Multiple incomplete views clustering via non-negative matrix factorization with its application in Alzheimer's disease analysis

2018 ◽  
Author(s):  
Kai Liu ◽  
Hua Wang ◽  
Shannon Risacher ◽  
Andrew Saykin ◽  
Li Shen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Matrix Factorization ◽  
Disease Analysis ◽  
Non Negative Matrix Factorization

scholarly journals Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yeonwoo Chung ◽  
Hyunju Lee ◽  
Michael W. Weiner ◽  
Paul Aisen ◽  
Ronald Petersen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Type 2 Diabetes ◽  
Alzheimer's Disease ◽  
Candidate Genes ◽  
Matrix Factorization ◽  
Differentially Expressed Gene ◽  
Gene Clustering ◽  
Type 3 ◽  
Non Negative Matrix Factorization

AbstractAlzheimer’s disease (AD) is a complex and heterogeneous disease that can be affected by various genetic factors. Although the cause of AD is not yet known and there is no treatment to cure this disease, its progression can be delayed. AD has recently been recognized as a brain-specific type of diabetes called type 3 diabetes. Several studies have shown that people with type 2 diabetes (T2D) have a higher risk of developing AD. Therefore, it is important to identify subgroups of patients with AD that may be more likely to be associated with T2D. We here describe a new approach to identify the correlation between AD and T2D at the genetic level. Subgroups of AD and T2D were each generated using a non-negative matrix factorization (NMF) approach, which generated clusters containing subsets of genes and samples. In the gene cluster that was generated by conventional gene clustering method from NMF, we selected genes with significant differences in the corresponding sample cluster by Kruskal–Wallis and Dunn-test. Subsequently, we extracted differentially expressed gene (DEG) subgroups, and candidate genes with the same regulation direction can be extracted at the intersection of two disease DEG subgroups. Finally, we identified 241 candidate genes that represent common features related to both AD and T2D, and based on pathway analysis we propose that these genes play a role in the common pathological features of AD and T2D. Moreover, in the prediction of AD using logistic regression analysis with an independent AD dataset, the candidate genes obtained better prediction performance than DEGs. In conclusion, our study revealed a subgroup of patients with AD that are associated with T2D and candidate genes associated between AD and T2D, which can help in providing personalized and suitable treatments.

O5-01-03: ATROPHY SUBTYPES IN ALZHEIMER'S DISEASE IDENTIFIED THROUGH NON-NEGATIVE MATRIX FACTORIZATION

2006 ◽  
Vol 14 (7S_Part_31) ◽  
pp. P1638-P1639
Author(s):  
Mara ten Kate ◽  
Ellen Dicks ◽  
Wiesje M. Van der Flier ◽  
Charlotte E. Teunissen ◽  
Philip Scheltens ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Matrix Factorization ◽  
Non Negative Matrix Factorization

scholarly journals JDSNMF: Joint Deep Semi-Non-Negative Matrix Factorization for Learning Integrative Representation of Molecular Signals in Alzheimer’s Disease

2021 ◽  
Vol 11 (8) ◽  
pp. 686
Author(s):  
Sehwan Moon ◽  
Hyunju Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Matrix Factorization ◽  
Omics Data ◽  
Biological Interactions ◽  
Multiple Data ◽  
Age Related ◽  
Latent Features ◽  
Non Negative Matrix Factorization

High dimensional multi-omics data integration can enhance our understanding of the complex biological interactions in human diseases. However, most studies involving unsupervised integration of multi-omics data focus on linear integration methods. In this study, we propose a joint deep semi-non-negative matrix factorization (JDSNMF) model, which uses a hierarchical non-linear feature extraction approach that can capture shared latent features from the complex multi-omics data. The extracted latent features obtained from JDSNMF enabled a variety of downstream tasks, including prediction of disease and module analysis. The proposed model is applicable not only to sample-matched multiple data (e.g., multi-omics data from one cohort) but also to feature-matched multiple data (e.g., omics data from multiple cohorts), and therefore it can be flexibly applied to various cases. We demonstrate the capabilities of JDSNMF using sample-matched simulated data and feature-matched multi-omics data from Alzheimer’s disease cohorts, evaluating the feature extraction performance in the context of classification. In a test application, we identify AD- and age-related modules from the latent matrices using an explainable artificial intelligence and regression model. These results show that the JDSNMF model is effective in identifying latent features having a complex interplay of potential biological signatures.

scholarly journals Molecular subtyping of Alzheimer’s disease with consensus non-negative matrix factorization

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250278
Author(s):  
Chunlei Zheng ◽  
Rong Xu
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Matrix Factorization ◽  
Molecular Subtypes ◽  
Clinical Trial Design ◽  
Gene Expression Omnibus ◽  
Clinical Presentations ◽  
Apoe Genotypes ◽  
Non Negative Matrix Factorization

Alzheimer’s disease (AD) is a heterogeneous disease and exhibits diverse clinical presentations and disease progression. Some pathological and anatomical subtypes have been proposed. However, these subtypes provide a limited mechanistic understanding for AD. Leveraging gene expression data of 222 AD patients from The Religious Orders Study and Memory and Aging Project (ROSMAP) Study, we identified two AD molecular subtypes (synaptic type and inflammatory type) using consensus non-negative matrix factorization (NMF). Synaptic type is characterized by disrupted synaptic vesicle priming and recycling and synaptic plasticity. Inflammatory type is characterized by disrupted IL2, interferon alpha and gamma pathways. The two AD molecular subtypes were validated using independent data from Gene Expression Omnibus. We further demonstrated that the two molecular subtypes are associated with APOE genotypes, with synaptic type more prevalent in AD patients with E3E4 genotype and inflammatory type more prevalent in AD patients with E3E3 genotype (p = 0.031). In addition, two molecular subtypes are differentially represented in male and female AD, with synaptic type more prevalent in male and inflammatory type in female patients (p = 0.051). Identification of AD molecular subtypes has potential in facilitating disease mechanism understanding, clinical trial design, drug discovery, and precision medicine for AD.

Discourse characteristics of patients with mild Alzheimer's disease: Analysis of a picture description task and a procedural explanation task.

2001 ◽  
Vol 21 (2) ◽  
pp. 152-161
Author(s):  
Rumi Honda ◽  
Harumi Matuura ◽  
Yoko Takatuki ◽  
Toshiko S. Watamori ◽  
Noriko Kamakura
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Picture Description ◽  
Description Task ◽  
Disease Analysis

Deep learning based neuronal soma detection and counting for Alzheimer's disease analysis

2021 ◽  
Vol 203 ◽  
pp. 106023
Author(s):  
Qiufu Li ◽  
Yu Zhang ◽  
Hanbang Liang ◽  
Hui Gong ◽  
Liang Jiang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Neuronal Soma ◽  
Detection And Counting ◽  
Disease Analysis

scholarly journals Pathophysiological subtypes of Alzheimer’s disease based on cerebrospinal fluid proteomics

2020 ◽  
Author(s):  
Betty M. Tijms ◽  
Johan Gobom ◽  
Lianne Reus ◽  
Iris Jansen ◽  
Shengjun Hong ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Innate Immune ◽  
Barrier Dysfunction ◽  
Physiological Processes ◽  
Increased Risk ◽  
Biological Heterogeneity ◽  
Subtype 3 ◽  
Non Negative Matrix Factorization

AbstractAlzheimer’s disease (AD) is biologically heterogeneous, and detailed understanding of the processes involved in patients is critical for development of treatments. Cerebrospinal fluid (CSF) contains hundreds of proteins, with concentrations reflecting ongoing (patho)physiological processes. This provides the opportunity to study many biological processes at the same time in patients. We studied whether AD biological subtypes can be detected in cerebrospinal fluid (CSF) proteomics using the dual clustering technique non-negative matrix factorization. In two independent cohorts (EMIF-AD MBD and ADNI) we found that 705 (77% of 913 tested) proteins differed between AD (defined as having abnormal amyloid, n=425) and controls (defined as having normal CSF amyloid and tau and intact cognition, n=127). Using these proteins for data-driven clustering, we identified within each cohorts three robust pathophysiological AD subtypes showing 1) hyperplasticity and increased BACE1 levels; 2) innate immune activation; and 3) blood-brain barrier dysfunction with low BACE1 levels. In both cohorts, the majority of individuals was labelled as having subtype 1 (80, 36% in EMIF-AD MBD; 117, 59% in ADNI), 71 (32%) in EMIF-AD MBD and 41 (21%) in ADNI were labelled as subtype 2, 72 (32%) in EMIF-AD MBD and 39 (20%) individuals in ADNI were labelled as subtype 3. Genetic analyses showed that all subtypes had an excess of genetic risk for AD (all p>0.01). Additional pathological comparisons that were available for a subset in ADNI only further showed that subtypes showed similar severity of AD pathology, and did not differ in the frequencies of co-pathologies, providing further support that these differences truly reflect AD heterogeneity. Compared to controls all non-demented AD individuals had increased risk to show clinical progression, and compared to subtype 1, subtype 2 showed faster progression to after correcting for age, sex, level of education and tau levels (HR (95%CI) subtype 2 vs 1 = 2.5 (1.2, 5.1), p = 0.01), and subtype 3 at trend level (HR (95%CI) = 2.1 (1.0, 4.4)). Together, these results demonstrate the value of CSF proteomics to study biological heterogeneity in AD patients, and suggest that subtypes may require tailored therapy.

scholarly journals Molecular Inverse Comorbidity between Alzheimer’s disease and Lung Cancer: new insights from Matrix Factorization

2019 ◽  
Author(s):  
Alessandro Greco ◽  
Jon Sanchez Valle ◽  
Vera Pancaldi ◽  
Anaïs Baudot ◽  
Emmanuel Barillot ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Matrix Factorization ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Biological Data ◽  
Biological Data Analysis ◽  
Specific Factors ◽  
Molecular Bases

AbstractMatrix Factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology.We here challenge MF in depicting the molecular bases of epidemiologically described Disease-Disease (DD) relationships. As use case, we focus on the inverse comorbidity association between Alzheimer’s disease (AD) and lung cancer (LC), described as a lower than expected probability of developing LC in AD patients. To the day, the molecular mechanisms underlying DD relationships remain poorly explained and their better characterization might offer unprecedented clinical opportunities.To this goal, we extend our previously designed MF-based framework for the molecular characterization of DD relationships. Considering AD-LC inverse comorbidity as a case study, we highlight multiple molecular mechanisms, among which the previously identified immune system and mitochondrial metabolism. We then discriminate mechanisms specific to LC from those shared with other cancers through a pancancer analysis. Additionally, new candidate molecular players, such as Estrogen Receptor (ER), CDH1 and HDAC, are pinpointed as factors that might underlie the inverse relationship, opening the way to new investigations. Finally, some lung cancer subtype-specific factors are also detected, suggesting the existence of heterogeneity across patients also in the context of inverse comorbidity.

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