Risk assessment with gene expression markers in sepsis development
RNA-seq (gene expression) · human · 267 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 267 samples
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- Risk assessment with gene expression markers in sepsis developmentCell reports. Medicine · 2024
Read the full summary ↓
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE208581_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA860175 and SRA study SRP387133. Searching any of these in the dataset finder brings you back here.
The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.
+ 267 more — browse all 267 samples with per-sample file links →
- GSE272023 CpG island methylator phenotype classification improves risk assessment in pediatric T-cell Acute Lymphoblastic Leukemia [RNA-seq] 111 samples shares assessment, risk
- GSE296830 Investigation of transcriptomic signatures associated with sepsis risk in patients with infection 32 samples shares sepsis, risk
- GSE319878 Interrogating the mechanistic link between neighborhood deprivation and colorectal cancer risk through gene expression analysis of normal colorectal biopsies 171 samples shares risk
- GSE262419 Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes 4558 samples shares risk
- GSE284467 Multi-omic Sequencing of Intermediate to High-Risk Cutaneous Squamous Cell Carcinoma Identifies Critical Genes and Expression Patterns Associated with Disease and Poor Outcomes 75 samples shares risk
- GSE251958 Predicting patient outcome based on gene-expression biomarkers learned from colorectal cancer organoids and cell lines 21 samples shares markers
- GSE232027 Platelet Reactivity ExpreSsion Score Predicts Cardiovascular Risk 84 samples shares risk
- GSE311877 Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [RNAseq_mutants] 80 samples shares assessment
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.