Browsing by Subject "Integrative analysis"

Now showing 1 - 4 of 4
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    Geographical Landscape and Transmission Dynamics of SARS-CoV-2 Variants Across India: A Longitudinal Perspective
    (Frontiers Media, 2021-12-17) Jha, Neha; Hall, Dwight; Kanakan, Akshay; Mehta, Priyanka; Maurya, Ranjeet; Mir, Quoseena; Gill, Hunter Mathias; Janga, Sarath Chandra; Pandey, Rajesh; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Globally, SARS-CoV-2 has moved from one tide to another with ebbs in between. Genomic surveillance has greatly aided the detection and tracking of the virus and the identification of the variants of concern (VOC). The knowledge and understanding from genomic surveillance is important for a populous country like India for public health and healthcare officials for advance planning. An integrative analysis of the publicly available datasets in GISAID from India reveals the differential distribution of clades, lineages, gender, and age over a year (Apr 2020–Mar 2021). The significant insights include the early evidence towards B.1.617 and B.1.1.7 lineages in the specific states of India. Pan-India longitudinal data highlighted that B.1.36* was the predominant clade in India until January–February 2021 after which it has gradually been replaced by the B.1.617.1 lineage, from December 2020 onward. Regional analysis of the spread of SARS-CoV-2 indicated that B.1.617.3 was first seen in India in the month of October in the state of Maharashtra, while the now most prevalent strain B.1.617.2 was first seen in Bihar and subsequently spread to the states of Maharashtra, Gujarat, and West Bengal. To enable a real time understanding of the transmission and evolution of the SARS-CoV-2 genomes, we built a transmission map available on https://covid19-indiana.soic.iupui.edu/India/EmergingLineages/April2020/to/March2021. Based on our analysis, the rate estimate for divergence in our dataset was 9.48 e-4 substitutions per site/year for SARS-CoV-2. This would enable pandemic preparedness with the addition of future sequencing data from India available in the public repositories for tracking and monitoring the VOCs and variants of interest (VOI). This would help aid decision making from the public health perspective.
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    Integrative Analysis for Identifying Multi-Layer Modules in Precision Medicine
    (2020-12) Yazdanparast, Aida; Wu, Huanmei; Li, Lang; Liu, Xiaowen; Liu, Yunlong; Zhang, Chi
    Precision medicine aims to employ information from all modalities to develop a comprehensive view of disease progression and administer therapies tailored to the individual patient. A set of genomic features (gene CNVs, mutations, mRNA expressions, and protein abundances) is associated with each patient and it is hard to explain the phenotypic similarities such as gene essentiality or variability in drug response in a single genomic level. Thus, to extract biological principles it is critical to seek mutual information from multi-dimensional datasets. To address these concerns, we first conduct an integrated mRNA/protein analysis in both breast cancer cell lines and tumors, and most interestingly in the breast cancer subtypes. We identified cell lines that provide optimum heterogeneity models for studying the underlying biological processes of tumors. Our systematic observation across multi-omics data identifies distinct subgroups of cancer cells and patients. Based on this identified signal transduction between mRNA and RPPA, we developed a biclustering model to characterize key genetic alterations that are shared in both cancer cell lines and patients. We integrated two types of omics data including copy number variations, transcriptome, and proteome. Bi-EB adopts a data-driven statistics strategy by using Expected-Maximum (EM) algorithm to extract the foreground bicluster pattern from its background noise data in an iterative search. Using Bi-EB algorithm we selected translational gene sets that are characterized by highly correlated molecular profiles among RNA and proteins. To further investigate cell line and tissue in breast cancer we explore the relationship vii between genomic features and the phenotypic factors. Using in vitro/in vivo drug screening data, we adopt partial least square regression method and develop a multi-modular approach to predict anticancer therapy benefits for ER-negative breast cancer patients. The identified joint multi-dimensional modules here provide us new insights into the molecular mechanisms of drugs and cancer treatment.
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    Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer
    (BMC, 2020-12-28) Xu, Siwen; Lu, Zixiao; Shao, Wei; Yu, Christina Y.; Reiter, Jill L.; Feng, Qianjin; Feng, Weixing; Huang, Kun; Liu, Yunlong; Medicine, School of Medicine
    Background: Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer. Methods: An established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues. We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. By integrating ATAC-seq data with matched RNA-seq data, we found the potential target genes that associated with these regulatory regions. Then we used these genes to perform the following pathway and survival analysis. Results: Using canonical correlation analysis, we detected 436 potential regulatory regions that exhibited significant correlation between quantitative chromatin accessibility changes and the epithelial tissue proportion in tumors from 54 patients (FDR < 0.05). We then found that these 436 regulatory regions were associated with 74 potential target genes. After functional enrichment analysis, we observed that these potential target genes were enriched in cancer-associated pathways. We further demonstrated that using the gene expression signals and the epithelial tissue proportion extracted from this integration framework could stratify patient prognoses more accurately, outperforming predictions based on only omics or image features. Conclusion: This integrative analysis is a useful strategy for identifying potential regulatory regions in the human genome that are associated with tumor tissue quantification. This study will enable efficient prioritization of genomic regulatory regions identified by ATAC-seq data for further studies to validate their causal regulatory function. Ultimately, identifying epithelial tissue proportion-associated regulatory regions will further our understanding of the underlying molecular mechanisms of disease and inform the development of potential therapeutic targets.
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    Spatial Transcriptomic Analysis Reveals Associations between Genes and Cellular Topology in Breast and Prostate Cancers
    (MDPI, 2022-10-04) Alsaleh, Lujain; Li, Chen; Couetil, Justin L.; Ye, Ze; Huang, Kun; Zhang, Jie; Chen, Chao; Johnson, Travis S.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Background: Cancer is the leading cause of death worldwide with breast and prostate cancer the most common among women and men, respectively. Gene expression and image features are independently prognostic of patient survival; but until the advent of spatial transcriptomics (ST), it was not possible to determine how gene expression of cells was tied to their spatial relationships (i.e., topology). Methods: We identify topology-associated genes (TAGs) that correlate with 700 image topological features (ITFs) in breast and prostate cancer ST samples. Genes and image topological features are independently clustered and correlated with each other. Themes among genes correlated with ITFs are investigated by functional enrichment analysis. Results: Overall, topology-associated genes (TAG) corresponding to extracellular matrix (ECM) and Collagen Type I Trimer gene ontology terms are common to both prostate and breast cancer. In breast cancer specifically, we identify the ZAG-PIP Complex as a TAG. In prostate cancer, we identify distinct TAGs that are enriched for GI dysmotility and the IgA immunoglobulin complex. We identified TAGs in every ST slide regardless of cancer type. Conclusions: These TAGs are enriched for ontology terms, illustrating the biological relevance to our image topology features and their potential utility in diagnostic and prognostic models.