<i>EnrichIntersect</i>: an R package for custom set enrichment analysis and interactive visualization of intersecting sets

Zhao, Zhi; Zucknick, Manuela; Aittokallio, Tero

doi:10.1093/bioadv/vbac073

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…The areas corresponding to each level of the ecological risk index were calculated, and the resulting information was used to complete the visualization of the spatial distribution map of landscape ecological risk. Subsequently, the "networkD3" package in R software was used to generate an interactive Sankey diagram visualizing the landscape ecological risk levels [56].…”

Section: Calculation Of Landscape Ecological Risk Indexmentioning

confidence: 99%

Landscape Ecological Risk Assessment of the Chaohu Lake Region Based on Dynamic Evolution of Landscape Patterns

Wang,

Niu,

Zhong

et al. 2024

Pol. J. Environ. Stud.

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In order to maintain the regional landscape ecological security, satellite remote sensing image data in 2011, 2016 and 2021 were utilized to analyze the landscape ecological risk in the Chaohu Lake Region. The ecological restoration project yielded initial results, as construction land area decreased in the later period. Waters emerged as the dominant land type, while grassland area increased over the 10-year period. Conversely, the area of cultivated land, forestland, and waters decreased, with the most significant decrease observed in cultivated land. The dynamic change in landscape patterns exhibited increasing complexity. Waters dominated the landscape, and the patch density (PD) and landscape shape index (LSI) of cultivated land, forestland, waters, and grassland tended to increase. The controlling role of dominant landscape types decreased, while landscape heterogeneity increased. The Shannon's diversity index (SHDI) and evenness index (SHEI) values increased, indicating diversification and equalization in the compositional structure of landscape types. The landscape division index (DIVISION) initially increased and then decreased, whereas the landscape aggregation degree showed an initial decrease followed by an increase. The contagion index (CONTAG) decreased continuously, indicating a decrease in the aggregation density of different land type patches. The highest-risk area increased, while higherrisk and medium-risk areas initially increased and then decreased. The lower-risk area exhibited less change, and the lowest-risk area showed a decreasing trend followed by an increasing trend. The spatial distribution pattern of the landscape ecological risk index displayed a "low on all sides and high in the middle" pattern. These research findings provide scientific references for optimizing land use structure, achieving sustainable landscape management, and maintaining regional ecological balance.

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Section: Calculation Of Landscape Ecological Risk Indexmentioning

confidence: 99%

Landscape Ecological Risk Assessment of the Chaohu Lake Region Based on Dynamic Evolution of Landscape Patterns

Wang,

Niu,

Zhong

et al. 2024

Pol. J. Environ. Stud.

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Proteomic analysis of non‐muscle invasive and muscle invasive bladder cancer highlights distinct subgroups with metabolic, matrisomal, and immune hallmarks and emphasizes importance of the stromal compartment

Dinh,

Rogg,

Cosenza‐Contreras

et al. 2024

The Journal of Pathology

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We present the proteomic profiling of 79 bladder cancers, including treatment‐naïve non‐muscle‐invasive bladder cancer (NMIBC, n = 17), muscle‐invasive bladder cancer (MIBC, n = 51), and neoadjuvant‐treated MIBC (n = 11). Proteins were extracted from formalin‐fixed, paraffin‐embedded samples and analyzed using data‐independent acquisition, yielding >8,000 quantified proteins. MIBC, compared to NMIBC, shows an extracellular matrix (ECM) and immune response signature as well as alteration of the metabolic proteome together with concomitant depletion of proteins involved in cell–cell adhesion and lipid metabolism. Neoadjuvant treatment did not consistently impact the proteome of the residual tumor mass. NMIBC presents two proteomic subgroups that correlate with histological grade and feature signatures of cell adhesion or lipid/DNA metabolism. Treatment‐naïve MIBC presents three proteomic subgroups with resemblance to the basal‐squamous, stroma‐rich, or luminal subtypes and signatures of metabolism, immune functionality, or ECM. The metabolic subgroup presents an immune‐depleted microenvironment, whereas the ECM and immune subgroups are enriched for markers of M2‐like tumor‐associated macrophages and dendritic cells. Markers for natural killer cells are exclusive for the ECM subgroup, and markers for cytotoxic T cells are a hallmark of the immune subgroup. Endogenous proteolysis is increased in MIBC alongside upregulation of matrix metalloproteases, including MMP‐14. Genomic panel sequencing yielded the prototypical profile of prevalent FGRF3 alterations in NMIBC and TP53 alterations in MIBC. Tumor–stroma interactions of MIBC were investigated by proteomic analysis of patient‐derived xenografts, highlighting specific tumor and stroma contributions to the matrisome and tumor‐induced stromal proteome phenotypes. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments

Fischer,

Claussen,

Kourtis

et al. 2024

Preprint

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Macromolecular protein complexes carry out most functions in the cell including essential functions required for cell survival. Unfortunately, we lack the subunit composition for all human protein complexes. To address this gap we integrated >25,000 mass spectrometry experiments using a machine learning approach to identify > 15,000 human protein complexes. We show our map of protein complexes is highly accurate and more comprehensive than previous maps, placing ~75% of human proteins into their physical contexts. We globally characterize our complexes using protein co-variation data (ProteomeHD.2) and identify co-varying complexes suggesting common functional associations. Our map also generates testable functional hypotheses for 472 uncharacterized proteins which we support using AlphaFold modeling. Additionally, we use AlphaFold modeling to identify 511 mutually exclusive protein pairs in hu.MAP3.0 complexes suggesting complexes serve different functional roles depending on their subunit composition. We identify expression as the primary way cells and organisms relieve the conflict of mutually exclusive subunits. Finally, we import our complexes to EMBL-EBI's Complex Portal (https://www.ebi.ac.uk/complexportal/home) as well as provide complexes through our hu.MAP3.0 web interface (https://humap3.proteincomplexes.org/). We expect our resource to be highly impactful to the broader research community.

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EnrichIntersect: an R package for custom set enrichment analysis and interactive visualization of intersecting sets

Cited by 4 publications

References 8 publications

Landscape Ecological Risk Assessment of the Chaohu Lake Region Based on Dynamic Evolution of Landscape Patterns

Landscape Ecological Risk Assessment of the Chaohu Lake Region Based on Dynamic Evolution of Landscape Patterns

Proteomic analysis of non‐muscle invasive and muscle invasive bladder cancer highlights distinct subgroups with metabolic, matrisomal, and immune hallmarks and emphasizes importance of the stromal compartment

hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments

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