Online bias-aware disease module mining with ROBUST-Web

Sarkar, Suryadipto; Lucchetta, Marta; Maier, Andreas; Abdrabbou, Mohamed M; Baumbach, Jan; List, Markus; Schaefer, Martin H.; Blumenthal, David B.

doi:10.1093/bioinformatics/btad345

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Their interpretability is a decisive advantage over predictions provided by deep learning models, which are often perceived as black boxes 21 . And the fact that SHouT is deterministic ensures that it consistently computes the same results when run several times on equivalent input -unlike many other methods in data-centric biomedicine 22,23 including Squidpy's nhood_enrichment function. Together, these three properties make the SHouT scores ideal ingredients for potential future biomarkers based on spatial omics data, both in the CTCL use case presented here and beyond.…”

Section: Discussionmentioning

confidence: 99%

Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma

Sarkar,

Möller,

Hartebrodt

et al. 2024

Preprint

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Cutaneous T cell lymphomas (CTCLs) are non-Hodgkin lymphomas caused by malignant T cells which migrate to the skin. The cancerous T cells lead to rash-like lesions which can be difficult to distinguish from inflammatory skin conditions like atopic dermatitis (AD) and psoriasis (PSO). To characterize CTCL in comparison to these differential diagnoses, we carried out multi-antigen imaging on 69 skin tissue samples (21 CTCL, 23 AD, 25 PSO). The resulting spatially resolved protein abundance maps were then analyzed via scoring functions to quantify heterogeneity of the individual cells' neighborhoods within spatial graphs inferred from the cells' positions in the tissue samples (available as a Python package at https://github.com/bionetslab/SHouT). Our analyses reveal several characteristic patterns of skin tissue organization in CTCL, including a combination of increased local entropy and egophily as characteristic properties of spatial T cell neighborhoods in CTCL as compared to AD and PSO. These results could not only pave the way for high-precision diagnosis of CTCL, but may also facilitate further insights into cellular disease mechanisms.

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Section: Discussionmentioning

confidence: 99%

Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma

Sarkar,

Möller,

Hartebrodt

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma

Sarkar,

Möller,

Hartebrodt

et al. 2024

npj Syst Biol Appl

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Cutaneous T-cell lymphomas (CTCLs) are non-Hodgkin lymphomas caused by malignant T cells which migrate to the skin and lead to rash-like lesions which can be difficult to distinguish from inflammatory skin conditions like atopic dermatitis (AD) and psoriasis (PSO). To characterize CTCL in comparison to these differential diagnoses, we carried out multi-antigen imaging on 69 skin tissue samples (21 CTCL, 23 AD, 25 PSO). The resulting protein abundance maps were then analyzed via scoring functions to quantify the heterogeneity of the individual cells’ neighborhoods within spatial graphs inferred from the cells’ positions in the tissue samples. Our analyses reveal characteristic patterns of skin tissue organization in CTCL as compared to AD and PSO, including a combination of increased local entropy and egophily in T-cell neighborhoods. These results could not only pave the way for high-precision diagnosis of CTCL, but may also facilitate further insights into cellular disease mechanisms.

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Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer

Ketteler,

Blumenthal

2023

Briefings in Bioinformatics

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Gene regulatory networks (GRNs) and gene co-expression networks (GCNs) allow genome-wide exploration of molecular regulation patterns in health and disease. The standard approach for obtaining GRNs and GCNs is to infer them from gene expression data, using computational network inference methods. However, since network inference methods are usually applied on aggregate data, distortion of the networks by demographic confounders might remain undetected, especially because gene expression patterns are known to vary between different demographic groups. In this paper, we present a computational framework to systematically evaluate the influence of demographic confounders on network inference from gene expression data. Our framework compares similarities between networks inferred for different demographic groups with similarity distributions obtained for random splits of the expression data. Moreover, it allows to quantify to which extent demographic groups are represented by networks inferred from the aggregate data in a confounder-agnostic way. We apply our framework to test four widely used GRN and GCN inference methods as to their robustness w. r. t. confounding by age, ethnicity and sex in cancer. Our findings based on more than $ {44000}$ inferred networks indicate that age and sex confounders play an important role in network inference for certain cancer types, emphasizing the importance of incorporating an assessment of the effect of demographic confounders into network inference workflows. Our framework is available as a Python package on GitHub: https://github.com/bionetslab/grn-confounders.

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Online bias-aware disease module mining with ROBUST-Web

Cited by 3 publications

References 30 publications

Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma

Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma

Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma

Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer

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