Evaluating gene representation in spatial transcriptomics across pre-designed panels
Bibliographic record
Abstract
Spatial transcriptomics (ST) has revolutionized our understanding of gene expression within tissues by preserving spatial context. Over the past few years, this technology has led to a number of paradigm-shifting discoveries that have enabled a more comprehensive understanding of cellular functions and interactions in normal and diseased states. However, ST technologies still face challenges related to resolution, sensitivity, and technical variability. In this study, we evaluate the read coverage of commercialized pre-designed panels using publicly available ST datasets generated from the 10X Genomics and Nanostring platforms. We introduce the Coverage Index (CI) as a quantitative metric to assess the representation of established gene signatures across multiple ST datasets. Our findings reveal that cancer-related gene lists exhibit the highest CI values, while genes encoding for ligands and receptors tend to have low coverage. Additionally, CI analysis can help highlight intrinsic biases in gene panel design, influencing the detection capacity and thereby downstream comprehension of certain biological pathways. The insights gained from this study provide a framework for assessing ST panel performance and optimizing gene panels for future spatial transcriptomic applications.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".