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About FineST

FineST (Fine -grained S patial T ranscriptomic) is a computational method to identify super-resolved spatial co-expression (i.e., spatial association) between a pair of ligand and receptor.

Uniquely, FineST can distinguish co-expressed ligand-receptor pairs (LR pairs) from spatially separating pairs at sub-spot level or single-cell level, and identify the super-resolved LR interaction.

Note

This project is under active development.

Tutorial

Please refer to our tutorials for details:

Spot interpolation for Visium datasets.

• Interpolate between-spots among within-spots by FineST (For Visium dataset).

Step1 and Step2 Train FineST and impute super-resolved spatial RNA-seq.

• FineST on Visium HD for super-resolved gene expression prediction (from 16um to 8um).

• FineST on Visium for super-resolved gene expression prediction (sub-spot or single-cell).

• FineST on Visium for enhencing gene expression signal (for HCCP1T dataset).

Step3 Fine-grained LR pair and CCC pattern discovery.

• Nuclei-resolved ligand-receptor interaction discovery by FineST (For Visium dataset).

• Super-resolved ligand-receptor interaction discovery by FineST (For Visium HD dataset).

Downstream analysis Cell type deconvolution, ROI region cropping, cell-cell colocalization.

• Nuclei-resolved cell type deconvolution of Visium (use FineST-imputed data).

• Super-resolved cell type deconvolution of Visium HD (For FineST-imputed data).

• Crop region of interest (ROI) from HE image by FineST (Visium or Visium HD).

Performance evaluation of FineST vs (TESLA and iSTAR).

• PCC-SSIM-CelltypeProportion-RunTimes comparison in FineST manuscript.

Inference comparison of FineST vs iStar (only LR genes).

• FineST on demo data.

• iStar on demo data.

References

FineST has been published on Nature Communications. If you are interested in FineST, please contact Dr. Lingyu Li (lingyuli@hku.hk) or Dr. Yuanhua Huang (yuanhua@hku.hk).