Integrating Transcriptomics, Mendelian Randomization, Machine Learning, and Experimental Validation to Identify Feature Genes in Sepsis with Thyroid Dysfunction_discovery cohort
RNA-seq (gene expression) · human · 13 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
- 13 samples: 8 × EG vs 5 × ctrlGroups 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 GSE341837_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 PRJNA1504318 and SRA study SRP722469. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 13 samples.
How we read this: 2 replicated groups read from 13 sample titles; they account for 13 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.
- GSM9918077 ctrl1 ctrl
- GSM9918078 ctrl2 ctrl
- GSM9918079 ctrl3 ctrl
- GSM9918080 ctrl4 ctrl
- GSM9918081 ctrl5 ctrl
- GSM9918082 EG2 EG
- GSM9918083 EG3 EG
- GSM9918084 EG5 EG
- GSM9918085 EG6 EG
- GSM9918086 EG7 EG
- GSM9918087 EG8 EG
- GSM9918088 EG9 EG
- GSM9918089 EG10 EG
- 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.