Transform lengthy academic papers into concise, structured 250-word abstracts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when performing time-dependent ROC curve analysis for survival data with follow-up time, event status, and a numeric marker. Supports CSV/TXT/TSV/Excel input, `risk_score` as the default marker unless `--marker_col` is provided, parameter validation, standardized output directories, AUC table export, ROC point export, and PDF figure generation.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this skill to compute and plot time-dependent ROC curves for survival outcomes.
futime and fustat columns.risk_score, or you will provide another marker explicitly with --marker_col.Rscript scripts/main.R \
--data_file <input_file> \
--times <comma_separated_times> \
--output_dir <output_dir>
Rscript is available in the shell.optparse, timeROC, survival, ggplot2.openxlsx.Rscript -e 'install.packages(c("optparse", "timeROC", "survival", "ggplot2", "openxlsx"), repos="https://cloud.r-project.org")'.| Argument | Required | Description |
|---|---|---|
--data_file | Yes | Input data file in CSV, TXT, TSV, TAB, XLS, or XLSX format |
--times | No | Prediction time points, comma-separated. Default 1,3,5 |
--marker_col | No | Marker column name. Default risk_score; provide this option when your file uses a different marker |
--output_dir | No | Output directory, default ./TimeROC_Results |
--time_unit | No | Time unit label for plot/output: year, month, or day. Default year |
--auto_convert_days | No | If TRUE, convert large futime values from days to the selected unit when appropriate. Default TRUE |
--weighting | No | IPCW weighting method: aalen, marginal, or cox. Default aalen |
--cause | No | Event code treated as the outcome of interest. Default 1 |
futime and fustat columns.--marker_col is omitted, the input file must contain risk_score.futime must be numeric or coercible to numeric.fustat must be numeric or coercible to numeric.futime, fustat, or marker values are excluded.cause are required.Example input:
id fustat futime risk_score risk_group GPR161 RIBC2
TCGA-C5-A1M5 1 5.6219 -1.1070 low 2.8252 5.3532
TCGA-EA-A5O9 0 2.1589 -0.8964 low 2.6046 4.4293
TCGA-C5-A3HL 0 1.7014 -0.9044 low 4.1248 4.9166
futime, fustat, and risk_score, or plan to pass --marker_col.scripts/main.R with the requested time points.If you omit --data_file, the script exits with SKILL_MISSING_INPUT.
Expected output structure:
<output_dir>/
├── data/
│ └── time_roc_points.<csv|txt>
├── session_info.txt
├── figure/
│ └── time_roc.pdf
└── table/
└── time_roc_auc.<csv|txt>
Primary output files:
data/time_roc_points.csv or data/time_roc_points.txttable/time_roc_auc.csv or table/time_roc_auc.txtfigure/time_roc.pdfsession_info.txtExported result fields include:
timetime_unitaucmarker_coln_completen_eventsExported ROC point fields include:
false_positive_ratesensitivitytimetime_unitauccurve_label| Need | File |
|---|---|
| Statistical details and assumptions | references/algorithm.md |
| More CLI examples | references/cli-guide.md |
| Error diagnosis | references/troubleshooting.md |
| Main execution entry point | scripts/main.R |
| Sample test data | tests/data/ |
Basic analysis:
Rscript scripts/main.R \
--data_file tests/data/time_roc_sample1.txt \
--times 1,3,5 \
--output_dir tests/output_basic
Specify marker column and plot styling:
Rscript scripts/main.R \
--data_file tests/data/time_roc_sample2.txt \
--marker_col GPR161 \
--times 1,2,4 \
--line_colors "#4DBBD5,#E64B35,#00A087" \
--legend_position bottom \
--output_dir tests/output_gpr161
Rscript scripts/main.R --help
Rscript scripts/main.R \
--data_file tests/data/time_roc_sample1.txt \
--times 1,3,5 \
--output_dir tests/validation_output
After running analysis, verify that these files exist:
tests/validation_output/data/time_roc_points.csvtests/validation_output/table/time_roc_auc.csvtests/validation_output/figure/time_roc.pdftests/validation_output/session_info.txtSKILL_FILE_NOT_FOUND: Input file path is wrong or inaccessible.SKILL_MISSING_COLUMNS: futime or fustat is missing.SKILL_INVALID_DATA: The marker column is non-numeric, no complete rows remain, event coding is unsuitable, or requested time points yield unusable AUC values.SKILL_INVALID_PARAMETER: An argument value is invalid.SKILL_INSUFFICIENT_DATA: Too few complete rows or no target events remain.SKILL_DEPENDENCY_MISSING: A required R package such as optparse, timeROC, survival, ggplot2, or openxlsx is unavailable.If the issue is not obvious, read references/troubleshooting.md.
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Transform lengthy academic papers into concise, structured 250-word abstracts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Refines long medical academic texts into SCI-style unstructured Chinese and English abstracts; use when you need to condense drafts/reports/summaries into bilingual abstracts and generate Summary_Report.md.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates submission-ready Elsevier/SCI Highlights from manuscript text or extracted PDF/DOCX/TXT content. Use when a user needs 3-5 concise, evidence-grounded highlight bullets for a research paper, review, meta-analysis, case report, or bioinformatics manuscript.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze data with `adme-property-predictor` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.