neuroscience, Virginia Tech, Blacksburg, VA, United States
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5
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van Bömmel A, Love MI, Chung HR, Vingron M. coTRaCTE predicts co-occurring transcription factors within cell-type specific enhancers. PLoS Comput Biol 2018; 14:e1006372. [PMID: 30142147 PMCID: PMC6126874 DOI: 10.1371/journal.pcbi.1006372] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 09/06/2018] [Accepted: 07/17/2018] [Indexed: 02/06/2023] Open
Abstract
Cell-type specific gene expression is regulated by the combinatorial action of transcription factors (TFs). In this study, we predict transcription factor (TF) combinations that cooperatively bind in a cell-type specific manner. We first divide DNase hypersensitive sites into cell-type specifically open vs. ubiquitously open sites in 64 cell types to describe possible cell-type specific enhancers. Based on the pattern contrast between these two groups of sequences we develop "co-occurring TF predictor on Cell-Type specific Enhancers" (coTRaCTE) - a novel statistical method to determine regulatory TF co-occurrences. Contrasting the co-binding of TF pairs between cell-type specific and ubiquitously open chromatin guarantees the high cell-type specificity of the predictions. coTRaCTE predicts more than 2000 co-occurring TF pairs in 64 cell types. The large majority (70%) of these TF pairs is highly cell-type specific and overlaps in TF pair co-occurrence are highly consistent among related cell types. Furthermore, independently validated co-occurring and directly interacting TFs are significantly enriched in our predictions. Focusing on the regulatory network derived from the predicted co-occurring TF pairs in embryonic stem cells (ESCs) we find that it consists of three subnetworks with distinct functions: maintenance of pluripotency governed by OCT4, SOX2 and NANOG, regulation of early development governed by KLF4, STAT3, ZIC3 and ZNF148 and general functions governed by MYC, TCF3 and YY1. In summary, coTRaCTE predicts highly cell-type specific co-occurring TFs which reveal new insights into transcriptional regulatory mechanisms.
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Affiliation(s)
- Alena van Bömmel
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Michael I. Love
- Department of Biostatistics, Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Ho-Ryun Chung
- Otto Warburg Laboratory, Max Planck Institute for Molecular Genetics, Berlin, Germany
- Philipps-Universität Marburg, Fachbereich Medizin, Institut für Medizinische Bioinformatik und Biostatistik, Marburg, Germany
| | - Martin Vingron
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
- * E-mail:
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6
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KLF6 and STAT3 co-occupy regulatory DNA and functionally synergize to promote axon growth in CNS neurons. Sci Rep 2018; 8:12565. [PMID: 30135567 PMCID: PMC6105645 DOI: 10.1038/s41598-018-31101-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 08/10/2018] [Indexed: 11/26/2022] Open
Abstract
The failure of axon regeneration in the CNS limits recovery from damage and disease. Members of the KLF family of transcription factors can exert both positive and negative effects on axon regeneration, but the underlying mechanisms are unclear. Here we show that forced expression of KLF6 promotes axon regeneration by corticospinal tract neurons in the injured spinal cord. RNA sequencing identified 454 genes whose expression changed upon forced KLF6 expression in vitro, including sub-networks that were highly enriched for functions relevant to axon extension including cytoskeleton remodeling, lipid synthesis, and bioenergetics. In addition, promoter analysis predicted a functional interaction between KLF6 and a second transcription factor, STAT3, and genome-wide footprinting using ATAC-Seq data confirmed frequent co-occupancy. Co-expression of the two factors yielded a synergistic elevation of neurite growth in vitro. These data clarify the transcriptional control of axon growth and point the way toward novel interventions to promote CNS regeneration.
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Narad P, Anand L, Gupta R, Sengupta A. Construction of Discrete Model of Human Pluripotency in Predicting Lineage-Specific Outcomes and Targeted Knockdowns of Essential Genes. Sci Rep 2018; 8:11031. [PMID: 30038409 PMCID: PMC6056480 DOI: 10.1038/s41598-018-29480-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 07/06/2018] [Indexed: 01/08/2023] Open
Abstract
A network consisting of 45 core genes was developed for the genes/proteins responsible for loss/gain of function in human pluripotent stem cells. The nodes were included on the basis of literature curation. The initial network topology was further refined by constructing an inferred Boolean model from time-series RNA-seq expression data. The final Boolean network was obtained by integration of the initial topology and the inferred topology into a refined model termed as the integrated model. Expression levels were observed to be bi-modular for most of the genes involved in the mechanism of human pluripotency. Thus, single and combinatorial perturbations/knockdowns were executed using an in silico approach. The model perturbations were validated with literature studies. A number of outcomes are predicted using the knockdowns of the core pluripotency circuit and we are able to establish the minimum requirement for maintenance of pluripotency in human. The network model is able to predict lineage-specific outcomes and targeted knockdowns of essential genes involved in human pluripotency which are challenging to perform due to ethical constraints surrounding human embryonic stem cells.
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Affiliation(s)
- Priyanka Narad
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India.
| | - Lakshay Anand
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
| | - Romasha Gupta
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
| | - Abhishek Sengupta
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
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Xu H, Ang YS, Sevilla A, Lemischka IR, Ma'ayan A. Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells. PLoS Comput Biol 2014; 10:e1003777. [PMID: 25122140 PMCID: PMC4133156 DOI: 10.1371/journal.pcbi.1003777] [Citation(s) in RCA: 74] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2014] [Accepted: 06/27/2014] [Indexed: 11/22/2022] Open
Abstract
A 30-node signed and directed network responsible for self-renewal and pluripotency of mouse embryonic stem cells (mESCs) was extracted from several ChIP-Seq and knockdown followed by expression prior studies. The underlying regulatory logic among network components was then learned using the initial network topology and single cell gene expression measurements from mESCs cultured in serum/LIF or serum-free 2i/LIF conditions. Comparing the learned network regulatory logic derived from cells cultured in serum/LIF vs. 2i/LIF revealed differential roles for Nanog, Oct4/Pou5f1, Sox2, Esrrb and Tcf3. Overall, gene expression in the serum/LIF condition was more variable than in the 2i/LIF but mostly consistent across the two conditions. Expression levels for most genes in single cells were bimodal across the entire population and this motivated a Boolean modeling approach. In silico predictions derived from removal of nodes from the Boolean dynamical model were validated with experimental single and combinatorial RNA interference (RNAi) knockdowns of selected network components. Quantitative post-RNAi expression level measurements of remaining network components showed good agreement with the in silico predictions. Computational removal of nodes from the Boolean network model was also used to predict lineage specification outcomes. In summary, data integration, modeling, and targeted experiments were used to improve our understanding of the regulatory topology that controls mESC fate decisions as well as to develop robust directed lineage specification protocols. For this study we first constructed a directed and signed network consisting of 15 pluripotency regulators and 15 lineage commitment markers that extensively interact to regulate mouse embryonic stem cells fate decisions from data available in the public domain. Given the connectivity structure of this network, the underlying regulatory logic was learned using single cell gene expression measurements of mESCs cultured in two different conditions. With connectivity and logic learned, the network was then simulated using a dynamic Boolean logic framework. Such simulations enabled prediction of knockdown effects on the overall activity of the network. Such predictions were validated by single and combinatorial RNA interference experiments followed by expression measurements. Finally, lineage specification outcomes upon single and combinatorial gene knockdowns were predicted for all possible knockdown combinations.
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Affiliation(s)
- Huilei Xu
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Department of Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Yen-Sin Ang
- Department of Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Ana Sevilla
- Department of Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Ihor R. Lemischka
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Department of Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- * E-mail: (IRL); (AM)
| | - Avi Ma'ayan
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- * E-mail: (IRL); (AM)
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Cha M, Zhou Q. Detecting clustering and ordering binding patterns among transcription factors via point process models. Bioinformatics 2014; 30:2263-71. [PMID: 24790155 DOI: 10.1093/bioinformatics/btu303] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Recent development in ChIP-Seq technology has generated binding data for many transcription factors (TFs) in various cell types and cellular conditions. This opens great opportunities for studying combinatorial binding patterns among a set of TFs active in a particular cellular condition, which is a key component for understanding the interaction between TFs in gene regulation. RESULTS As a first step to the identification of combinatorial binding patterns, we develop statistical methods to detect clustering and ordering patterns among binding sites (BSs) of a pair of TFs. Testing procedures based on Ripley's K-function and its generalizations are developed to identify binding patterns from large collections of BSs in ChIP-Seq data. We have applied our methods to the ChIP-Seq data of 91 pairs of TFs in mouse embryonic stem cells. Our methods have detected clustering binding patterns between most TF pairs, which is consistent with the findings in the literature, and have identified significant ordering preferences, relative to the direction of target gene transcription, among the BSs of seven TFs. More interestingly, our results demonstrate that the identified clustering and ordering binding patterns between TFs are associated with the expression of the target genes. These findings provide new insights into co-regulation between TFs. AVAILABILITY AND IMPLEMENTATION See 'www.stat.ucla.edu/∼zhou/TFKFunctions/' for source code.
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Affiliation(s)
- Maria Cha
- Department of Statistics, University of California, Los Angeles, CA 90095, USA
| | - Qing Zhou
- Department of Statistics, University of California, Los Angeles, CA 90095, USA
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