Transform lengthy academic papers into concise, structured 250-word abstracts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when screening differentially expressed genes from a bulk expression matrix between two user-specified groups, producing DEG tables, a volcano plot, and a clustered heatmap. Triggers include DEG analysis, volcano plot, clustered heatmap, limma-based two-group comparison, and case-vs-control screening. NOT for single-cell RNA-seq, multi-group contrasts, count-model workflows such as DESeq2/edgeR, or non-expression omics data.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this skill when you need a reproducible two-group DEG workflow on a bulk expression matrix and want:
Typical requests include:
Do not use this skill for:
DESeq2 or edgeRIf the request falls outside this scope, stop and hand off to a more appropriate analysis workflow instead of forcing the data through this skill.
Diffanalysis.csv currently exports name, logFC, P.value, and P.adj.--p_type controls both DEG screening semantics and volcano plot significance semantics.plot/heatmap.pdf is generated only when at least two heatmap genes remain after ranking.| Situation | File to Read | Purpose |
|---|---|---|
| Need algorithm details or statistical assumptions | references/algorithm.md | limma method, filtering logic, volcano/heatmap selection rules |
| Need to execute the workflow | scripts/main.R | Get the exact CLI entry and runnable command |
| Encounter an error code or bad input format | references/troubleshooting.md | Match SKILL_* errors to causes and fixes |
| Need more CLI examples | references/cli-guide.md | See complete command examples for common use cases |
| Need a minimal runnable example | tests/data/ | Use bundled test input files for validation |
Rscript scripts/main.R \
--input_file tests/data/oa_exp.csv \
--group_file tests/data/oa_group.csv \
--case OA \
--control control \
--output_dir ./results
| Short | Long | Type | Default | Required | Description |
|---|---|---|---|---|---|
-i | --input_file | character | none | yes | Expression matrix CSV. First column is gene ID, remaining columns are sample values. |
-g | --group_file | character | none | yes | Group annotation CSV. The script auto-detects sample and group columns, including files where the first column is row names or index. |
-o | --output_dir | character | ./DEG | no | Output directory for tables, plots, and session metadata. |
| --case | character | none | yes | Case group name to compare. Matching is case-insensitive and trimmed. |
| --control | character | none | yes | Control group name to compare. Matching is case-insensitive and trimmed. |
-m | --diff_method | character | limma | no | Differential expression method. Current implementation supports limma only. |
-p | --p_threshold | numeric | 0.05 | no | Significance threshold for DEG screening. |
-f | --logfc_threshold | numeric | 1 | no | Absolute log fold change threshold for DEG screening. |
| --top_n | integer | 5 | no | Number of top upregulated and top downregulated genes considered for heatmap selection. |
| --p_type | character | p.adj | no | P-value field used for significance filtering and volcano significance coloring. Allowed values: p, p.adj. |
| --run_plots | logical | TRUE | no | Whether to generate the volcano plot and clustered heatmap. |
| --timeout_seconds | integer | 3600 | no | Maximum allowed runtime before timeout. |
-s | --seed | integer | 42 | no | Random seed recorded for reproducibility. |
| File | Format | Description |
|---|---|---|
session_info.txt | txt | R session metadata and package versions used in the run. |
data/DEG_list.rda | rda | Serialized R object containing method, groups, thresholds, the full differential table, and the screened DEG table. |
table/Diffanalysis.csv | csv | Full differential expression result table with columns name, logFC, P.value, and P.adj. |
table/DEG.csv | csv | Significant DEG table only, containing screened genes with group labels up or down. |
plot/volcano_plot.pdf | Volcano plot of differential genes using the p-value mode selected by --p_type. | |
plot/heatmap.pdf | Clustered heatmap for selected top differential genes when at least two heatmap genes are available and plotting is enabled. |
case - controlp_threshold and logfc_thresholdP.value or P.adj based on --p_typeup, down, or noplot/volcano_plot.pdf directly from the full differential tableplot/heatmap.pdf only when at least two heatmap genes are available| Error Code | Meaning | Typical Fix |
|---|---|---|
SKILL_FILE_NOT_FOUND | Input file path does not exist | Verify the file path and rerun |
SKILL_PACKAGE_NOT_FOUND | Required R package is missing | Install the missing package, then rerun |
SKILL_MISSING_COLUMNS | Input file does not contain the necessary columns | Check CSV structure and column placement |
SKILL_EMPTY_DATA | Input file is empty or limma returns no analyzable rows | Validate input content or confirm the matrix contains enough valid values |
SKILL_INVALID_PARAMETER | Argument value or group selection is invalid | Check thresholds, --case, --control, and --p_type |
SKILL_SAMPLE_MISMATCH | Expression matrix samples and group file samples do not match | Align sample IDs between the two input files |
SKILL_TIMEOUT | The run exceeded the allowed runtime | Increase --timeout_seconds or simplify the run |
If you need step-by-step fixes, read references/troubleshooting.md.
Rscript tests/run_tests.R
Minimal CLI smoke test:
Rscript scripts/main.R \
--input_file tests/data/oa_exp.csv \
--group_file tests/data/oa_group.csv \
--case OA \
--control control \
--output_dir ./tests_output
Expected outputs:
tests_output/table/Diffanalysis.csvtests_output/table/DEG.csvtests_output/plot/volcano_plot.pdftests_output/session_info.txttests_output/plot/heatmap.pdf is expected only when enough significant genes remain for heatmap rendering.
Runs with fewer than two selected heatmap genes skip heatmap generation with a warning instead of failing.
tests_output/table/DEG.csv may be empty when no genes pass the current thresholds.
Skill name: deg-screening-analysis
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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.
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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.
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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.