Data Sheet 3_Integrative multi-omics and machine learning reveals the spatial niche distri

Data Sheet 3 contains integrative multi-omics and machine learning analysis results from a study on non-small cell lung cancer. The 8.9 MB PDF was authored by Qingsheng Liu and last updated on March 18, 2026. The study integrates single-cell and spatial transcriptomic data to investigate spatial niches and the role of CYP27A1+TAMs in immunotherapy response.

Use Cases

• Train prognostic models based on spatial niche composition and cell type features mentioned in the description.
• Analyze the role of CYP27A1+TAMs in recruiting CD8+ T cells based on antigen presentation and chemokine secretion data.
• Investigate the activation of the LXR pathway in macrophages following immune checkpoint blockade treatment.
• Validate spatial niche distributions across different tumor regions and patient response groups.

Strengths

• Data integrates single-cell and spatial transcriptomic data from the same patients.
• Analysis identified nine distinct spatial niches and a specific macrophage subtype (CYP27A1+TAMs).
• Findings were validated through external cohorts, immunohistochemistry, immunofluorescence, and in vivo experiments.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is a PDF (8.9 MB), which may require extraction of underlying data for computational analysis.

Provenance

Source

figshare, authored by Qingsheng Liu.

Collection Method

Integrated single-cell and spatial transcriptomic data using ISCHIA, validated with external cohorts and in vivo experiments.

Time Range

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Freshness

Last updated 2026-03-18 07:41:32; freshness should be verified.

Geography

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