Transcriptomic Endotypes of Sepsis Identified by Consensus Clustering of Whole-Blood Gene Expression

# Sepsis Immune Endotypes Transcriptomic Dataset ## Overview This dataset contains processed data files from the manuscript **"Transcriptomic Endotypes of Sepsis Identified by Consensus Clustering of Whole-Blood Gene Expression"**. Using consensus clustering on 5 publicly available whole-blood transcriptomic datasets (1,002 sepsis patients), we identified 3 sepsis immune endotypes:- **C1 Immune Activation** (306 patients, 30.5%): mortality 26.1%- **C2 Interferon Response** (447 patients, 44.6%): mortality 15.4%- **C3 Erythroid Dysregulation** (249 patients, 24.9%): mortality 25.3% ## Directory Structure ```sepsis_immune_zenodo/├── discovery/ # Discovery cohorts (5 datasets)├── validation/ # Validation cohorts (4 datasets)├── clustering/ # Clustering results├── differential_expression/ # Differential expression & enrichment├── phenotype/ # Clinical phenotype data├── README.md├── CITATION.cff└── LICENSE``` ## Data Dictionary All files are in R RDS format. Load with `readRDS()`. ### discovery/ — Discovery Cohorts Raw and processed gene expression matrices (rows = genes, columns = samples). Processed matrices contain the 9-platform universal gene set (2,456 genes). | File | Dimensions | Platform | Size ||------|-----------|----------|------|| GSE65682_raw.rds | 11,518 x 479 | GPL13667 (Affymetrix HG-U219) | 41.6 MB || GSE65682_processed.rds | 2,456 x 479 | — | 7.4 MB || GSE185263_raw.rds | 35,302 x 345 | GPL16791 (Illumina HiSeq 2500) | 44.9 MB || GSE185263_processed.rds | 2,456 x 345 | — | 5.5 MB || GSE63042_raw.rds | 14,077 x 106 | GPL9115 (Illumina HumanHT-12 V3) | 3.5 MB || GSE63042_processed.rds | 2,456 x 106 | — | 852 KB || GSE95233_raw.rds | 22,880 x 51 | GPL570 (Affymetrix HG-U133 Plus 2) | 8.8 MB || GSE95233_processed.rds | 2,456 x 51 | — | 817 KB || GSE137340_raw.rds | 30,308 x 21 | GPL10558 (Illumina HumanHT-12 V4) | 4.8 MB || GSE137340_processed.rds | 2,456 x 21 | — | 354 KB | ### validation/ — Validation Cohorts Full expression matrices (rows = genes, columns = samples) for LASSO classifier external validation. | File | Dimensions | Size ||------|-----------|------|| GSE54514_validation_expr.rds | 16,101 x 35 | 4.1 MB || GSE26378_validation_expr.rds | 22,880 x 103 | 7.4 MB || GSE26440_validation_expr.rds | 22,880 x 130 | 9.7 MB || GSE32707_validation_expr.rds | 31,326 x 144 | 15.2 MB | ### clustering/ — Clustering Results | File | Type | Dimensions | Description ||------|------|-----------|-------------|| combined_expression_batch_corrected.rds | matrix | 2,456 x 1,002 | ComBat batch-corrected combined expression matrix || expression_selected_genes.rds | matrix | 122 x 1,002 | Feature gene expression matrix after MAD filtering || clustering_results.rds | list (7) | — | Full ConsensusClusterPlus output || cluster_assignments_all_k.rds | list (9) | — | Cluster assignments for k = 2 through k = 10 || endotype_centroids.rds | matrix | 2,456 x 3 | Centroid expression profiles for 3 endotypes | ### differential_expression/ — Differential Expression | File | Type | Description ||------|------|-------------|| differential_expression_results.rds | list (3) | limma DE results for each endotype vs. rest || enrichment_results.rds | list (3) | GO/KEGG functional enrichment results | ### phenotype/ — Clinical Phenotype | File | Dimensions | Description ||------|-----------|-------------|| GSE65682_phenotype.rds | 802 x 13 | Clinical metadata for the GSE65682 cohort (includes survival data) | ## Usage Examples ```r# Load batch-corrected expression matrixexpr <- readRDS("clustering/combined_expression_batch_corrected.rds")dim(expr) # 2456 x 1002 # Load cluster assignments (k = 3)assignments <- readRDS("clustering/cluster_assignments_all_k.rds")clusters_k3 <- assignments[["3"]] # Load differential expression resultsde_results <- readRDS("differential_expression/differential_expression_results.rds")names(de_results) # DE results per endotype # Load validation dataval_expr <- readRDS("validation/GSE54514_validation_expr.rds")``` ## Data Processing Pipeline 1. Download raw data from GEO -> `discovery/*_raw.rds`2. Gene symbol mapping and 9-platform intersection (2,456 genes) -> `discovery/*_processed.rds`3. ComBat batch correction -> `clustering/combined_expression_batch_corrected.rds`4. MAD feature selection (MAD >= 1.0) -> `clustering/expression_selected_genes.rds` (122 genes)5. PAM + Pearson distance consensus clustering -> `clustering/clustering_results.rds`6. limma differential expression -> `differential_expression/` ## License This dataset is released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.