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roc-diagnostic-performance Use when evaluating diagnostic biomarker performance from case-control expression data with logistic regression and ROC curves, exporting coefficient and AUC tables together with a ROC PDF. NOT for: survival analysis, time-to-event outcomes, multiclass classification, calibration curves, decision-curve analysis, or nomogram construction.
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name roc-diagnostic-performance description Use when evaluating diagnostic biomarker performance from case-control expression data with logistic regression and ROC curves, exporting coefficient and AUC tables together with a ROC PDF. NOT for: survival analysis, time-to-event outcomes, multiclass classification, calibration curves, decision-curve analysis, or nomogram construction.
ROC Diagnostic Performance
When to Use
Use this skill when you need to:
evaluate one or more diagnostic marker genes in a case-control cohort;
build a multivariable logistic regression diagnostic model from marker expression values;
compare the ROC performance of the full model against individual markers.
Typical user requests:
"Use these genes to build a diagnostic ROC model for case vs control samples."
"Evaluate the AUC of FOXP3, CD45, and CD3E and plot all ROC curves together."
"Run logistic regression on biomarker expression and export ROC results."
When Not to Use
Do not use this skill for:
survival or prognostic analysis with time-to-event outcomes;
multiclass classification tasks;
calibration plots, nomograms, or decision-curve analysis;
non-expression diagnostic inputs such as imaging, clinical scores, or mutation-only tables.
When to Read External Files
Situation File to Read Purpose Need algorithm details references/algorithm.mdLogistic regression, ROC, AUC, and modeling assumptions Need to run analysis scripts/main.RExecute Rscript scripts/main.R --expression_file ... --group_file ...
Encounter errors references/troubleshooting.mdCommon SKILL_* errors and solutions
Need CLI examples references/cli-guide.mdDetailed command-line examples
Need test data tests/data/Example expression matrix and group file
Usage Rscript scripts/main.R \
--expression_file ./expression_matrix.csv \
--group_file ./group_info.csv \
--marker_genes FOXP3,CD45,CD3E \
--case_group Disease \
--output_dir ./output/ \
--seed 42
Arguments Short Long Type Default Description -e--expression_filecharacter required Expression matrix file in CSV/TSV format -g--group_filecharacter required Group file with sample IDs and labels -m--marker_genescharacter required Comma-separated marker genes -c--case_groupcharacter required Case group label in the group file --group_colcharacter NULLOptional group column name; auto-detected if omitted -o--output_dircharacter ./output/Output directory --overwriteflag FALSEAllow writing into a non-empty output directory -s--seedinteger 42Random seed for reproducibility -T--timeout_secondsinteger 0Elapsed time limit in seconds; 0 disables timeout --plot_widthdouble 6ROC plot width in inches --plot_heightdouble 6ROC plot height in inches --font_familycharacter sansPDF font family --line_colorscharacter #E64B35,#4DBBD5,#00A087,#3C5488,#F39B7FComma-separated ROC line colors --line_widthdouble 1.2ROC curve line width --show_diagonalcharacter trueShow diagonal reference line: true or false --diagonal_colorcharacter #7F7F7FDiagonal line color --diagonal_ltyinteger 2Diagonal line type --plot_titlecharacter ROC Diagnostic PerformanceROC plot title --x_labelcharacter 1 - SpecificityX-axis label --y_labelcharacter SensitivityY-axis label --base_cexdouble 0.9Base text-size multiplier --legend_positioncharacter bottomrightLegend position --legend_cexdouble 0.8Legend text size
Input Format
Expression Matrix (expression_file) CSV or TSV file with genes as rows and samples as columns. The first column must store unique gene identifiers.
gene,Sample1,Sample2,Sample3
FOXP3,8.4,7.1,3.8
CD45,2.1,1.9,5.4
CD3E,5.8,6.2,4.0
File extension must be .csv, .tsv, or .txt.
The first column must contain non-missing, unique gene identifiers.
Remaining columns must be sample IDs.
Selected marker genes must have numeric finite expression values across matched samples.
Group File (group_file) CSV or TSV file with sample IDs in the first column and at least one group-label column.
sample,group
Sample1,Disease
Sample2,Disease
Sample3,Control
File extension must be .csv, .tsv, or .txt.
The first column must contain non-missing, unique sample IDs.
At least one group column must be present.
The case_group value must appear in the selected group column.
At least 10 matched samples, 2 case samples, and 2 control samples are required.
Output Files File Description data/analysis_data.rdsMatched sample-level analysis dataset used for model fitting data/roc_model.rdsSaved logistic regression model bundle with data and selected genes table/model_coefficients.csvLogistic regression coefficients, z statistics, p-values, and odds ratios table/roc_auc_summary.csvAUC values for the full model and each marker plot/roc_curve.pdfROC curves for the full model and individual markers session_info.txtSession information and run parameters
model_coefficients.csv Column Description termModel term name estimateLogistic regression coefficient std_errorStandard error of the coefficient z_valueWald z statistic p_valueWald test p-value odds_ratioExponentiated coefficient odds_ratio_95_ciOdds ratio with 95% confidence interval
roc_auc_summary.csv Column Description modelFull model or marker name aucArea under the ROC curve
Workflow
Step 1: Validate Input
Check that the expression matrix and group file exist and have supported formats.
Validate unique gene identifiers and sample IDs.
Match samples shared by both files.
Step 2: Prepare Analysis Dataset
Keep only the requested marker genes that exist in the expression matrix.
Merge matched expression values with group labels.
Convert the selected case group to binary outcome labels.
Step 3: Fit Logistic Regression
Fit a multivariable logistic regression model using the selected markers.
Extract coefficient estimates, standard errors, p-values, and odds ratios.
Step 4: Compute ROC Performance
Generate the ROC curve of the full logistic model.
Generate ROC curves for each individual marker.
Calculate AUC values for the full model and each marker.
Step 5: Save Outputs
Save the matched analysis dataset and model bundle as .rds files.
Save coefficient and AUC summary tables as .csv files.
Save the combined ROC plot as a PDF.
Examples
Basic Usage Rscript scripts/main.R \
-e expression_matrix.csv \
-g group_info.csv \
-m FOXP3,CD45,CD3E \
-c Disease \
-o ./output/
With Explicit Group Column and Custom Plot Rscript scripts/main.R \
-e expression_matrix.csv \
-g group_info.csv \
-m FOXP3,CD45,CD3E \
-c Disease \
--group_col diagnosis \
--plot_width 8 \
--plot_height 6 \
--plot_title "Biomarker ROC Comparison" \
--legend_position topright \
-o ./output/
With Test Data Rscript scripts/main.R \
-e tests/data/sample_expression_matrix.csv \
-g tests/data/sample_group_info.csv \
-m FOXP3,CD45,CD3E \
-c Disease \
-o tests/expected_output/ \
--overwrite
Error Handling Error Cause Solution SKILL_INVALID_PARAMETERMissing required argument, invalid option value, invalid matrix/group structure, invalid case label, insufficient case-control counts, or logistic fitting failure Check argument names, input content, class balance, and model stability SKILL_FILE_NOT_FOUNDInput file does not exist Verify the file path SKILL_EMPTY_DATAInput file contains no usable rows, or no requested markers remain after filtering Check file content, delimiter, and marker names SKILL_MISSING_COLUMNSRequested group column is absent Verify --group_col and the group file header SKILL_SAMPLE_MISMATCHExpression matrix and group file do not share sample IDs Verify that sample IDs match exactly between files SKILL_PACKAGE_NOT_FOUNDRequired R package is not installed Install the missing CRAN package
IF error persists , READ: references/troubleshooting.md
Testing
Smoke Test With Included Data Rscript scripts/main.R --help
Rscript scripts/main.R \
-e tests/data/sample_expression_matrix.csv \
-g tests/data/sample_group_info.csv \
-m FOXP3,CD45,CD3E \
-c Disease \
-o tests/expected_output/ \
--overwrite
Automated Smoke Test Script Rscript tests/run_smoke_test.R
bash tests/run_smoke_test.sh
Expected Output tests/expected_output/
|-- data/analysis_data.rds
|-- data/roc_model.rds
|-- plot/roc_curve.pdf
|-- session_info.txt
|-- table/model_coefficients.csv
`-- table/roc_auc_summary.csv
References
Hosmer DW, Lemeshow S, Sturdivant RX (2013). Applied Logistic Regression .
Fawcett T (2006). An Introduction to ROC Analysis. Pattern Recognition Letters .
Robin X et al. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics .
For detailed algorithm , READ: references/algorithm.md
Implementation Checklist
Last updated: 2026-04-17 | Version: 2.1.0
