Transform lengthy academic papers into concise, structured 250-word abstracts.
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Use when estimating relative immune cell infiltration from a bulk expression matrix with a CIBERSORT-style nu-SVR deconvolution workflow based on an LM22 signature matrix, comparing one case group against one control group, and generating structured tables plus immune-fraction plots. NOT for single-cell RNA-seq, spatial data, clinical diagnosis, or workflows that require the original hosted CIBERSORT web service.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
scripts/main.R with the case and control groups.run_record.txt and output_manifest.txt after each run, including failed validation attempts.| Situation | File to Read | Purpose |
|---|---|---|
| Need to run the analysis | scripts/main.R | CLI entry point |
| Need algorithm details | references/algorithm.md | HQ reference workflow and result interpretation |
| Encounter an error | references/troubleshooting.md | Error codes and environment fixes |
| Need CLI examples or the baseline record | references/cli-guide.md | Example commands and validation notes |
| Need packaged test inputs | tests/data/ | Demo expression matrix, group file, and LM22 file |
Rscript scripts/main.R \
--input_file ./expression_matrix.csv \
--group_file ./group_info.csv \
--signature_file ./LM22.txt \
--case_group treatment \
--control_group control \
--output_dir ./output \
--qn false \
--seed 42
| Short | Long | Type | Default | Description |
|---|---|---|---|---|
-i | --input_file | file | required | Expression matrix with genes as rows and samples as columns |
-g | --group_file | file | required | Group annotation table |
-a | --case_group | string | required | Case group label |
-b | --control_group | string | required | Control group label |
-o | --output_dir | dir | ./output | Output directory |
--signature_file | file | tests/data/LM22.txt when present | Signature matrix file | |
--sample_col | string/int | none | Optional sample column name or 1-based index | |
--group_col | string/int | none | Optional group column name or 1-based index | |
--gene_id_case | string | upper | Gene ID normalization: asis, upper, or lower | |
--auto_unlog | boolean | true | Apply 2^x only if the expression matrix passes a conservative log-scale heuristic | |
--min_mean_expression | numeric | 1 | Minimum mean expression before deconvolution | |
--perm | integer | 1000 | Permutation count for empirical p-value estimation; 0 keeps the run lightweight but records P-value as NA | |
--qn | boolean | true | Apply quantile normalization to the mixture matrix | |
--svm_cores | integer | 1 | Worker count for the nu-SVR model selection step | |
--make_plots | boolean | true | Generate PDF plots | |
--plot_width | numeric | 16 | Default plot width in inches | |
--plot_height | numeric | 10 | Default plot height in inches | |
-s | --seed | integer | 42 | Random seed |
-t | --timeout_seconds | integer | 0 | Optional timeout in seconds; 0 disables it |
--verbose | boolean | true | Print progress logs |
CSV or TSV. The first column must contain gene identifiers. Remaining columns must be numeric sample-level expression values.
When --auto_unlog=true, the workflow reports summary statistics and applies 2^x only if the matrix passes a conservative log-scale heuristic. If the matrix is ambiguous, the values are left unchanged and the startup log explains why.
If duplicate gene identifiers are present, they are consolidated after gene-ID normalization by taking the per-sample maximum before downstream filtering and deconvolution.
gene,Sample1,Sample2,Sample3
TP53,10.2,8.5,9.1
CXCL9,4.3,6.1,5.7
CSV or TSV with one sample column and one group column.
sample,group
Sample1,control
Sample2,treatment
Sample3,treatment
The packaged default is tests/data/LM22.txt. A custom signature matrix must contain one gene column followed by immune-cell signature columns.
All immune-cell signature columns must be numeric and finite. If duplicate gene identifiers are present, they are consolidated by taking the per-cell-type maximum before gene intersection.
| File | Description |
|---|---|
data/cibersort_input.rds | Serialized aligned input matrices used by the local algorithm |
data/cibersort_null_distribution.rds | Serialized permutation null distribution |
data/cibersort_result.rds | Serialized result object with cell fractions, metrics, runtime settings, and heatmap rendering metadata |
table/CIBERSORT_Results.csv | Full result table in CSV format |
table/CIBERSORT-Results.txt | Full result table in tab-delimited text format |
table/cibersort_cell_fractions_wide.csv | Wide-format immune cell fraction table |
table/cibersort_cell_fractions_long.csv | Long-format immune cell fraction table |
table/cibersort_group_compare.csv | Case-vs-control comparison summary |
table/cibersort_quality_metrics.csv | Sample-level P-value, Correlation, and RMSE table |
table/immune_cell_correlation_matrix.csv | Spearman correlation matrix across immune cell types |
table/immune_cell_correlation_pvalue.csv | P-value matrix aligned to the correlation matrix |
plot/immune_cell_composition_sample.pdf | Sample-level stacked composition plot when --make_plots=true |
plot/immune_group_boxplot.pdf | Group comparison boxplot when --make_plots=true |
plot/immune_correlation_heatmap.pdf | Immune-cell correlation heatmap when --make_plots=true |
session_info.txt | R session information |
output_manifest.txt | Append-only output manifest for successful and failed runs |
run_record.txt | Append-only structured run record, including runtime notes and failed-run summaries |
When --make_plots=false, the plot/ directory may still exist as part of the standard output layout, but no PDF plot files are written.
When --perm=0, the workflow logs a warning and completes without empirical permutation testing, so the P-value column is recorded as NA.
When a rerun targets an existing --output_dir and then fails validation or execution, the previous successful payload is preserved and the failure is appended to run_record.txt and output_manifest.txt.
| Error Code | Meaning | Solution |
|---|---|---|
SKILL_FILE_NOT_FOUND | An input file or signature matrix was not found | Check the file path and rerun |
SKILL_MISSING_COLUMNS | A required column is missing | Fix the input schema |
SKILL_EMPTY_DATA | No usable genes, samples, or deconvolution outputs remain | Check the data, filtering, or signature overlap |
SKILL_INVALID_PARAMETER | A CLI parameter is missing or invalid | Review the argument table and input values |
SKILL_SAMPLE_MISMATCH | Expression samples and group annotations do not align | Harmonize sample identifiers |
SKILL_PACKAGE_NOT_FOUND | A required R package is missing | Install the missing package |
SKILL_TIMEOUT | The configured time limit was exceeded | Increase --timeout_seconds or set it to 0 |
If the error persists, READ: references/troubleshooting.md
This skill accepts:
Do not use this skill for:
If the user's request is outside this scope, do not proceed with the workflow. Instead respond:
"cibersort-immune-infiltration-analysis is designed for local CIBERSORT-style immune deconvolution from a bulk expression matrix with one case group and one control group. Your request appears to be outside this scope. Please provide compatible bulk-expression inputs and group labels, or use a more appropriate tool for your task."
Rscript scripts/main.R --help
Rscript tests/run_tests.R
Rscript tests/test_skill.R
Validated packaged test path:
Rscript scripts/main.R \
--input_file tests/data/expression_matrix.csv \
--group_file tests/data/group_info.csv \
--signature_file tests/data/LM22.txt \
--case_group Tumor \
--control_group Healthy \
--output_dir tests/output \
--perm 25 \
--qn false \
--svm_cores 1 \
--seed 42
Container note:
--qn false because preprocessCore::normalize.quantiles() may trigger environment-level thread failures in some containers.references/cli-guide.md.tests/run_tests.R also checks that a failed rerun does not erase an existing successful payload directory.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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.
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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.
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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.
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Analyze data with `adme-property-predictor` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
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