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 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.030 |
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".