10x Visium Spatial Transcriptomics Dataset: Kidney (3) and Lung (5) Cancer with Tertiary Lymphoid Structures
Bibliographic record
Abstract
The kidney and lung cancer dataset with tertiary lymphoid structures (TLS) consists of 5 μm thick FFPE sections from kidney (3) and lung (5) tumors obtained from the Institute of Pathology at the University Hospital of Zurich, mounted onto Visium slides with the Human Probe Set v1. The samples were stained with hematoxylin and eosin (H&E) and subsequently processed for sequencing following the manufacturer's recommendations. After library preparation, the samples were sequenced on an Illumina NovaSeq 6000 and preprocessed using Space Ranger v2.1.0. Patient analyses were conducted in accordance with the Declaration of Helsinki. Ethical approval for performing research on anonymized, archival patient material was obtained from the Cantonal Ethics Commission of Zurich (BASEC Nr. 2022-01854 and BASEC Nr. 2024-01428). Spatial transcriptomics sequencing was performed at the Functional Genomics Center Zurich (FGCZ) of University of Zurich and ETH Zurich. The Visium spots were annotated in the corresponding H&E images by expert researchers (K.S. and S.D.) and included manual annotations with the following labels: TLS, Immune, Tumor, Normal, and Unassigned. Data samples include KC[1-3] indicating kidney cancer and LC[1-5] indicating lung cancer. K.N performed the manual fiducial alignment and tissue detection on the high-resolution images using Loupe Browser 8.0.0 and preprocesseed the AnnData objects. Data summary: 1) 10x_Visium contains the Space Ranger v2.1.0 output (raw matrix, filtered matrix and spatial information including scaled images). 2) h5ad contains preprocessed AnnData objects along with the manual annotations. 3) tif_slides contains the high-resolution images along with aligned spot coordinates. Folder structure: ├── 10x_Visium │ ├── KC1 │ │ ├── filtered_feature_bc_matrix │ │ ├── raw_feature_bc_matrix │ │ └── spatial │ ├── KC2 │ │ ├── filtered_feature_bc_matrix │ │ ├── raw_feature_bc_matrix │ │ └── spatial │ ├── KC3 │ │ ├── filtered_feature_bc_matrix │ │ ├── raw_feature_bc_matrix │ │ └── spatial │ ├── LC1 │ │ ├── filtered_feature_bc_matrix │ │ ├── raw_feature_bc_matrix │ │ └── spatial │ ├── LC2 │ │ ├── filtered_feature_bc_matrix │ │ ├── raw_feature_bc_matrix │ │ └── spatial │ ├── LC3 │ │ ├── filtered_feature_bc_matrix │ │ ├── raw_feature_bc_matrix │ │ └── spatial │ ├── LC4 │ │ ├── filtered_feature_bc_matrix │ │ ├── raw_feature_bc_matrix │ │ └── spatial │ └── LC5 │ ├── filtered_feature_bc_matrix │ ├── raw_feature_bc_matrix │ └── spatial ├── h5ad_preprocessed └── tif_slides
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".