Integrating Transcriptomics, Mendelian Randomization, Machine Learning, and Experimental Validation to Identify Feature Genes in Sepsis with Thyroid Dysfunction_validation cohort
RNA-seq (gene expression) · human · 8 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
- 8 samples: 5 × SPTD vs 3 × SPGroups are read from the sample names, not from the paper. Check the list below.
- 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.
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 GSE341840_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 PRJNA1504319 and SRA study SRP722473. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 8 samples.
How we read this: 2 replicated groups read from 8 sample titles; they account for 8 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM9918100 SP6 SP
- GSM9918101 SP7 SP
- GSM9918102 SP8 SP
- GSM9918103 SPTD11 SPTD
- GSM9918104 SPTD12 SPTD
- GSM9918105 SPTD13 SPTD
- GSM9918106 SPTD14 SPTD
- GSM9918107 SPTD15 SPTD
- GSE298578 Single cell RNA profiling of blood CD4+ T cells identifies distinct helper and dysfunctional regulatory clusters in children with SLE (5' PBMC validation dataset) 14 samples shares dysfunction, validation
- GSE296585 RESTORATION OF NOX4 SIGNALLING REVERSES ENDOTHELIAL COLONY-FORMING CELL ANGIOGENIC DYSFUNCTION ASSOCIATED WITH EXPERIMENTAL AND CLINICAL DIABETES 11 samples shares experimental, dysfunction
- GSE336982 Obesity Promotes Lung Carcinogenesis Through Airway Immune Dysfunction 183 samples shares dysfunction
- GSE261070 Dysfunctional neutrophil Type 1 interferon responses in preschool children with recurrent wheezing and IL-4-mediated aeroallergen sensitization 134 samples shares dysfunction
- GSE261892 Australian Chronic Allograft Dysfunction III 123 samples shares dysfunction
- GSE281797 Transcriptomic profiles of liver biopsies in obese patients with metabolic dysfunction-associated steatotic liver disease 94 samples shares dysfunction
- GSE261258 Human regulatory memory B cells defined by expression of TIM-1 and TIGIT are dysfunctional in Multiple Sclerosis. 89 samples shares dysfunction
- GSE335070 Malignant epithelial states drive immune dysfunction in ampulla of Vater carcinoma 75 samples shares dysfunction
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.