Transform lengthy academic papers into concise, structured 250-word abstracts.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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.
中文简介正在准备中。当前显示原文说明。
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中文简介正在准备中。当前显示原文说明。
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中文简介正在准备中。当前显示原文说明。
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中文简介正在准备中。当前显示原文说明。
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中文简介正在准备中。当前显示原文说明。
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中文简介正在准备中。当前显示原文说明。