A multi-omics integration approach relying on circulating factors does not discern subtypes in childhood type 1 diabetes.
RNA-seq (gene expression) · human · 103 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
- 103 samples: 103 × blood, patientGroups 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.
- Paper
- A multi-omics integration approach relying on circulating factors does not discern subtypes of childhood type 1 diabetesCommunications medicine · 2025
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 GSE287275_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 PRJNA1212008 and SRA study SRP558026. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 103 samples.
How we read this: 1 replicated groups read from 103 sample titles; they account for 103 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.
- GSM8742772 blood, patient_1 blood, patient
- GSM8742773 blood, patient_2 blood, patient
- GSM8742774 blood, patient_3 blood, patient
- GSM8742775 blood, patient_4 blood, patient
- GSM8742776 blood, patient_5 blood, patient
- GSM8742777 blood, patient_6 blood, patient
- GSM8742778 blood, patient_7 blood, patient
- GSM8742779 blood, patient_8 blood, patient
- GSM8742780 blood, patient_9 blood, patient
- GSM8742781 blood, patient_10 blood, patient
- GSM8742782 blood, patient_11 blood, patient
- GSM8742783 blood, patient_12 blood, patient
- GSM8742784 blood, patient_13 blood, patient
- GSM8742785 blood, patient_14 blood, patient
- GSM8742786 blood, patient_15 blood, patient
- GSM8742787 blood, patient_16 blood, patient
- GSM8742788 blood, patient_17 blood, patient
- GSM8742789 blood, patient_18 blood, patient
- GSM8742790 blood, patient_19 blood, patient
- GSM8742791 blood, patient_20 blood, patient
- GSM8742792 blood, patient_21 blood, patient
- GSM8742793 blood, patient_22 blood, patient
- GSM8742794 blood, patient_23 blood, patient
- GSM8742795 blood, patient_24 blood, patient
- GSM8742796 blood, patient_25 blood, patient
- GSM8742797 blood, patient_26 blood, patient
- GSM8742798 blood, patient_27 blood, patient
- GSM8742799 blood, patient_28 blood, patient
- GSM8742800 blood, patient_29 blood, patient
- GSM8742801 blood, patient_30 blood, patient
- GSM8742802 blood, patient_31 blood, patient
- GSM8742803 blood, patient_33 blood, patient
- GSM8742804 blood, patient_34 blood, patient
- GSM8742805 blood, patient_35 blood, patient
- GSM8742806 blood, patient_36 blood, patient
- GSM8742807 blood, patient_37 blood, patient
- GSM8742808 blood, patient_39 blood, patient
- GSM8742809 blood, patient_40 blood, patient
- GSM8742810 blood, patient_41 blood, patient
- GSM8742811 blood, patient_42 blood, patient
+ 63 more — browse all 103 samples with per-sample file links →
- GSE273036 Multi-Omics Analysis and Molecular Subtypes Provide Potential Therapeutic Implications in Fumarate Hydratase-deficient Renal Cell Carcinoma 93 samples shares multi-omics, subtypes
- GSE316337 Multi-omics profiling reveals microbiota, metabolite, lipid, and immunological heterogeneity underlying distinct pathophysiological mechanisms of age-related endotypes in type 1 diabetes 54 samples shares multi-omics, diabetes
- GSE274934 Single-cell multi-omics reveals tumor microenvironment factors underlying poor immunotherapy responses in ALK-positive lung cancer 23 samples shares multi-omics, factors
- GSE271689 Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer 586 samples shares multi-omics
- GSE253910 PriOmics: integration of high_throughput proteomic data with complementary omics layers using mixed graphical modeling with group priors 360 samples shares integration
- GSE285943 A reference model for circulating hematopoietic stem cells with applications to diagnostics 299 samples shares circulating
- GSE263083 Multi-omics data analysis for the JGOG3025-TR2 ovarian cancer cohort [RNA-seq] 291 samples shares multi-omics
- GSE325054 Time-Resolved Multi-Omics Identify Biomarkers of Immediate Reactions to mRNA Vaccination [RNA-Seq] 187 samples shares multi-omics
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.