scClustViz – Single-cell RNAseq cluster assessment and visualization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it