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Pasarkar A, Kinsella I, Zhou P, Wu M, Pan D, Fan JL, Wang Z, Abdeladim L, Peterka DS, Adesnik H, Ji N, Paninski L. maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.14.557777. [PMID: 37745388 PMCID: PMC10515957 DOI: 10.1101/2023.09.14.557777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
A number of calcium imaging methods have been developed to monitor the activity of large populations of neurons. One particularly promising approach, Bessel imaging, captures neural activity from a volume by projecting within the imaged volume onto a single imaging plane, therefore effectively mixing signals and increasing the number of neurons imaged per pixel. These signals must then be computationally demixed to recover the desired neural activity. Unfortunately, currently-available demixing methods can perform poorly in the regime of high imaging density (i.e., many neurons per pixel). In this work we introduce a new pipeline (maskNMF) for demixing dense calcium imaging data. The main idea is to first denoise and temporally sparsen the observed video; this enhances signal strength and reduces spatial overlap significantly. Next we detect neurons in the sparsened video using a neural network trained on a library of neural shapes. These shapes are derived from segmented electron microscopy images input into a Bessel imaging model; therefore no manual selection of "good" neural shapes from the functional data is required here. After cells are detected, we use a constrained non-negative matrix factorization approach to demix the activity, using the detected cells' shapes to initialize the factorization. We test the resulting pipeline on both simulated and real datasets and find that it is able to achieve accurate demixing on denser data than was previously feasible, therefore enabling faithful imaging of larger neural populations. The method also provides good results on more "standard" two-photon imaging data. Finally, because much of the pipeline operates on a significantly compressed version of the raw data and is highly parallelizable, the algorithm is fast, processing large datasets faster than real time.
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Affiliation(s)
- Amol Pasarkar
- Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Computer Science, Columbia University, New York, NY, 10027, USA
| | - Ian Kinsella
- Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Statistics, Columbia University, New York, NY, 10027, USA
| | - Pengcheng Zhou
- Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China
| | - Melissa Wu
- Department of Biomedical Engineering, Duke University, Durham, NC 27708
| | - Daisong Pan
- Department of Physics, University of California, Berkeley, California 94720, USA
| | - Jiang Lan Fan
- Joint Bioengineering Graduate Program, University of California, Berkeley, CA 94720
| | - Zhen Wang
- Department of Electrical and Computer Engineering, UCLA, Los Angeles, CA, 90095, USA
| | - Lamiae Abdeladim
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Darcy S Peterka
- Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Hillel Adesnik
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Na Ji
- Department of Physics, University of California, Berkeley, California 94720, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Liam Paninski
- Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Statistics, Columbia University, New York, NY, 10027, USA
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Xu H, Shao Z, Zhang S, Liu X, Zeng P. How can childhood maltreatment affect post-traumatic stress disorder in adult: Results from a composite null hypothesis perspective of mediation analysis. Front Psychiatry 2023; 14:1102811. [PMID: 36970281 PMCID: PMC10033829 DOI: 10.3389/fpsyt.2023.1102811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 02/20/2023] [Indexed: 03/11/2023] Open
Abstract
BackgroundA greatly growing body of literature has revealed the mediating role of DNA methylation in the influence path from childhood maltreatment to psychiatric disorders such as post-traumatic stress disorder (PTSD) in adult. However, the statistical method is challenging and powerful mediation analyses regarding this issue are lacking.MethodsTo study how the maltreatment in childhood alters long-lasting DNA methylation changes which further affect PTSD in adult, we here carried out a gene-based mediation analysis from a perspective of composite null hypothesis in the Grady Trauma Project (352 participants and 16,565 genes) with childhood maltreatment as exposure, multiple DNA methylation sites as mediators, and PTSD or its relevant scores as outcome. We effectively addressed the challenging issue of gene-based mediation analysis by taking its composite null hypothesis testing nature into consideration and fitting a weighted test statistic.ResultsWe discovered that childhood maltreatment could substantially affected PTSD or PTSD-related scores, and that childhood maltreatment was associated with DNA methylation which further had significant roles in PTSD and these scores. Furthermore, using the proposed mediation method, we identified multiple genes within which DNA methylation sites exhibited mediating roles in the influence path from childhood maltreatment to PTSD-relevant scores in adult, with 13 for Beck Depression Inventory and 6 for modified PTSD Symptom Scale, respectively.ConclusionOur results have the potential to confer meaningful insights into the biological mechanism for the impact of early adverse experience on adult diseases; and our proposed mediation methods can be applied to other similar analysis settings.
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Affiliation(s)
- Haibo Xu
- Center for Mental Health Education and Research, Xuzhou Medical University, Xuzhou, China
- School of Management, Xuzhou Medical University, Xuzhou, China
- *Correspondence: Haibo Xu,
| | - Zhonghe Shao
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Xin Liu
- Center for Mental Health Education and Research, Xuzhou Medical University, Xuzhou, China
- School of Management, Xuzhou Medical University, Xuzhou, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Ping Zeng,
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Shen J, Li H, Yu X, Bai L, Dong Y, Cao J, Lu K, Tang Z. Efficient feature extraction from highly sparse binary genotype data for cancer prognosis prediction using an auto-encoder. Front Oncol 2023; 12:1091767. [PMID: 36703783 PMCID: PMC9872139 DOI: 10.3389/fonc.2022.1091767] [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: 11/07/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is how to integrate highly sparse genetic genomics data with a mass of minor effects into a prediction model for improving prediction power. We find that the deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower-dimensional continuous data in a non-linear way. This may provide benefits in risk prediction-associated genotype data. We developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for cancer prognosis. Specifically, we first reduced the size of binary biomarkers via a univariable regression model to a moderate size. Then, a trainable auto-encoder was used to learn compact features from the reduced data. Next, we performed a LASSO problem process to select the optimal combination of extracted features. Lastly, we applied such feature combination to real cancer prognostic models and evaluated the raw predictive effect of the models. The results indicated that these compressed transformation features could better improve the model's original predictive performance and might avoid an overfitting problem. This idea may be enlightening for everyone involved in cancer research, risk reduction, treatment, and patient care via integrating genomics data.
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Affiliation(s)
- Junjie Shen
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Huijun Li
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Xinghao Yu
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China,Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Lu Bai
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Yongfei Dong
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Jianping Cao
- School of Radiation Medicine and Protection and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, China
| | - Ke Lu
- Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, Suzhou, China,*Correspondence: Zaixiang Tang, ; Ke Lu,
| | - Zaixiang Tang
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China,*Correspondence: Zaixiang Tang, ; Ke Lu,
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Qiao J, Shao Z, Wu Y, Zeng P, Wang T. Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing. Lab Invest 2022; 20:424. [PMID: 36138484 PMCID: PMC9503281 DOI: 10.1186/s12967-022-03637-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 09/12/2022] [Indexed: 11/21/2022]
Abstract
Background Detecting trans-ethnic common associated genetic loci can offer important insights into shared genetic components underlying complex diseases/traits across diverse continental populations. However, effective statistical methods for such a goal are currently lacking. Methods By leveraging summary statistics available from global-scale genome-wide association studies, we herein proposed a novel genetic overlap detection method called CONTO (COmposite Null hypothesis test for Trans-ethnic genetic Overlap) from the perspective of high-dimensional composite null hypothesis testing. Unlike previous studies which generally analyzed individual genetic variants, CONTO is a gene-centric method which focuses on a set of genetic variants located within a gene simultaneously and assesses their joint significance with the trait of interest. By borrowing the similar principle of joint significance test (JST), CONTO takes the maximum P value of multiple associations as the significance measurement. Results Compared to JST which is often overly conservative, CONTO is improved in two aspects, including the construction of three-component mixture null distribution and the adjustment of trans-ethnic genetic correlation. Consequently, CONTO corrects the conservativeness of JST with well-calibrated P values and is much more powerful validated by extensive simulation studies. We applied CONTO to discover common associated genes for 31 complex diseases/traits between the East Asian and European populations, and identified many shared trait-associated genes that had otherwise been missed by JST. We further revealed that population-common genes were generally more evolutionarily conserved than population-specific or null ones. Conclusion Overall, CONTO represents a powerful method for detecting common associated genes across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03637-8.
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Affiliation(s)
- Jiahao Qiao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Zhonghe Shao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yuxuan Wu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China. .,Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China. .,Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China. .,Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China. .,Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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Fatemi N, Tierling S, Es HA, Varkiani M, Nazemalhosseini Mojarad E, Asadzadeh Aghdaei H, Walter J, Totonchi M. DNA Methylation Biomarkers in Colorectal Cancer: Clinical Applications for Precision Medicine. Int J Cancer 2022; 151:2068-2081. [PMID: 35730647 DOI: 10.1002/ijc.34186] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/29/2022] [Accepted: 06/08/2022] [Indexed: 11/06/2022]
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer death worldwide that is attributed to gradual long-term accumulation of both genetic and epigenetic changes. To reduce the mortality rate of CRC and to improve treatment efficacy, it will be important to develop accurate noninvasive diagnostic tests for screening, acute, and personalized diagnosis. Epigenetic changes such as DNA methylation play an important role in the development and progression of CRC. Over the last decade, a panel of DNA methylation markers has been reported showing a high accuracy and reproducibility in various semi-invasive or noninvasive biosamples. Research to obtain comprehensive panels of markers allowing a highly sensitive and differentiating diagnosis of CRC is ongoing. Moreover, the epigenetic alterations for cancer therapy, as a precision medicine strategy will increase their therapeutic potential over time. Here, we discuss the current state of DNA methylation-based biomarkers and their impact on CRC diagnosis. We emphasize the need to further identify and stratify methylation-biomarkers and to develop robust and effective detection methods that are applicable for a routine clinical setting of CRC diagnostics particularly at the early stage of the disease.
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Affiliation(s)
- Nayeralsadat Fatemi
- Basic & Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology & Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sascha Tierling
- Department of Genetics/Epigenetics, Faculty NT, Life Sciences, Saarland University, Saarbrücken, Germany
| | | | - Maryam Varkiani
- Department of Molecular Genetics, Faculty of Basic Sciences and Advanced Technologies in Biology, University of Science and Culture, Tehran, Iran
| | - Ehsan Nazemalhosseini Mojarad
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Asadzadeh Aghdaei
- Basic & Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology & Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Jörn Walter
- Department of Genetics/Epigenetics, Faculty NT, Life Sciences, Saarland University, Saarbrücken, Germany
| | - Mehdi Totonchi
- Basic & Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology & Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Department of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
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6
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Polygenic risk and causal inference of psychiatric comorbidity in inflammatory bowel disease among patients with European ancestry. J Transl Med 2022; 20:43. [PMID: 35086532 PMCID: PMC8793227 DOI: 10.1186/s12967-022-03242-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 01/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Approximately 40% of persons with inflammatory bowel disease (IBD) experience psychiatric comorbidities (PC). Previous studies demonstrated the polygenetic effect on both IBD and PC. In this study, we evaluated the contribution of genetic variants to PC among the IBD population. Additionally, we evaluated whether this effect is mediated by the expression level of the RBPMS gene, which was identified in our previous studies as a potential risk factor of PC in persons with IBD. MATERIALS AND METHODS The polygenic risk score (PRS) was estimated among persons with IBD of European ancestry (n = 240) from the Manitoba IBD Cohort Study by using external genome-wide association studies (GWAS). The association and prediction performance were examined between the estimated PRS and PC status among persons with IBD. Finally, regression-based models were applied to explore whether the imputed expression level of the RBPMS gene is a mediator between estimated PRS and PC status in IBD. RESULTS The estimated PRS had a significantly positive association with PC status (for the highest effect: P-value threshold = 5 × 10-3, odds ratio = 2.0, P-value = 1.5 × 10-5). Around 13% of the causal effect between the PRS and PC status in IBD was mediated by the expression level of the RBPMS gene. The area under the curve of the PRS-based PC prediction model is around 0.7 at the threshold of 5 × 10-4. CONCLUSION PC status in IBD depends on genetic influences among persons with European ancestry. The PRS could potentially be applied to PC risk screening to identify persons with IBD at a high risk of PC. Around 13% of this genetic influence could be explained by the expression level of the RBPMS gene.
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