Quantification and analysis of multiplexed fluorescence in situ hybridization data using open-source tools
Bibliographic record
Abstract
Here, we present a protocol to quantify and analyze multiplexed fluorescence in situ hybridization (mFISH) data using two open-source tools, FijiFISH and RUHi. FijiFISH, an ImageJ-based plugin, enables image registration, cell segmentation, and gene expression quantification. RUHi, an R-based package, supports dimensionality reduction, clustering, and visualization through both code and a Shiny app. The protocol also accommodates experimentally induced exogenous fluorophores, providing multimodal, single-cell resolution insights into spatial gene expression. For complete details on the use and execution of this protocol, please refer to Sullivan et al. 1 • Steps for mFISH image registration, segmentation, and quantification with FijiFISH • Instructions for dimensionality reduction and clustering using RUHi and Shiny app • Guidance for visualization and interpretation of mFISH data Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Here, we present a protocol to quantify and analyze multiplexed fluorescence in situ hybridization (mFISH) data using two open-source tools, FijiFISH and RUHi. FijiFISH, an ImageJ-based plugin, enables image registration, cell segmentation, and gene expression quantification. RUHi, an R-based package, supports dimensionality reduction, clustering, and visualization through both code and a Shiny app. The protocol also accommodates experimentally induced exogenous fluorophores, providing multimodal, single-cell resolution insights into spatial gene expression.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".