Transform lengthy academic papers into concise, structured 250-word abstracts.
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Use when analyzing bulk RNA-seq or microarray expression data to identify differentially expressed genes between two biological groups (case vs control), with volcano plots and heatmap visualization. NOT for:single-cell RNA-seq, methylation analysis, non-expression data.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
| Situation | File to Read | Purpose |
|---|---|---|
| Need algorithm details | references/algorithm.md | Statistical methods, formulas, assumptions |
| Need to run analysis | scripts/main.R | Execute: Rscript scripts/main.R --input_file ... --group_file ... |
| Encounter errors | references/troubleshooting.md | Common errors and solutions |
| Need CLI examples | references/cli-guide.md | Detailed CLI usage examples |
| Need test data | tests/data/ | Sample input files for testing |
Rscript scripts/main.R \
--input_file ./expression_matrix.csv \
--group_file ./group_info.csv \
--output_dir ./output/ \
--diff_method limma \
--p_threshold 0.05 \
--logfc_threshold 0.1 \
--seed 42
| Short | Long | Type | Default | Description |
|---|---|---|---|---|
-i | --input_file | character | required | Expression matrix file (genes as rows, samples as columns) |
-g | --group_file | character | required | Group information file (sample ID + group columns) |
-o | --output_dir | character | ./output/ | Output directory |
-m | --diff_method | character | limma | Method: limma, deseq2, edger, t, wilcox |
-n | --norm_method | character | TMM | Normalization for edgeR: TMM, RLE, upperquartile |
-p | --p_threshold | numeric | 0.05 | P-value threshold |
-f | --logfc_threshold | numeric | 0.1 | Log fold change threshold |
-s | --seed | integer | 42 | Random seed for reproducibility |
Genes as rows, samples as columns, CSV format with gene ID in first column.
"","GSM1442228","GSM1442229","GSM1442230"
"0610006L08Rik",3.438,3.237,3.265
"0610007P14Rik",6.734,7.017,6.807
CSV with sample ID and group columns.
"ID","group"
"GSM1442228","Control"
"GSM1442229","Control"
"GSM1442230","DIC"
| File | Description |
|---|---|
Diffanalysis.csv | Complete DE results with gene_id, logFC, Pvalue, Padj |
volcano_plot.pdf | Volcano plot with significance thresholds |
heatmap.pdf | Heatmap of top upregulated/downregulated genes |
session_info.txt | R session and package version info |
temp/rdegs.csv | Significant differentially expressed genes |
temp/Diffanalysis_filtered.csv | Full results with group annotations |
Linear models for microarray and RNA-seq with empirical Bayes moderation. Recommended for normalized expression data (FPKM, TPM).
Negative binomial GLM with variance stabilization. Recommended for raw count data.
Empirical Bayes methods with TMM normalization. Supports robust dispersion estimation.
Simple pairwise statistical tests. t-test for parametric, Wilcoxon for non-parametric.
Rscript scripts/main.R \
-i expression_matrix.csv \
-g group_info.csv \
-o ./output \
-m limma
Rscript scripts/main.R \
-i count_matrix.csv \
-g group_info.csv \
-o ./output \
-m deseq2
Rscript scripts/main.R \
-i expression_matrix.csv \
-g group_info.csv \
-o ./output \
-p 0.01 \
-f 0.5
| Error | Cause | Solution |
|---|---|---|
SKILL_FILE_NOT_FOUND | Input file doesn't exist | Check file path |
SKILL_SAMPLE_MISMATCH | Sample names don't match | Verify group file matches expression matrix columns |
SKILL_INVALID_DATA | Less than 2 groups or samples per group | Check group file |
SKILL_FILTER_ERROR | No significant genes found | Relax thresholds or check data quality |
SKILL_DEPENDENCY_MISSING | R package not installed | Install required packages |
IF error persists, READ: references/troubleshooting.md
# Check help
Rscript scripts/main.R --help
# Run with sample data
Rscript scripts/main.R \
-i tests/data/Combined_Datasets_Matrix_mus.csv \
-g tests/data/Combined_Datasets_mus_Group.csv \
-o tests/output/
# Count lines in output
wc -l output/Diffanalysis.csv
# Check volcano plot exists
ls -la output/volcano_plot.pdf
optparseset.seed() for reproducibilityrequireNamespace() dependency checksscripts/ directoryreferences/ directoryLast updated: 2026-04-01 | Version: 2.0.0
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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.
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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.