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数据与复现
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bio-proteomics-differential-abundance
维护者 FreedomIntelligence · 最近更新 2026年4月1日
bio-proteomics-differential-abundance：统计检验 ，用于 differentially abundant proteins between conditions。 Covers limma 、 MSstats workflows ，支持 multiple testing correction。 适合在identifying proteins ，支持 significant abundance changes between experimental groups时使用。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-proteomics-differential-abundance
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- R：MSstats：：groupComparison() ，用于 feature-level mixed models。
- R：limma：：eBayes() ，用于 empirical Bayes moderated t-tests on protein-level data。
- Python：scipy.stats.ttest_ind() ，支持 statsmodels FDR correction。
- Find differentially abundant proteins between my conditions" → Perform 统计检验 on quantified protein intensities to identify proteins ，支持 significant abundance changes between experimental groups. R：MSstats：：groupComparison() ，用于 feature-level mixed models R：limma：：eBayes() ，用于 empirical Bayes moderated t-tests on protein-level data Python：scipy.stats.ttest_ind() ，支持 statsmodels FDR correction。
- comparison_matrix <- matrix(c(1，-1，0，0，1，0，-1，0，0，1，-1，0)，nrow = 3，byrow = TRUE) rownames(comparison_matrix) <- c('Treatment1-Control'，'Treatment2-Control'，'Treatment1-Treatment2') colnames(comparison_matrix) <- c('Control'，'Treatment1'，'Treatment2'，'Treatment3')。
原始文档
SKILL.md 摘录
MSstats Group Comparison (R stats (base)+)
Goal: Identify differentially abundant proteins between experimental conditions using feature-level mixed models or moderated t-tests.
Approach: Define contrast matrices for pairwise comparisons, run MSstats groupComparison (or limma eBayes for protein-level data), then filter results by adjusted p-value and log2 fold change thresholds.
library(MSstats)
## Significant proteins
sig_proteins <- results$ComparisonResult[results$ComparisonResult$adj.pvalue < 0.05 &
abs(results$ComparisonResult$log2FC) > 1, ]
Log2 intensities matrix (proteins x samples)
design <- model.matrix(~ 0 + condition, data = sample_info) colnames(design) <- levels(sample_info$condition)
fit <- lmFit(protein_matrix, design)
contrast_matrix <- makeContrasts(Treatment - Control, levels = design) fit2 <- contrasts.fit(fit, contrast_matrix) fit2 <- eBayes(fit2)
results <- topTable(fit2, number = Inf, adjust.method = 'BH') sig_results <- results[results$adj.P.Val < 0.05 & abs(results$logFC) > 1, ]
适用场景
- 适合在identifying proteins ，支持 significant abundance changes between experimental groups时使用。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
上游相关技能
- quantification - Prepare normalized data for testing
- differential-expression/deseq2-basics - Similar concepts for RNA-seq
- data-visualization/specialized-omics-plots - Volcano plots, MA plots
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