Table 1_Integrating single-cell and spatial transcriptomics reveals glycolysis heterogenei

Fengming Yang's dataset, published on figshare in April 2026, contains results from an integrated analysis of colorectal cancer. The 11.7 KB XLSX file likely presents findings on glycolysis heterogeneity, malignant subtypes, and the role of the NEK6 gene, derived from single-cell RNA sequencing, spatial transcriptomics, and Mendelian randomization.

Use Cases

• Identify high-glycolytic malignant cell subtypes based on AUCell and NMF analysis mentioned in the description.
• Analyze cell-cell communication networks, such as MIF signaling, using inferred interaction data.
• Investigate the spatial distribution of glycolysis subtypes within tumor domains via spatial deconvolution results.
• Validate candidate genes like NEK6 for their association with CRC risk and immunosuppressive microenvironment features.

Strengths

• Data integrates multiple advanced omics techniques: single-cell RNA-seq, spatial transcriptomics, and Mendelian randomization.
• Includes results from functional validation assays, such as NEK6 knockdown effects on cell proliferation and glycolysis.
• Released under a permissive CC-BY-4.0 license, allowing for broad reuse and adaptation.

Limitations

• Row count and column-level documentation are unknown, requiring manual inspection after download.
• The dataset is very small at 11.7 KB, indicating it likely contains summary or processed results rather than raw sequencing data.
• Description metadata is limited; actual data structure and field semantics must be inferred from the XLSX file.

Provenance

Source

figshare, author Fengming Yang.

Collection Method

Integrated analysis using scRNA-seq, spatial transcriptomics, Mendelian randomization, and in vitro functional assays.

Time Range

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Freshness

Last updated 2026-04-28 05:28:15.

Geography

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