Traceable Claims in Bioinformatics

 Quick Analysis Plan Copied

 Long Analysis Plan

Spatial Transcriptomics Analysis Plan

Goal: Plan a complete, reproducible pipeline for high-resolution spatial transcriptomics (ST) analysis using standardized resources, scalable domain-detection methods, multimodal integration, and biologically grounded benchmarking.

1. Data Acquisition & Preprocessing

Start with standardized, publicly accessible high-resolution ST datasets. The ten-mouse-organ Stereo-seq resource provides 23 tissue sections (brain, kidney, lung, thymus, large intestine, skin, spleen, ovary, testis, uterus) with matched histology images, cell-bin (single-cell resolution), and bin-50 expression matrices (STOmics STT0000184; Zenodo DOI 10.5281/zenodo.18463527), processed with SAW v8.1.0 and annotated via CellBin v2 + cell2location against MCA 3.0 and Tabula Muris Senis references Ten-organ Stereo-seq mouse atlas. For cross-platform comparability, SpatialBench supplies matched Visium HD, Xenium, and MERSCOPE spleen sections (27 sections total; 8 µm and 16 µm binning) with a shared 90-gene set, BANKSY dimensionality reduction, Harmony integration, RCTD annotation, and STalign cross-platform alignment Cross-platform spleen ST benchmark.

Wall-clock benchmarks from FaST show mapping+analysis of 596 M reads (OpenST mouse head, 55,615 cells, 763 mean UMI/cell) completes in ~49 min, while 1.26 B reads (metastatic lymph node, 110,724 cells, 893 UMI/cell) completes in ~41 min, demonstrating lightweight RNA-based segmentation pipelines outperform HPC alternatives FaST lightweight ST pipeline benchmarks.

2. Spatial Domain Detection at Scale

For ultra-large datasets (≥1 million cells/spots), full-graph graph neural network methods exhaust memory. STAHD uses METIS-based multilevel graph partitioning with a graph attention autoencoder (latent dim 64, 3-layer encoder, Adam lr 0.001) to achieve median ARI ~0.62 on DLPFC slice 151673, requiring only ~2.1 GB GPU memory for the 1.29 M-cell Xenium whole-mouse dataset and outperforming SEDR, STAGATE, SpaceFlow, GraphST, and PAST STAHD scalable spatial domain detection. For multimodal enhancement, HESTIA integrates dual autoencoders with hierarchical vision transformer histology features, processing 2 M-bin lung adenosquamous carcinoma on a single 24 GB GPU and revealing immunoregulatory domains (CCL19, IGLC2, MZB1) and CEACAM6-high subdomains in colorectal cancer HESTIA multimodal ST integration.

3. Spatial Gene Regulatory Network Inference

Beyond clustering, spatially varying gene networks reveal region-specific biology. The Bayesian multi-resolution spatial graphical regression (mSGR) method infers spatially varying precision matrices across hierarchical tumor regions, achieving MCC ~0.94, TPR ~0.90, and FDR ~0.01 in RCC-based simulations. Applied to a single RCC patient's CosMx data (23 FOVs, 26,298 cells, 960 genes), mSGR identified 47 edges (23 region-specific) in transitional regions and hub genes MALAT1 and VIM, with pathway connectivity (resisting cell death, tissue invasion/metastasis) increasing along the clear cell → transitional → sarcomatoid gradient mSGR spatial gene network inference. Single-patient data limit generalizability; validation in multi-patient cohorts is pending.

4. Cell–Cell Interaction & Disease Mechanism

Ultra-high-resolution ST (Seq-Scope, 0.5–0.8 µm resolution) maps spatial cell–cell interactions. In AngII-induced Apoe-/- abdominal aortic aneurysm (104,762 hexagons, 12 clusters), GPNMB-high macrophages localized near SMCs where elastic lamina was damaged; in vitro, recombinant sGPNMB (40 ng/mL) reduced SMC contractile markers (MYH11, ACTA2, TAGLN) and upregulated MMP2/MMP9/ADAM10 via CD44, with siCD44 knockdown attenuating these effects AAA Seq-Scope spatial cell interactions. Male-only model and 4-week single time point limit sex and temporal generalizability.

5. Clinical Translation Caveats

Despite technical advances, clinical translation of high-resolution ST requires standardized protocols, large healthy/disease reference atlases, cross-platform reproducibility, and prospective validation linking spatial biomarkers to patient outcomes—none of which is yet routine ST clinical translation review. Patent conflicts (Open-ST) and platform-specific biases in method papers warrant scrutiny.