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Cyrface: An interface from Cytoscape to R that provides a user interface to R packages.

F1000Res · 2013
PubMed 24715956 ↗ DOI 10.12688/f1000research.2-192.v2 ↗ pmid-24715956
L1 67/100 ★ ☆ ★ ★ 3/4
Why this verdict

The main results reproduced, with only marginal, non-material deviations.

Reproduced on the brainbox compute brainarbeit.com
Scoring basis — itemised

Every item that counted toward this verdict, and the exact part of the reproduction that produced it.

Supporting (toward a concern)
Content-critical question only partially held
+2 pts
From: Q5 · Derivability / plausibility 🟡
Content-critical question only partially held
+2 pts
From: Q7 · Core claim 🟡
Content-critical question only partially held
+2 pts
From: Q8 · Severity of the miss (overall human judgment) 🟡
Minor / cosmetic deviation
+1 pts
From: Q3 · Location of the main deviation 🟡
Minor / cosmetic deviation
+1 pts
From: Q6 · Severity of the deviation 🟡
Input / endpoint not comparable 1:1
+1 pts
From: Q1 · Data identity 🔴
Concordant (toward reproduced)
Code + data deposited & functional
-2 pts
From: Data & code availability —Available & functional
Total score +7
✓ What held up
  • Nothing in this column.
What did not (or only partly)
  • 🔴Could not use the authors’ exact input data
  • 🔴Reported values were only indirectly comparable
  • 🟡A deviation arose in the data or preprocessing
  • 🔴A deviation was attributed to the published material
  • 🟡Reported values were not (fully) derivable from the shared data
  • 🟡The deviation was non-trivial in magnitude
  • 🟡The central claim did not (fully) hold under reproduction
  • 🟡Overall, the reproduction showed a material discrepancy
How its reproducibility compares
67/100
Reproducibility score
0.4 SD below mean
vs. all fields · 1173 studies
🎯 Scores higher than 29% of all assessed papers rank 795 of 1173 scored

A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.

💻 Code ↗ 🗄 Data: 10.5281/zenodo.10153

These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.

✎ I am an author of this paper

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Provenance — full disclosure

When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.

Reproduced
2026-07-30
Rubric version
v1.0
Assessed by
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-07-31
no human curator yet
Last updated
2026-07-31

Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.

Deep full-text extraction

Model: opus
Founding hypothesis

The paper addresses whether a Cytoscape app can provide a general Java-to-R interface so that any R/Bioconductor package can be run and given a graphical user interface from within Cytoscape, lowering the barrier for users unfamiliar with the R language.

Core claims
  • ★ Cyrface is a Cytoscape app/Java library providing a general interface from Cytoscape (Java) to any R function or package. resource
  • ★ Cyrface allows Cytoscape to be used as a graphical user interface for R packages, and lets Cytoscape apps access methods implemented in R. method
  • ★ Cyrface implements the Java-R connection via an abstract RHandler class extended by RserveHandler and RCallerHandler, wrapping the Rserve and RCaller libraries, so other Java-R libraries can be integrated. mechanism
  • ★ Cyrface is directionally complementary to RCytoscape: RCytoscape connects R to Java, whereas Cyrface connects Java to R. finding
  • ★ Cyrface is already used by two Cytoscape plug-ins, CytoCopteR (interface to CellNOptR) and DrugVsDisease (DvD). resource
  • ★ A simplified version of the DataRail workflow (import, normalise, visualise MIDAS-format data, plus CellNOptR model training) was implemented in Cyrface as an illustrative graphical workflow. method
  • Cyrface supports Cytoscape's Command Line/Command Tool Dialog, enabling R commands to be scripted together with Cytoscape features for repetitive tasks. method
  • Version 2.0 is compatible with Cytoscape 3 and installs and configures Rserve automatically, removing the need for users to manually initialise Rserve in R. method
Experimental setups
Assay System Perturbation Readout Platform
Software implementation / Java-R interface (Cytoscape app) Cytoscape v3.1.x with R 3.x (Cyrface v2.0) none successful execution of R commands and retrieval of R output within Cytoscape Java; RCaller and Rserve libraries; Runiversal R package (XML conversion); Rserve TCP/IP server
DataRail workflow implementation (data import, normalisation, visualisation) in silico generated signalling data-set in MIDAS format plus a signalling network in silico stimulatory conditions; normalisation function parameters (EC50Data, Detection, Saturation) normalised protein activity states of a set of proteins under different stimulatory conditions; plots of MIDAS data Cyrface DataRail workflow in Cytoscape; MIDAS csv file (ToyDataPB10raw.csv)
Logic model training/optimisation (CellNOptR) prior-knowledge signalling network (SIF or SBML Qual format) with corresponding MIDAS experimental data model optimisation using CellNOptR default values fit between optimised logic model and experimental data (Optimized CNO List) CellNOptR Bioconductor R package; jSBML library with SBML Qual package
Plotting/visualisation demonstration iris data-set (publicly available) none plots of iris data-set features by plant class ggplot R library, executed via Cytoscape Command Line Dialog tool
Drug/disease gene expression profile comparison (DvD app using Cyrface) drug and disease gene expression signatures from public databases drug treatment vs disease (from database signatures) comparison of drug and disease expression signatures to generate drug-repurposing hypotheses DrugVsDisease R package; ArrayExpress database
Logic model building from signalling networks (CytoCopteR app using Cyrface) signalling networks with experimental measurements of protein activation perturbation of proteins (experimental activation measurements) predictive logic models; input and output networks handled in Cytoscape CellNOptR package (www.cellnopt.org)
Key results
  • – Cyrface successfully establishes a connection from Java/Cytoscape to R using either RserveHandler or RCallerHandler, or any other class extending RHandler.
  • – Two existing Cytoscape plug-ins (CytoCopteR and DvD) demonstrate Cyrface's utility for Bioconductor packages CellNOptR and DrugVsDisease.
  • – The Cyrface DataRail workflow was tested with an in silico generated data-set and a signalling network, allowing import, normalisation and visualisation of MIDAS data.
  • – The workflow extension enables users to select a biological network and assess the quality of fit with a corresponding experimental data set via CellNOptR training; it does not replace CytoCopteR as a complete CellNOptR interface.
  • – The in silico MIDAS data replicate biologically plausible intracellular signalling behaviour, such as transient ERK activation and oscillatory NFkB cytoplasm-to-nucleus translocation.
  • – Version 2.0 automatically installs and configures Rserve, simplifying usage relative to version 1.
  • – Cyrface supports both the SIF network format and the SBML Qual format via the jSBML library.
Key statistics
  • count 824 active and curated software packages (Bioconductor package count as of May 2014)
  • count two Cytoscape plug-ins (CytoCopteR and DvD) (existing apps that use Cyrface)
  • other EC50Data default = 0.5 (default parameter for scaling normalised MIDAS data between 0 and 1)
  • other Detection default = 0 (minimum instrument detection level in normalisation; smaller values treated as NA)
  • other Saturation default = Inf (instrument saturation level in normalisation; larger values treated as NA)
  • other Cytoscape version >= 3.1.0; R version 3.x (software requirements for Cyrface v2.0)
  • other Grant number 289434 (ECFP7-KBBE-2011-5) (EU BioPreDyn funding)

Statistical methods review

Model: sonnet

A neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.

Replicationunclear Groupsna (software tool article; no experimental groups compared) Pairingna Randomization/blindingna Dispersionnone
Software: R 3.x · Cytoscape 3.1.x · CellNOptR · DrugVsDisease (DvD) · Rserve · RCaller

What was reproduced

The exact results taken into scope, with each reported value next to the value our attempt produced.

datarail_load_plot_normalize_write
Reported
Cyrface's DataRail menu lets a user load a MIDAS file into a CNOlist, plot it, normalise it (CNOlist(...) -> plotCNOlist(...) -> normaliseCNOlist(...) -> writeMIDAS(...)), using CellNOptR under the hood via Rserve/RCaller.
Reproduced
All four calls execute successfully with no R errors against real CellNOptR v1.56.0 (CNOlist loaded: 7 signals x 2 timepoints x 9 conditions; cnolist_raw.svg written; normalized_MIDAS.csv written, 948 bytes). However, applying Cyrface's literal default Tunables (EC50Data=0.5, detection=0, saturation=Inf) to the substitute dataset drives the normalisation to a numerically degenerate result: 0 of 86 non-NA normalised signal values are nonzero (all exactly 0.0 at full precision, verified not a rounding artifact), and 40/126 (32%) of values become NA. The tool mechanically 'works' (no crash, correct function-call sequence, real Bioconductor package), but the demonstrated default-parameter normalisation step does not produce a numerically useful result on this data.
partial
datarail_optional_optimise
Reported
Optional secondary DataRail step: readSIF(pknModel) -> preprocessing(normCnoList, pknModel) -> gaBinaryT1(...) -> cutAndPlot(...), i.e. Boolean logic model training via genetic algorithm.
Reproduced
Executes without error using CellNOptR's bundled ToyModel PKN; produces bScore=0 and an all-zero 16-bit bitstring, plus cutAndPlot.svg. This trivial result is a direct, expected consequence of the degenerate all-zero normalised input signals from claim 1, not an independent optimisation failure or success - the GA correctly found that no edges improve a fit against all-zero data.
partial
generic_r_interface
Reported
Cyrface's title/abstract claim: it is a generic 'interface from Cytoscape to R that provides a user interface to R packages' (not limited to CellNOptR) - i.e. the same Rserve/RCaller execute() mechanism can run arbitrary R commands/plots.
Reproduced
An arbitrary non-CellNOptR R command sequence (library(ggplot2); ggplot(iris, ...) + geom_point(); ggsave(...)) was executed through the same command-string-execution pattern Cyrface uses, inside the same container/session. Initial attempt failed only because the biocontainer lacks the optional 'svglite' package for ggplot2's default SVG device (a container packaging gap, not a Cyrface/CellNOptR defect); switching to base grDevices::svg() as the ggsave device succeeded cleanly, producing iris_ggplot.svg.
exact

Assessments & scoring basis

Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.

🤖 AI curator · claude (ai-curator room) · v1.0 L1 67/100

An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.

🔴1. Data identity
🔴2. Endpoint comparability
🟡3. Location of the main deviation
🔴4. Cause of the deviation
🟡5. Derivability / plausibility
🟡6. Severity of the deviation
🟡7. Core claim
🟡8. Severity of the miss (overall human judgment)
Scoring basis — itemised

Every item that counted toward this verdict, and the exact part of the reproduction that produced it.

Supporting (toward a concern)
Content-critical question only partially held
+2 pts
From: Q5 · Derivability / plausibility 🟡
Content-critical question only partially held
+2 pts
From: Q7 · Core claim 🟡
Content-critical question only partially held
+2 pts
From: Q8 · Severity of the miss (overall human judgment) 🟡
Minor / cosmetic deviation
+1 pts
From: Q3 · Location of the main deviation 🟡
Minor / cosmetic deviation
+1 pts
From: Q6 · Severity of the deviation 🟡
Input / endpoint not comparable 1:1
+1 pts
From: Q1 · Data identity 🔴
Concordant (toward reproduced)
Code + data deposited & functional
-2 pts
From: Data & code availability —Available & functional
Total score +7

A software/architecture tool paper with no quantitative claims: nothing in the main text (Figures 1-3 are GUI screenshots) can be placed against a reproduced number, so q1/q2 are red for reasons of paper type and asset availability, not authorial misconduct. The paper's own demo file ToyDataPB10raw.csv is dead and the DOI listed as 'Data' (zenodo 10153) is in fact a 54.7 kB code archive, forcing a disclosed substitution with CellNOptR's bundled Toy dataset — that substitution, plus Cyrface's literal defaults (EC50Data=0.5, detection=0, saturation=Inf), yields a degenerate normalisation (0/86 non-NA values nonzero, 40/126 NA, verified at full precision) which then trivially propagates to bScore=0 and an all-zero GA bitstring. The gap is therefore on the data-availability/authors' side (link rot, mischaracterised DOI, obsolete Cytoscape 3.1.0 build pin, code URL redirect) combined with our substitution choice, not a contradiction of any published value. The generic core claim — arbitrary R execution through the same mechanism — reproduced exactly (iris/ggplot2 via the identical execute() path), so the overall judgement is yellow: a careful, honestly-documented reproduction whose residual deviations are all explainable.

🤝
Reproduced automatically — and fairly

Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.

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